**Brain Imaging and the Prediction of Treatment Outcomes in Mood and Anxiety Disorders**

Leah M. Jappe, Bonnie Klimes-Dougan and Kathryn R. Cullen

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55446

### **1. Introduction**

[16] Fehr T, Achtziger A, Hinrichs H, Hermann M. Interindividual Differences in Oscilla‐ tory Brain Activity in Higher Cognitive Functions- Methodological Approached in Analyzing Continuous MEG Data. In: Reinvang, I., Greenlee, M.W. and Hermann, M. (Eds.) The Cognitive Neuroscience of Individual Differences. Oldenburg: bis-pub‐

[17] De Pasquale F, Penna SD, Snyder AZ, Lewis C, Mantini D, Marzetti L, Belardinelli P, Ciancetta L, Pizzella V, Romani GL, Corbetta M. Temporal Dynamics of Spontaneous MEG Activity in Brain Networks. Proceedings of the National Academy of Sciences

[18] He BJ, Zempel JM, Snyder AZ, and Raichle ME. The Temporal Structures and Func‐

[19] Buckner RL, Andrews-Hanna JR, Schacter DL. The Brain's Default Network: Anato‐ my, Function, and Relevance to Disease. Annals of The New York Academy of Sci‐

[20] Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, and Buckner RL. Functional-Anatomic Fractionation of the Brain's Default Network. Neuron 2010; 65, 550-562. [21] Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN. Wander‐ ing Minds: The Default Network and Stimulus-Independent Thought. Science 2007;

tional Significance of Scale-free Brain Activity. Neuron 2010; 66, 353-369.

of the United States of America 2010; 107, 6040-6045.

278 Functional Brain Mapping and the Endeavor to Understand the Working Brain

lishers; 2003..

ence 2008; 1124,1-38.

315, 393-395.

#### **1.1. Neuroimaging for treatment prediction: An advance in personalized medicine**

In addition to elucidating the mechanisms of disease, neuroimaging holds another great promise for the mental health field: the ability to predict treatment outcomes. Evidence-based treatments are available for many mental health disorders. However, not all individuals benefit from every treatment. Psychiatric research has begun to focus on the neurobiological factors that predict who will benefit from an intervention by experiencing symptom improvement. This application of neuroimaging is still very much in development, but it has the potential to facilitate a major advance in psychiatry, namely that of personalized care. Personalization of treatment for mental health disorders has been identified as a public health priority [1]. The idea is to select the best therapy for a patient at the beginning of treatment based on a set of patient characteristics that have been shown to be associated with positive outcomes with a given intervention. Those who are well matched for a particular treatment are more likely to stay engaged in the treatment, which will lead to better outcomes [2]. Given the scarcity and expense of available mental health resources, treatment should be conserved so that sufficient resources are available for those who would benefit from a specific type of treatment [3]. Optimally, these efforts will serve to guide treatment development and planning, improve overall response rates, decrease treatment costs, and eventually improve the prognosis of those who suffer from mental illness. In this chapter we review recent advances in application of neuroimaging tools to predict treatment response in patients with internalizing psychological disorders. Following the core themes of *Brain Mapping,* this chapter focuses on describing the brain structures and functions that have been associated with clinically significant response to

© 2013 Jappe et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

psychological and pharmacological treatments in internalizing disorders in addition to the underling research methodology used to investigate such relationships.

interpersonal therapy (IPT). For patients that do not respond to either or a combination of these treatments, additional options are considered including electroconvulsive therapy (ECT) and

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281

Similarly for anxiety disorders, antidepressant medications and behavioral therapies, includ‐ ing CBT, are frequently the treatments of choice. While CBT in MDD primarily aims to change behaviorbyalteringdistortedcognitions,formsofCBTinthecontextofanxietydisordersemploy the use of exposure techniques, where individuals face feared stimuli until their fear response naturally declines. Anxiolytics (e.g., benzodiazepines) are also used to mitigate acute symp‐ toms of anxiety and are employed for short-term treatment of anxiety in more extreme cases [13]. Unfortunately, even when treatments are delivered under ideal circumstances, 30-60% patients with depressive and anxiety disorders who are treated are not likely to achieve remission with their first treatment [14-17]. Therefore, there is a great need for the identification of biological markers that predict which interventions would work and for whom, thus helping guide clinicians in selecting a treatment with the greatest potential to provide effective

**4. Brain mapping methodologies employed to assess structural and**

individuals diagnosed with Major Depressive Disorder and Anxiety Disorders.

Major Depressive Disorder is a prevalent and debilitating disorder that is a leading cause of disability worldwide [18]. MDD often starts in adolescence and places youth at risk for

Several different types of neuroimaging techniques have been developed and increasingly employedinthecontextofpsychiatricresearch.Researchstudiesthathaveinvestigatedneurobio‐ logical predictors of treatment response have relied on the use of structural and functional brain imaging technologies. In structural magnetic resonance imaging (MRI), a non-invasive imag‐ ingtechnique,bothwholebrainandindividualstructurevolumesareexamined.Researchersuse this methodology to examine anatomical detail, localize individual brain regions and to identi‐ fy brain pathology. Functional MRI (fMRI) methodology provides useful temporal information about brain function by measuring the blood-oxygen-level-dependent (BOLD) contrast, where changes in energy between oxygenated and deoxygenated blood within the brain across time are examined to assess neural functioning within specific task constraints. Additional functional imaging methods employed in the context of treatment prediction research include positron emissiontomography(PET)andsingle-photonemissioncomputedtomography(SPECT).These procedures are considered invasive procedures in that they use radioactive substances in order to generate contrasts that assess brain blood flow, blood perfusion, and glucose metabolism as an indirect measure of neural activity. This wide array of brain imaging techniques has been used to assess which brain structures and functions prior to treatment predict treatment response in

**functional predictors of treatment response**

transcranial magnetic stimulation (TMS).

symptom management.

**5. Major depressive disorder**

### **2. Internalizing disorders: The focus on mood and anxiety disorders**

It is critically important to direct attention towards the study of internalizing problems. Internalizing disorders are associated with significant impairment and distress and they often lead to the development and reoccurrence of debilitating psychiatric illness [4,5]. Based on empirically derived classification models, internalizing disorders are characterized by maladjustment primarily expressed inwardly, as compared to externalizing patterns of behavior where maladjustment is expressed outwardly [6,7]. Although internalizing behavior is increasingly conceptualized as a dimensional construct, treatment research has typically focused on extreme conditions, tending to examine questions regarding internalizing behavior through the lens of discrete psychiatric disorders. Some internalizing disorders, such as Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD), involve negative affect characterized by anxious misery and distress. Other internalizing disorders, including Social Phobia, Specific Phobia, Agoraphobia, and Panic Disorder, involve negative affect associated with activation of the fear system. Obsessive Compulsive Disorder (OCD) has also been characterized as an internalizing disorder [8]. Grouping mental illnesses more broadly along an internalizing dimension is advantageous in a number of ways. Namely, this approach accounts for the high rates of comorbidity between internalizing disorders and it groups problems that share commonalities in pathophysiology and genetic variance [7]. For example, internalizing problems are centrally implicated in the threat response system and involve abnormalities in fronto-limbic brain circuitry. This chapter focuses on the most commonly exhibited internalized disorders, namely MDD and Anxiety Disorders [9].

### **3. Available treatments for major depressive disorder and anxiety disorders**

The past two decades have shown significant advances in the development and refinement of treatments available to those who suffer from internalizing problems. Validated, evidencebased treatments (EBTs) are now available for treating the classes of internalizing problems discussed here, including specific mood and anxiety disorders. The commonalities in the EBTs for these classes of problems are considerable. Validated treatments include medication and/or psychotherapy [10-12].

In MDD, first-line treatments that are currently offered include antidepressant medications and psychotherapy. Regarding antidepressants, the first options are typically those that impact the monoamine neurotransmitters, such as the selective serotonin reuptake inhibitors (SSRIs). Second-line medication treatments impact other neurotransmitters such as dopamine or norepinephrine, and some impact serotonin by alternate mechanisms. Regarding psycho‐ therapies, empirically validated interventions include cognitive behavioral therapy (CBT) and interpersonal therapy (IPT). For patients that do not respond to either or a combination of these treatments, additional options are considered including electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS).

Similarly for anxiety disorders, antidepressant medications and behavioral therapies, includ‐ ing CBT, are frequently the treatments of choice. While CBT in MDD primarily aims to change behaviorbyalteringdistortedcognitions,formsofCBTinthecontextofanxietydisordersemploy the use of exposure techniques, where individuals face feared stimuli until their fear response naturally declines. Anxiolytics (e.g., benzodiazepines) are also used to mitigate acute symp‐ toms of anxiety and are employed for short-term treatment of anxiety in more extreme cases [13].

Unfortunately, even when treatments are delivered under ideal circumstances, 30-60% patients with depressive and anxiety disorders who are treated are not likely to achieve remission with their first treatment [14-17]. Therefore, there is a great need for the identification of biological markers that predict which interventions would work and for whom, thus helping guide clinicians in selecting a treatment with the greatest potential to provide effective symptom management.

### **4. Brain mapping methodologies employed to assess structural and functional predictors of treatment response**

Several different types of neuroimaging techniques have been developed and increasingly employedinthecontextofpsychiatricresearch.Researchstudiesthathaveinvestigatedneurobio‐ logical predictors of treatment response have relied on the use of structural and functional brain imaging technologies. In structural magnetic resonance imaging (MRI), a non-invasive imag‐ ingtechnique,bothwholebrainandindividualstructurevolumesareexamined.Researchersuse this methodology to examine anatomical detail, localize individual brain regions and to identi‐ fy brain pathology. Functional MRI (fMRI) methodology provides useful temporal information about brain function by measuring the blood-oxygen-level-dependent (BOLD) contrast, where changes in energy between oxygenated and deoxygenated blood within the brain across time are examined to assess neural functioning within specific task constraints. Additional functional imaging methods employed in the context of treatment prediction research include positron emissiontomography(PET)andsingle-photonemissioncomputedtomography(SPECT).These procedures are considered invasive procedures in that they use radioactive substances in order to generate contrasts that assess brain blood flow, blood perfusion, and glucose metabolism as an indirect measure of neural activity. This wide array of brain imaging techniques has been used to assess which brain structures and functions prior to treatment predict treatment response in individuals diagnosed with Major Depressive Disorder and Anxiety Disorders.

### **5. Major depressive disorder**

psychological and pharmacological treatments in internalizing disorders in addition to the

It is critically important to direct attention towards the study of internalizing problems. Internalizing disorders are associated with significant impairment and distress and they often lead to the development and reoccurrence of debilitating psychiatric illness [4,5]. Based on empirically derived classification models, internalizing disorders are characterized by maladjustment primarily expressed inwardly, as compared to externalizing patterns of behavior where maladjustment is expressed outwardly [6,7]. Although internalizing behavior is increasingly conceptualized as a dimensional construct, treatment research has typically focused on extreme conditions, tending to examine questions regarding internalizing behavior through the lens of discrete psychiatric disorders. Some internalizing disorders, such as Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD), involve negative affect characterized by anxious misery and distress. Other internalizing disorders, including Social Phobia, Specific Phobia, Agoraphobia, and Panic Disorder, involve negative affect associated with activation of the fear system. Obsessive Compulsive Disorder (OCD) has also been characterized as an internalizing disorder [8]. Grouping mental illnesses more broadly along an internalizing dimension is advantageous in a number of ways. Namely, this approach accounts for the high rates of comorbidity between internalizing disorders and it groups problems that share commonalities in pathophysiology and genetic variance [7]. For example, internalizing problems are centrally implicated in the threat response system and involve abnormalities in fronto-limbic brain circuitry. This chapter focuses on the most commonly

**2. Internalizing disorders: The focus on mood and anxiety disorders**

underling research methodology used to investigate such relationships.

280 Functional Brain Mapping and the Endeavor to Understand the Working Brain

exhibited internalized disorders, namely MDD and Anxiety Disorders [9].

and/or psychotherapy [10-12].

**3. Available treatments for major depressive disorder and anxiety disorders**

The past two decades have shown significant advances in the development and refinement of treatments available to those who suffer from internalizing problems. Validated, evidencebased treatments (EBTs) are now available for treating the classes of internalizing problems discussed here, including specific mood and anxiety disorders. The commonalities in the EBTs for these classes of problems are considerable. Validated treatments include medication

In MDD, first-line treatments that are currently offered include antidepressant medications and psychotherapy. Regarding antidepressants, the first options are typically those that impact the monoamine neurotransmitters, such as the selective serotonin reuptake inhibitors (SSRIs). Second-line medication treatments impact other neurotransmitters such as dopamine or norepinephrine, and some impact serotonin by alternate mechanisms. Regarding psycho‐ therapies, empirically validated interventions include cognitive behavioral therapy (CBT) and

Major Depressive Disorder is a prevalent and debilitating disorder that is a leading cause of disability worldwide [18]. MDD often starts in adolescence and places youth at risk for morbidity and mortality across the lifespan. The negative outcomes associated with MDD affect all aspects of life: personal, social, and academic functioning, and may result in chronic suffering and early death. The prognosis for depression is particularly poor when the problems are evident early on in development [19-21]. While a broader array of mood disorders (e.g., Dsythymic Disorder) may be relevant to include here, this chapter focuses on MDD because most of the predictive literature has focused on adults diagnosed with this disorder. fMRI, PET, SPECT and volumetric imaging have been used to examine predictive biomarkers of treatment response in MDD. Since a majority of findings have implicated subregions of the anterior cingulate cortex (ACC), we begin by reviewing these regions and then extend to other parts of the brain that have been implicated through various modalities as predictive of treatment response.

#### **5.1. The anterior cingulate cortex**

Many imaging studies have now implicated the pregenual ACC as a key area differentiating responders from nonresponders for a variety of psychiatric treatments. For the most part, as suggested in a meta-analysis of 23 studies of adults with MDD using various modalities and treatments [22], elevated activity or metabolism in the pregenual ACC at baseline is generally predictive of a positive response to treatment. For example, Fu and colleagues [23] reported that at baseline, increased activity in the ACC was associated with a positive treatment response to CBT. Similarly, elevated resting activity of the pregenual ACC "confers better treatment outcomes by fostering adaptive self-referential processing and by helping to recalibrate cingulate regions implicated in cognitive control" [22].

**Figure 1.** This figure illustrates the anatomical locations of divisions within the anterior cingulate cortex (ACC). A re‐ constructed MRI of the medial surface of the right hemisphere of the brain depicts the ACC (sulcus and gyrus) in rela‐ tion to the underlying corpus callosum (upper right). Cytoarchitecture and functional differences have distinguished cognitive (red) and affective (blue) divisions of the ACC (left; 31). Better treatment response to pharmacological and psychological therapies in MDD has been associated with activity within the affective division of the ACC, namely in‐ creased pre-treatment activity in the pregenual ACC (includes Brodmann Area BA32 and inferior portions of BA24] and decreased activity in the subgenual ACC (BA25 and caudal portions of BA32 and BA 24). The subgenual ACC has been identified as a target for deep-brain stimulation in patients with treatment resistant MDD [30]. Reprinted and adapted from *Trends in Cognitive Sciences*, volume 4[6], Bush, G., Luu, P., & Posner, M.I., Cognitive and emotional influ‐

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283

Not all imaging studies have pointed only to the pregenual and subgenual ACC as an important predictoroftreatmentresponseinMDD.Usingavarietyofmethodologicalapproaches,agrowing numberofstudieshaveimplicatedarangeofbrainregionsthatarebroadlyassociatedwithfrontolimbic circuitry. One fMRI study using an emotion-processing task before treatment with antidepressant medications (mirtazapine or venlafaxine) showed that at baseline, patients had higher activation in the dorsal/medial prefrontal cortex (PFC), posterior cingulate cortex and superior frontal gyrus. Furthermore, pre-treatment activations in caudate and insula were associated with successful treatment [33]. In an fMRI study that focused on anhedonia [34], patients with lower ventral/lateral PFC activation during cognitive reappraisal (suppression) of positive emotion at baseline had greater rates of improvement in anhedonia after 8 weeks of treatment with an antidepressant, specifically venlafaxine extended release or fluoxetine. Another study employing fMRI reported that with treatment using various antidepressants, greater right visual cortex and right subgenual ACC responses to sad stimuli, but not happy stimuli, were associated with a good clinical outcome in the early stages of treatment [35]. Similar to the findings reported by Light and colleagues [34], greater ventral/lateral PFC responses to

ences in anterior cingulate cortex, pages 215-222, Copyright (2000), with permission from Elsevier [32].

**5.2. Broader fronto-limbic brain regions**

Careful attention should be paid to the problem of inconsistencies across studies. For instance, as Pizagalli [22] noted, four of the studies in his meta-analysis showed that pregenual ACC predicted non-response to paroxetine [24], venlafaxine, CBT [25], and ECT [26] as measured by PET and non-response to repetitive transcranial magnetic stimulation (rTMS) as measured by SPECT [27]. Part of the inconsistency may be due to error in the assessment. Specifically, low resolution in fMRI acquisition may interfere with the ability to pinpoint exactly which areas predict treatment response versus non-response. For example, a PET study showed that pretreatment hypermetabolism at the interface between pregenual and subgenual ACC was notable in non-responders in comparison to responders [25]. Indeed, in contrast to pregenual ACC findings, it appears that the subgenual region of the ACC is associated with the opposite pattern, where some studies have suggested that increased resting metabolism or activation predicts treatment resistance [25,28,29]. In an fMRI study, hyperactivity of the subgenual ACC in response to emotional stimuli was associated with poor response to 16 sessions of CBT in 14 adults with MDD [28]. This group replicated their finding in a second, larger sample of 49 patients with MDD, finding that individuals with the lowest pretreatment sustained subgenual ACC reactivity in response to negative words displayed the most improvement after cognitive therapy [29]. Such work focusing on the subgenual ACC has contributed to current models in which this region has become one of the targets of deep-brain stimulation for patients with treatment-refractory MDD [30]. Figure 1 provides an illustration of various divisions within the ACC, including pregenual and subgenual regions.

Brain Imaging and the Prediction of Treatment Outcomes in Mood and Anxiety Disorders http://dx.doi.org/10.5772/55446 283

**Figure 1.** This figure illustrates the anatomical locations of divisions within the anterior cingulate cortex (ACC). A re‐ constructed MRI of the medial surface of the right hemisphere of the brain depicts the ACC (sulcus and gyrus) in rela‐ tion to the underlying corpus callosum (upper right). Cytoarchitecture and functional differences have distinguished cognitive (red) and affective (blue) divisions of the ACC (left; 31). Better treatment response to pharmacological and psychological therapies in MDD has been associated with activity within the affective division of the ACC, namely in‐ creased pre-treatment activity in the pregenual ACC (includes Brodmann Area BA32 and inferior portions of BA24] and decreased activity in the subgenual ACC (BA25 and caudal portions of BA32 and BA 24). The subgenual ACC has been identified as a target for deep-brain stimulation in patients with treatment resistant MDD [30]. Reprinted and adapted from *Trends in Cognitive Sciences*, volume 4[6], Bush, G., Luu, P., & Posner, M.I., Cognitive and emotional influ‐ ences in anterior cingulate cortex, pages 215-222, Copyright (2000), with permission from Elsevier [32].

#### **5.2. Broader fronto-limbic brain regions**

morbidity and mortality across the lifespan. The negative outcomes associated with MDD affect all aspects of life: personal, social, and academic functioning, and may result in chronic suffering and early death. The prognosis for depression is particularly poor when the problems are evident early on in development [19-21]. While a broader array of mood disorders (e.g., Dsythymic Disorder) may be relevant to include here, this chapter focuses on MDD because most of the predictive literature has focused on adults diagnosed with this disorder. fMRI, PET, SPECT and volumetric imaging have been used to examine predictive biomarkers of treatment response in MDD. Since a majority of findings have implicated subregions of the anterior cingulate cortex (ACC), we begin by reviewing these regions and then extend to other parts of the brain that have been implicated through various modalities as predictive of

282 Functional Brain Mapping and the Endeavor to Understand the Working Brain

Many imaging studies have now implicated the pregenual ACC as a key area differentiating responders from nonresponders for a variety of psychiatric treatments. For the most part, as suggested in a meta-analysis of 23 studies of adults with MDD using various modalities and treatments [22], elevated activity or metabolism in the pregenual ACC at baseline is generally predictive of a positive response to treatment. For example, Fu and colleagues [23] reported that at baseline, increased activity in the ACC was associated with a positive treatment response to CBT. Similarly, elevated resting activity of the pregenual ACC "confers better treatment outcomes by fostering adaptive self-referential processing and by helping to

Careful attention should be paid to the problem of inconsistencies across studies. For instance, as Pizagalli [22] noted, four of the studies in his meta-analysis showed that pregenual ACC predicted non-response to paroxetine [24], venlafaxine, CBT [25], and ECT [26] as measured by PET and non-response to repetitive transcranial magnetic stimulation (rTMS) as measured by SPECT [27]. Part of the inconsistency may be due to error in the assessment. Specifically, low resolution in fMRI acquisition may interfere with the ability to pinpoint exactly which areas predict treatment response versus non-response. For example, a PET study showed that pretreatment hypermetabolism at the interface between pregenual and subgenual ACC was notable in non-responders in comparison to responders [25]. Indeed, in contrast to pregenual ACC findings, it appears that the subgenual region of the ACC is associated with the opposite pattern, where some studies have suggested that increased resting metabolism or activation predicts treatment resistance [25,28,29]. In an fMRI study, hyperactivity of the subgenual ACC in response to emotional stimuli was associated with poor response to 16 sessions of CBT in 14 adults with MDD [28]. This group replicated their finding in a second, larger sample of 49 patients with MDD, finding that individuals with the lowest pretreatment sustained subgenual ACC reactivity in response to negative words displayed the most improvement after cognitive therapy [29]. Such work focusing on the subgenual ACC has contributed to current models in which this region has become one of the targets of deep-brain stimulation for patients with treatment-refractory MDD [30]. Figure 1 provides an illustration of various divisions within

recalibrate cingulate regions implicated in cognitive control" [22].

the ACC, including pregenual and subgenual regions.

treatment response.

**5.1. The anterior cingulate cortex**

Not all imaging studies have pointed only to the pregenual and subgenual ACC as an important predictoroftreatmentresponseinMDD.Usingavarietyofmethodologicalapproaches,agrowing numberofstudieshaveimplicatedarangeofbrainregionsthatarebroadlyassociatedwithfrontolimbic circuitry. One fMRI study using an emotion-processing task before treatment with antidepressant medications (mirtazapine or venlafaxine) showed that at baseline, patients had higher activation in the dorsal/medial prefrontal cortex (PFC), posterior cingulate cortex and superior frontal gyrus. Furthermore, pre-treatment activations in caudate and insula were associated with successful treatment [33]. In an fMRI study that focused on anhedonia [34], patients with lower ventral/lateral PFC activation during cognitive reappraisal (suppression) of positive emotion at baseline had greater rates of improvement in anhedonia after 8 weeks of treatment with an antidepressant, specifically venlafaxine extended release or fluoxetine. Another study employing fMRI reported that with treatment using various antidepressants, greater right visual cortex and right subgenual ACC responses to sad stimuli, but not happy stimuli, were associated with a good clinical outcome in the early stages of treatment [35]. Similar to the findings reported by Light and colleagues [34], greater ventral/lateral PFC responses to

eitherhappyorsadfaceswereassociatedwitharelativelypooroutcome[35].ArecentrTMSstudy found that greater symptom improvement was significantly correlated with smaller deactiva‐ tions at baseline in the ACC, the left medial orbitofrontal and the right middle frontal cortices, but larger activations in the putamen [36]. Using SPECT, responders to rTMS had greater perfu‐ sionsintheleftmedialandbilateralsuperiorfrontalcortices(BA10),theleftuncus/parahippocam‐ palcortex(BA20/BA35]andtherightthalamus[37].InaPETstudyinadultswithlate-onsetMDD, 34patientsremittedand13didnotaftertreatmentwithantidepressantsfor12weeks.Leftanterior fronto-cerebellarperfusionratiohadaglobalpredictivepowerof87%[38].Analyzingthisvariable together with the baseline variables age of onset and duration of index episode, the predictive power of the model rose to 94% [38].

ACC as being particularly salient indicators of treatment outcome. Specifically, increased activity in areas within the ACC, namely the pregenual ACC, may be particularly predictive of improved outcome following both psychological and pharmacological intervention whereas hyperactivity in the subgenual ACC may be associated with poorer treatment response. In addition, pre-treatment serotonergic binding appears predict response to antidepressant therapy in adults with MDD. Other studies have linked structural and func‐ tional differences to pharmacological and psychological treatment response, but findings differ significantly as a function of the type of imaging modality employed (e.g., fMRI task

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**Cognitive Behavioral Therapy** 

**Anti-depressant Medications**

ACC, insula, and right tempro-parietal cortex grey matter

 dorsal/medial PFC, posterior cingulate cortex, superior frontal gyrus, caudate, and insula activity in response to emotional

 ventral/lateral PFC activity when viewing happy and sad faces **Repetitive Transcranial Magnetic Stimulation** 

 perfusion in left medial frontal cortex, superior frontal cortex, left uncus/parahippocampal cortex, and right thalamus

activity in ACC regions, left-medial OFC, and right middle frontal

**Figure 2.** Summary of pre-treatment neuroimaging findings that have been associated with positive responses to Cognitive-Behavioral Therapy (CBT), repetitive transcranial magnetic stimulation (rTMS), and various anti-depressant

Several distinct types of anxiety disorders have been recognized in the field of psychiatry and delineated within the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR). Three will be discussed here, namely Obsessive Compulsive Disorder (OCD), General Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). Some initial headway is being made using neuroimaging to attempt to identify who will respond to which type of intervention for

*Increased* pregenual ACC activity *Decreased* subgenual ACC activity

pregenual ACC activity and metabolism

diencephalic serotonin availability

based paradigm, PET). See Figure 2.

*Increased*:

volumes

stimuli

*Decreased* 

*Increased:* 

*Decreased:* 

medication treatments in MDD.

**6. Anxiety disorders**

these disorders.

cortex

putamen activity

A few studies have reported on anatomical differences that have predicted MDD treatment response in broader front-limbic brain regions. Chen and colleagues [39] found that increased grey matter volumes in ACC, insula, and right tempro-parietal cortex was associated with faster rates of symptom improvement with fluoxetine. A recent study found that smaller left hippocampal volumes predicted better treatment response to six weeks of daily rTMS in adults with treatment-refractory depression; however, the significance for this prediction was only a trend [40]. If volumetric predictors could be established, these would be useful in comparison to other imaging techniques (e.g., PET, SPECT), as this type of imaging acquisition is relatively easy, safe and is consistent in analysis across sites. Like other modalities, however, the extant data are from cross-sectional studies, so it is unclear whether any differences relate to preexisting processes or to scarring from disease exposure.

#### **5.3. Serotonin systems**

Since most medication treatments focus on serotonin, a reasonable approach is to examine how either serotonin binding or brain regions associated with serotonin production might be relevant to treatment response. A SPECT study that examined serotonin binding availability found that higher pretreatment diencephalic serotonin availability significantly predicted better treatment response to 4 weeks of paroxetine [41]. Miller and colleagues [42] used PET to assess serotonin transporter (5-HTT) binding in 19 currently depressed subjects with MDD who received naturalistic antidepressant treatment for one year. They found that non-remitters had lower 5-HTT binding than controls in midbrain, amygdala, and ACC (sub-region not specified). Remitters did not differ significantly from controls or non-remitters in 5-HTT binding. Assessment of baseline 5-HTT binding as a predictor of remission status was suggestive but not significant. In a PET study of adults with MDD who received communitybased monoaminergic anti-depressant treatments by their physician, Milak and colleagues [43] reported that treatment remitters had lower activity in the region of the midbrain where monoaminergic nuclei are located prior to treatment, and that degree of improvement correlated with pretreatment midbrain activity.

#### **5.4. Major depressive disorder summary**

Studies investigating neurobiological predictors of treatment response in MDD have primarily focused on adults with the illness. The most replicated findings implicate regions within the ACC as being particularly salient indicators of treatment outcome. Specifically, increased activity in areas within the ACC, namely the pregenual ACC, may be particularly predictive of improved outcome following both psychological and pharmacological intervention whereas hyperactivity in the subgenual ACC may be associated with poorer treatment response. In addition, pre-treatment serotonergic binding appears predict response to antidepressant therapy in adults with MDD. Other studies have linked structural and func‐ tional differences to pharmacological and psychological treatment response, but findings differ significantly as a function of the type of imaging modality employed (e.g., fMRI task based paradigm, PET). See Figure 2.


**Figure 2.** Summary of pre-treatment neuroimaging findings that have been associated with positive responses to Cognitive-Behavioral Therapy (CBT), repetitive transcranial magnetic stimulation (rTMS), and various anti-depressant medication treatments in MDD.

### **6. Anxiety disorders**

eitherhappyorsadfaceswereassociatedwitharelativelypooroutcome[35].ArecentrTMSstudy found that greater symptom improvement was significantly correlated with smaller deactiva‐ tions at baseline in the ACC, the left medial orbitofrontal and the right middle frontal cortices, but larger activations in the putamen [36]. Using SPECT, responders to rTMS had greater perfu‐ sionsintheleftmedialandbilateralsuperiorfrontalcortices(BA10),theleftuncus/parahippocam‐ palcortex(BA20/BA35]andtherightthalamus[37].InaPETstudyinadultswithlate-onsetMDD, 34patientsremittedand13didnotaftertreatmentwithantidepressantsfor12weeks.Leftanterior fronto-cerebellarperfusionratiohadaglobalpredictivepowerof87%[38].Analyzingthisvariable together with the baseline variables age of onset and duration of index episode, the predictive

A few studies have reported on anatomical differences that have predicted MDD treatment response in broader front-limbic brain regions. Chen and colleagues [39] found that increased grey matter volumes in ACC, insula, and right tempro-parietal cortex was associated with faster rates of symptom improvement with fluoxetine. A recent study found that smaller left hippocampal volumes predicted better treatment response to six weeks of daily rTMS in adults with treatment-refractory depression; however, the significance for this prediction was only a trend [40]. If volumetric predictors could be established, these would be useful in comparison to other imaging techniques (e.g., PET, SPECT), as this type of imaging acquisition is relatively easy, safe and is consistent in analysis across sites. Like other modalities, however, the extant data are from cross-sectional studies, so it is unclear whether any differences relate to pre-

Since most medication treatments focus on serotonin, a reasonable approach is to examine how either serotonin binding or brain regions associated with serotonin production might be relevant to treatment response. A SPECT study that examined serotonin binding availability found that higher pretreatment diencephalic serotonin availability significantly predicted better treatment response to 4 weeks of paroxetine [41]. Miller and colleagues [42] used PET to assess serotonin transporter (5-HTT) binding in 19 currently depressed subjects with MDD who received naturalistic antidepressant treatment for one year. They found that non-remitters had lower 5-HTT binding than controls in midbrain, amygdala, and ACC (sub-region not specified). Remitters did not differ significantly from controls or non-remitters in 5-HTT binding. Assessment of baseline 5-HTT binding as a predictor of remission status was suggestive but not significant. In a PET study of adults with MDD who received communitybased monoaminergic anti-depressant treatments by their physician, Milak and colleagues [43] reported that treatment remitters had lower activity in the region of the midbrain where monoaminergic nuclei are located prior to treatment, and that degree of improvement

Studies investigating neurobiological predictors of treatment response in MDD have primarily focused on adults with the illness. The most replicated findings implicate regions within the

power of the model rose to 94% [38].

**5.3. Serotonin systems**

existing processes or to scarring from disease exposure.

284 Functional Brain Mapping and the Endeavor to Understand the Working Brain

correlated with pretreatment midbrain activity.

**5.4. Major depressive disorder summary**

Several distinct types of anxiety disorders have been recognized in the field of psychiatry and delineated within the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR). Three will be discussed here, namely Obsessive Compulsive Disorder (OCD), General Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). Some initial headway is being made using neuroimaging to attempt to identify who will respond to which type of intervention for these disorders.

#### **6.1. Obsessive compulsive disorder**

OCD is a significantly impairing mental illness associated with debilitating cycles of persistent anxiety-provoking thoughts, impulses or images that are accompanied by repetitive behaviors aimed at counteracting anxiety [44]. For example, an individual may have constant and intrusive thoughts that surfaces that he or she comes in contact with are dirty or have germs. These thoughts are experienced as extremely distressing to the individual, who as a result, engages in compulsive behavior (e.g., repetitive hand washing) to prevent or alleviate fear associated with the content of obsessive thoughts (e.g., contamination).

One study to date has investigated structural predictors of treatment response in OCD. Hoexter and colleagues [45] recruited thirty-eight treatment naive individuals with a primary diagnosis of OCD and randomized them to receive either 12 weeks of treatment with fluoxetine or 12 weekly sessions of group CBT. Specifically interested in structural prognostic indicators of treatment response, Hoexter et al. [45] found that smaller grey matter volumes prior to treatment initiation in the right middle lateral orbital frontal cortex (OFC) predicted a decrease in OCD symptoms following pharmacological intervention whereas greater grey matter volumes in the medial prefrontal cortex predicted better response following CBT (Figure 3). This study suggests that improvement via pharmacologic and psychological approaches in

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Numerous functional imaging studies, primarily using PET imaging, have also investigated biological prognostic indicators in OCD. Brody and colleagues [46] showed that decreased metabolic activity in the orbitofrontal cortex (OFC) was associated with better outcomes with fluoxetine treatment whereas as increased metabolism in the same region predicted improve‐ ment following cognitive behavioral therapy (CBT). However, it is important to note that, unlike the Hoexter et al. [45] study above, treatment designation in this study was not randomized. Similar to Brody et al. [46], Saxena et al. [47] found an inverse relationship between OFC glucose metabolism using PET and response to 8-12 weeks of SSRI (paroxetine) treatment in 20 OCD outpatients. These negative correlations between regional OFC glucose metabolism and treatment response appear to be present in adults with childhood onset OCD [48]. In a symptoms provocation study, where individuals with contamination-related OCD were exposed to neutral and contamination specific stimuli, lower regional cerebral flood flow (rCBF) measured by PET in the OFC and higher pre-treatment rCBF in the bilateral posterior cinglate cortex (PCC) predicted better symptom reduction after a 12-week open trial of fluvoxamine [49]. The relationship between rCBF and treatment outcome was present in response to both OCD-related and neutral stimuli, suggesting that activity in the OFC and PCC exist independent of OCD-salient cues. Using a functional MRI paradigm that evoked OCD symptoms by displaying salient illness-related words, BOLD response in the right cerebellum and left superior temporal gyrus (STG) positively correlated with improvements in OCD

symptoms following 12 weeks of SSRI (fluvoxamine) pharmacotherapy [50].

SRIs may improve OCD and MDD pathology by its impact at different brain sites.

Given that SSRI medications have been shown to be effective in both OCD and MDD, Saxena et al. [47] examined whether pretreatment brain activity would differentially predict response to pharmacotherapy in these two different patient groups. 27 individuals with OCD and 27 with MDD underwent PET to measure cerebral glucose metabolism prior to paroxetine treatment. These researchers concluded that OCD symptom improvement was related to increased pretreatment metabolism in the right caudate nucleus whereas decreased depression symptoms were predicted by low amygdala and thalamus but increased medial prefrontal and ACC metabolism prior to treatment. This study, in particular, suggests that treatment with

Using SPECT imaging, investigators have examined neurochemical transporters as predictors of response to medication treatments in OCD. Specifically, Zitteral et al. [51] found that serotonin transporter (SERT) availability in thalamic and hypothalamic brain regions predict‐

OCD may occur via different mechanisms.

**Figure 3.** *Top:* Loci of significant correlations between pretreatment gray matter volume and subsequent response to Fluoxetine (top left) and CBT (top right). *Bottom left*: negative statistically significant correlation between pretreat‐ ment gray matter volume within the right middle lateral orbitofrontal cortex and improvements in OCD severity (measured by the Yale-Brown Obsessive Compulsive Scale: Y–BOCS) following treatment with fluoxetine. *Bottom right*: positive statistically significant correlation between pretreatment gray matter volume within the right medial prefrontal cortex, (subgenual anterior cingulate cortex) and Y–BOCS improvement following treatment with CBT. Re‐ printed and adapted from *European Neuropsychopharmacology*, published online, Hoexter et al., Differential prefron‐ tal gray matter correlates of treatment response to fluoxetine or cognitive-behavioral therapy in obsessive–compulsive disorder, pages 1-12, Copyright (2012), with permission from Elsevier [45].

One study to date has investigated structural predictors of treatment response in OCD. Hoexter and colleagues [45] recruited thirty-eight treatment naive individuals with a primary diagnosis of OCD and randomized them to receive either 12 weeks of treatment with fluoxetine or 12 weekly sessions of group CBT. Specifically interested in structural prognostic indicators of treatment response, Hoexter et al. [45] found that smaller grey matter volumes prior to treatment initiation in the right middle lateral orbital frontal cortex (OFC) predicted a decrease in OCD symptoms following pharmacological intervention whereas greater grey matter volumes in the medial prefrontal cortex predicted better response following CBT (Figure 3). This study suggests that improvement via pharmacologic and psychological approaches in OCD may occur via different mechanisms.

**6.1. Obsessive compulsive disorder**

286 Functional Brain Mapping and the Endeavor to Understand the Working Brain

OCD is a significantly impairing mental illness associated with debilitating cycles of persistent anxiety-provoking thoughts, impulses or images that are accompanied by repetitive behaviors aimed at counteracting anxiety [44]. For example, an individual may have constant and intrusive thoughts that surfaces that he or she comes in contact with are dirty or have germs. These thoughts are experienced as extremely distressing to the individual, who as a result, engages in compulsive behavior (e.g., repetitive hand washing) to prevent or alleviate fear

**Figure 3.** *Top:* Loci of significant correlations between pretreatment gray matter volume and subsequent response to Fluoxetine (top left) and CBT (top right). *Bottom left*: negative statistically significant correlation between pretreat‐ ment gray matter volume within the right middle lateral orbitofrontal cortex and improvements in OCD severity (measured by the Yale-Brown Obsessive Compulsive Scale: Y–BOCS) following treatment with fluoxetine. *Bottom right*: positive statistically significant correlation between pretreatment gray matter volume within the right medial prefrontal cortex, (subgenual anterior cingulate cortex) and Y–BOCS improvement following treatment with CBT. Re‐ printed and adapted from *European Neuropsychopharmacology*, published online, Hoexter et al., Differential prefron‐ tal gray matter correlates of treatment response to fluoxetine or cognitive-behavioral therapy in obsessive–compulsive

disorder, pages 1-12, Copyright (2012), with permission from Elsevier [45].

associated with the content of obsessive thoughts (e.g., contamination).

Numerous functional imaging studies, primarily using PET imaging, have also investigated biological prognostic indicators in OCD. Brody and colleagues [46] showed that decreased metabolic activity in the orbitofrontal cortex (OFC) was associated with better outcomes with fluoxetine treatment whereas as increased metabolism in the same region predicted improve‐ ment following cognitive behavioral therapy (CBT). However, it is important to note that, unlike the Hoexter et al. [45] study above, treatment designation in this study was not randomized. Similar to Brody et al. [46], Saxena et al. [47] found an inverse relationship between OFC glucose metabolism using PET and response to 8-12 weeks of SSRI (paroxetine) treatment in 20 OCD outpatients. These negative correlations between regional OFC glucose metabolism and treatment response appear to be present in adults with childhood onset OCD [48]. In a symptoms provocation study, where individuals with contamination-related OCD were exposed to neutral and contamination specific stimuli, lower regional cerebral flood flow (rCBF) measured by PET in the OFC and higher pre-treatment rCBF in the bilateral posterior cinglate cortex (PCC) predicted better symptom reduction after a 12-week open trial of fluvoxamine [49]. The relationship between rCBF and treatment outcome was present in response to both OCD-related and neutral stimuli, suggesting that activity in the OFC and PCC exist independent of OCD-salient cues. Using a functional MRI paradigm that evoked OCD symptoms by displaying salient illness-related words, BOLD response in the right cerebellum and left superior temporal gyrus (STG) positively correlated with improvements in OCD symptoms following 12 weeks of SSRI (fluvoxamine) pharmacotherapy [50].

Given that SSRI medications have been shown to be effective in both OCD and MDD, Saxena et al. [47] examined whether pretreatment brain activity would differentially predict response to pharmacotherapy in these two different patient groups. 27 individuals with OCD and 27 with MDD underwent PET to measure cerebral glucose metabolism prior to paroxetine treatment. These researchers concluded that OCD symptom improvement was related to increased pretreatment metabolism in the right caudate nucleus whereas decreased depression symptoms were predicted by low amygdala and thalamus but increased medial prefrontal and ACC metabolism prior to treatment. This study, in particular, suggests that treatment with SRIs may improve OCD and MDD pathology by its impact at different brain sites.

Using SPECT imaging, investigators have examined neurochemical transporters as predictors of response to medication treatments in OCD. Specifically, Zitteral et al. [51] found that serotonin transporter (SERT) availability in thalamic and hypothalamic brain regions predict‐ ed better treatment outcomes following 14 weeks of sertraline (an SSRI) administration in a homogenous sample of OCD patients with behavioral checking compulsions. It is important to note that SERT availability has been associated with OCD symptom severity in previous studies [51,52], suggesting that individuals with higher transporter availability may be more likely to respond favorably to SSRIs as their serotonin system is less impaired prior to beginning intervention. Another SPECT study prior to 12 weeks of treatment with Inositol, a chemical precursor of second messengers in critical brain signaling pathways, found that higher blood perfusion in the left medial prefrontal regions differentiated OCD responders from nonresponders [53] and regional cerebral blood flow (rCBF) in cerebellar regions in addition to whole brain tracer uptake has also been shown to be elevated in OCD responders compared to nonresponders prior to beginning an open label trial of fluvoxamine [54].

brain areas specifically predict GAD treatment outcome. However, the overlap in findings observed between studies in GAD and MDD, where activity in the pregenual ACC is impli‐ cated as a predictor of treatment response, may highlight the commonality in the underlying mechanisms of these disorders, which are commonly co-morbid. Future studies employing randomized, placebo controlled designs will need to be conducted in order to ensure that findings described above predict improvement with venlafaxine, not simply improvement in

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SAD is characterized by intense fear of being in social situations in which judgment or embarrassment may occur. Age of onset in SAD is typically during mid-teen years, where symptoms tend to follow a long, protracted course of illness that often goes untreated [61].

Two known studies have investigated neuroimaging predictors of treatment outcome in SAD following psychotherapy interventions. Nine patients diagnosed with SAD underwent PET imaging using dopamine agonist ligands to examine dopamine function prior to 15 weeks of CBT [62]. The study found that reduced dopamine D2 receptor binding in the medial prefrontal cortex and the hippocampus prior to treatment predicted greater changes in self-reported

Employing fMRI methodology, Doehrmann et al. [63] investigated functional brain activity in response to emotional faces and scenes. Using whole-brain regression analyses, Doehrmann and colleagues found that BOLD response to angry vs. neutral faces in right occiptotemporal brain areas predicted better response to CBT, especially in initially more severe patients. This was true even when accounting for possible confounding effects of depressive co-morbidity. Researchers purport that predictive activity to faces over emotional non-face scenes is consistent with the social nature of SAD. While connectivity between higher-order visual and emotion processing areas has been shown to be altered in SAD, the authors note that further research is needed to elucidate the how the relationship between pretreatment activity in occiptotemporal brains relates to altered activity in limbic brain regions identified in other

Within the class of anxiety disorders, neuroimaging outcome prediction studies have, thus far, focused mostly on OCD. Findings implicate the OFC as being especially important in regards to predicting outcomes following pharmacological and psychological interventions in this disorder; however, areas of the PFC, ACC, caudate, cerebellum and STG in addition to serotonin system functioning may be salient predictors of treatment response in OCD as well. Research in GAD and SAD is still in its infancy; however, initial studies suggest that activity in the ACC may differentiate individual response to medication treatment in GAD whereas D2 receptor binding in the prefrontal cortex and hippocampus can be used to predict better

social anxiety outcomes following psychological intervention. (Figure 4).

general.

**6.3. Social anxiety disorder**

social anxiety symptoms after CBT.

areas of research.

**6.4. Anxiety disorder summary**

#### **6.2. Generalized anxiety disorder**

GAD is a chronic and prevalent disorder characterized by frequent and excessive worry that is difficult to control [55]. This worry lasts for a minimum of six months and is associated with somatic and cognitive difficulties (e.g., fatigue, concentration problems), significant role impairment [44] and suicide [56].

To date, two known studies have investigated predictors of treatment response and nonresponse in GAD, both involving the use of fMRI methodology. Nitschke et al. [57] looked at brain reactivity to anticipatory cues of neutral and adverse stimuli (e.g., attack scenes vs. household items) in adults with GAD and examined how individual responses to these cues predicted outcome following an 8-week open label trial of venlafaxine, a type of selective serotonin and norepinephrine reuptake inhibitor (SNRI). Reminiscent of what has been found in the depression literature as discussed above, Nitschke et al. [57] found that activity in the pregenual ACC in response to anticipatory aversive and neutral cues predicted better out‐ comes. Specifically, individuals with hyperresponsivity in the pregenual ACC showed greater response to treatment measured by decreases in self-reported anxiety symptoms. The prege‐ nual ACC is thought to play a role in the detection and resolution of emotional conflict [58] and thus Nitschke et al. [57] have proposed that individuals with greater pretreatment activity in this area may be better able to engage top-down control and regulate emotions when given treatment.

In the same participant pool, Whalen et al. [59] examined whether response to an emotional faces task could predict response following venlafaxine treatment in GAD. They specifically examined reactivity in the amygdala and rostral region of the ACC, as these areas have been found to be functionally related and relevant to the study of visually presented expressions of emotions [60]. Results from this study showed that increased reactivity in the rostral ACC and decreased reactivity in the amygdala when viewing fearful faces was related to improved outcomes after the 8-week medication trial (similarly measured by self-reported anxiety symptoms).

Since all participants were free from comorbid diagnoses, findings in these two studies cannot be accounted for by any other axis I disorder. In addition, results persisted after controlling for current depressive symptoms, further strengthening the conclusion that activity in these brain areas specifically predict GAD treatment outcome. However, the overlap in findings observed between studies in GAD and MDD, where activity in the pregenual ACC is impli‐ cated as a predictor of treatment response, may highlight the commonality in the underlying mechanisms of these disorders, which are commonly co-morbid. Future studies employing randomized, placebo controlled designs will need to be conducted in order to ensure that findings described above predict improvement with venlafaxine, not simply improvement in general.

### **6.3. Social anxiety disorder**

ed better treatment outcomes following 14 weeks of sertraline (an SSRI) administration in a homogenous sample of OCD patients with behavioral checking compulsions. It is important to note that SERT availability has been associated with OCD symptom severity in previous studies [51,52], suggesting that individuals with higher transporter availability may be more likely to respond favorably to SSRIs as their serotonin system is less impaired prior to beginning intervention. Another SPECT study prior to 12 weeks of treatment with Inositol, a chemical precursor of second messengers in critical brain signaling pathways, found that higher blood perfusion in the left medial prefrontal regions differentiated OCD responders from nonresponders [53] and regional cerebral blood flow (rCBF) in cerebellar regions in addition to whole brain tracer uptake has also been shown to be elevated in OCD responders compared to nonresponders prior to beginning an open label trial of fluvoxamine [54].

288 Functional Brain Mapping and the Endeavor to Understand the Working Brain

GAD is a chronic and prevalent disorder characterized by frequent and excessive worry that is difficult to control [55]. This worry lasts for a minimum of six months and is associated with somatic and cognitive difficulties (e.g., fatigue, concentration problems), significant role

To date, two known studies have investigated predictors of treatment response and nonresponse in GAD, both involving the use of fMRI methodology. Nitschke et al. [57] looked at brain reactivity to anticipatory cues of neutral and adverse stimuli (e.g., attack scenes vs. household items) in adults with GAD and examined how individual responses to these cues predicted outcome following an 8-week open label trial of venlafaxine, a type of selective serotonin and norepinephrine reuptake inhibitor (SNRI). Reminiscent of what has been found in the depression literature as discussed above, Nitschke et al. [57] found that activity in the pregenual ACC in response to anticipatory aversive and neutral cues predicted better out‐ comes. Specifically, individuals with hyperresponsivity in the pregenual ACC showed greater response to treatment measured by decreases in self-reported anxiety symptoms. The prege‐ nual ACC is thought to play a role in the detection and resolution of emotional conflict [58] and thus Nitschke et al. [57] have proposed that individuals with greater pretreatment activity in this area may be better able to engage top-down control and regulate emotions when given

In the same participant pool, Whalen et al. [59] examined whether response to an emotional faces task could predict response following venlafaxine treatment in GAD. They specifically examined reactivity in the amygdala and rostral region of the ACC, as these areas have been found to be functionally related and relevant to the study of visually presented expressions of emotions [60]. Results from this study showed that increased reactivity in the rostral ACC and decreased reactivity in the amygdala when viewing fearful faces was related to improved outcomes after the 8-week medication trial (similarly measured by self-reported anxiety

Since all participants were free from comorbid diagnoses, findings in these two studies cannot be accounted for by any other axis I disorder. In addition, results persisted after controlling for current depressive symptoms, further strengthening the conclusion that activity in these

**6.2. Generalized anxiety disorder**

impairment [44] and suicide [56].

treatment.

symptoms).

SAD is characterized by intense fear of being in social situations in which judgment or embarrassment may occur. Age of onset in SAD is typically during mid-teen years, where symptoms tend to follow a long, protracted course of illness that often goes untreated [61].

Two known studies have investigated neuroimaging predictors of treatment outcome in SAD following psychotherapy interventions. Nine patients diagnosed with SAD underwent PET imaging using dopamine agonist ligands to examine dopamine function prior to 15 weeks of CBT [62]. The study found that reduced dopamine D2 receptor binding in the medial prefrontal cortex and the hippocampus prior to treatment predicted greater changes in self-reported social anxiety symptoms after CBT.

Employing fMRI methodology, Doehrmann et al. [63] investigated functional brain activity in response to emotional faces and scenes. Using whole-brain regression analyses, Doehrmann and colleagues found that BOLD response to angry vs. neutral faces in right occiptotemporal brain areas predicted better response to CBT, especially in initially more severe patients. This was true even when accounting for possible confounding effects of depressive co-morbidity. Researchers purport that predictive activity to faces over emotional non-face scenes is consistent with the social nature of SAD. While connectivity between higher-order visual and emotion processing areas has been shown to be altered in SAD, the authors note that further research is needed to elucidate the how the relationship between pretreatment activity in occiptotemporal brains relates to altered activity in limbic brain regions identified in other areas of research.

#### **6.4. Anxiety disorder summary**

Within the class of anxiety disorders, neuroimaging outcome prediction studies have, thus far, focused mostly on OCD. Findings implicate the OFC as being especially important in regards to predicting outcomes following pharmacological and psychological interventions in this disorder; however, areas of the PFC, ACC, caudate, cerebellum and STG in addition to serotonin system functioning may be salient predictors of treatment response in OCD as well. Research in GAD and SAD is still in its infancy; however, initial studies suggest that activity in the ACC may differentiate individual response to medication treatment in GAD whereas D2 receptor binding in the prefrontal cortex and hippocampus can be used to predict better social anxiety outcomes following psychological intervention. (Figure 4).


in anxiety disorders have primarily focused on OCD, most frequently implicating the orbital frontal cortex. Treatment predication research in other anxiety disorders, such as GAD and

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While the research reviewed above provides an initial foundation for future research to advance personalized psychiatric care, several points need to be highlighted when considering these treatment studies. Most of the studies to date have reported results on small samples with uncontrolled treatment delivery, assessing imaging in the context of either a naturalistic and community-based treatment, or in the setting of a trial that compared different treatments but then examined effects after treatment arms were collapsed. While the field is currently limited in that large-scale treatment studies that involve comprehensive neurobiological assessments are highly labor intensive and are rarely feasible (for a noted exception see Dunlop et al. [64], next steps will require larger, more diverse samples and controlled treatment

Most research to date has been conducted in adult samples with little research examining biological predictors of treatment response in younger populations. It will be particularly important for future research to identify predictors of treatment response for children and adolescents suffering from anxiety and depression given that neurobiological factors associ‐ ated with treatment outcomes may differ across development, early onset is a negative prognostic indicator of future problems and plasticity in key neural networks may be amenable to alteration during this period in development [20,65]. Furthermore, with the exception of symptom severity [20,66,67], younger age [67] and positive family history [68], few psycho‐ social indexes have consistently identified who responds favorably to an intervention [69], and very little is known as to which variables differentially predict response across types of interventions. Recent work has taken initial steps towards using brain imaging methods to identify biological markers for use in tailoring treatment for adolescent depression. In the only study to date that has published data on predictive imaging for adolescent depression, Forbes et al. [70] examined reward-related brain functioning in adolescent MDD before treatment with either CBT (n=7) or CBT plus a selective serotonin reuptake inhibitor (n=6). Due to the small number, the treatment arms were combined. Greater striatal activity during reward outcome predicted higher general severity after treatment, whereas greater striatal activation

Inclusion of broader populations characterized as suffering from internalizing disorders may provide additional insights into relevant brain mechanisms for prevention. As previously mentioned, internalizing disorders have high rates of co-morbidity with one another, and although research to date has focused on depression and anxiety disorders, future research may be needed to delineate the biological underpinnings that account for such overlap. This work may help us refine particular psychological and pharmacological treatments. Similarly, expanding prediction studies to include internalizing problems outside of those classified as mood or anxiety disorders are also needed. Particularly, Eating Disorders have been charac‐ terized as belonging to the internalizing construct [71]; however, while imaging research has begun to characterize the neurobiological underpinnings of Eating Disorders [72-75], research has yet to examine neurobiological predictors of treatment response in this population.

delivery to more accurately and reliably assess prediction across interventions.

during reward anticipation predicted lower anxiety after treatment.

SAD is beginning to receive more attention.

**Figure 4.** Summary of pre-treatment neuroimaging findings that have been associated with positive responses to either Cognitive-Behavioral Therapy or anti-depressant medication treatments in anxiety disorders. *(PFC=prefrontal cortex, OFC=orbital frontal cortex, SERT=serotonin transporter, STG=superior temporal gyrus, ACC=anterior cingulate cortex).*

### **7. Conclusions and future clinical applications**

Internalizing disorders are serious and often debilitating problems associated with significant impairment and individual suffering. While pharmacological and psychological interventions show some efficacy in the treatment of MDD and anxiety disorders, more precise personalized care is needed in order to improve overall treatment outcomes and to reduce the cost of psychiatric interventions. While this avenue of research is in its infancy, the use of imaging methods to identify neurobiological markers that predict treatment outcome holds the potential to further advance the field of personalized psychiatry and may eventually help guide clinicians towards the selection of treatments that have the highest likelihood of improving individuals patients' symptoms.

Advanced technologies have greatly facilitated efforts to examine anomalies in neural structure and function over the past decade. The findings in MDD show that regions of the anterior cingulate cortex have most reliably been identified as areas differentiating treatment responders from non-responders. Studies aimed at examining predictors of treatment outcome in anxiety disorders have primarily focused on OCD, most frequently implicating the orbital frontal cortex. Treatment predication research in other anxiety disorders, such as GAD and SAD is beginning to receive more attention.

**Obsessive-Compulsive Disorder** 

 lower regional cerebral flood flow in OFC and higher regional cerebral flood flow in bilateral posterior cinglate cortex in response to symptom provocation increased right cerebellum and left STG activity to illness-related words

**Generalized Anxiety Disorder**

hyperactivity in the pregenual ACC in response to anticipation of aversive and

**Social Anxiety Disorder** 

**Figure 4.** Summary of pre-treatment neuroimaging findings that have been associated with positive responses to either Cognitive-Behavioral Therapy or anti-depressant medication treatments in anxiety disorders. *(PFC=prefrontal cortex, OFC=orbital frontal cortex, SERT=serotonin transporter, STG=superior temporal gyrus, ACC=anterior cingulate cortex).*

Internalizing disorders are serious and often debilitating problems associated with significant impairment and individual suffering. While pharmacological and psychological interventions show some efficacy in the treatment of MDD and anxiety disorders, more precise personalized care is needed in order to improve overall treatment outcomes and to reduce the cost of psychiatric interventions. While this avenue of research is in its infancy, the use of imaging methods to identify neurobiological markers that predict treatment outcome holds the potential to further advance the field of personalized psychiatry and may eventually help guide clinicians towards the selection of treatments that have the highest likelihood of

Advanced technologies have greatly facilitated efforts to examine anomalies in neural structure and function over the past decade. The findings in MDD show that regions of the anterior cingulate cortex have most reliably been identified as areas differentiating treatment responders from non-responders. Studies aimed at examining predictors of treatment outcome

increased activity in the rostral ACC and decreased amygdala activity when

 reduced dopamine D2 receptor binding in medial PFC and hippocampus increased activity in right occiptotemporal brain areas in response to response to

*Cognitive Behavioral Therapy:* 

*Pharmacotherapy:* 

*Pharmacotherapy:* 

neutral stimuli

viewing fearful faces

*Cognitive Behavioral Therapy:* 

angry vs. neutral faces

**7. Conclusions and future clinical applications**

improving individuals patients' symptoms.

increased metabolism in OFC

290 Functional Brain Mapping and the Endeavor to Understand the Working Brain

 decreased metabolic activity in OFC increased right caudate nucleus metabolism

larger grey matter volumes in medial PFC

smaller grey matter volumes in right middle lateral OFC

SERT availability in thalamic and hypothalamic brain regions

While the research reviewed above provides an initial foundation for future research to advance personalized psychiatric care, several points need to be highlighted when considering these treatment studies. Most of the studies to date have reported results on small samples with uncontrolled treatment delivery, assessing imaging in the context of either a naturalistic and community-based treatment, or in the setting of a trial that compared different treatments but then examined effects after treatment arms were collapsed. While the field is currently limited in that large-scale treatment studies that involve comprehensive neurobiological assessments are highly labor intensive and are rarely feasible (for a noted exception see Dunlop et al. [64], next steps will require larger, more diverse samples and controlled treatment delivery to more accurately and reliably assess prediction across interventions.

Most research to date has been conducted in adult samples with little research examining biological predictors of treatment response in younger populations. It will be particularly important for future research to identify predictors of treatment response for children and adolescents suffering from anxiety and depression given that neurobiological factors associ‐ ated with treatment outcomes may differ across development, early onset is a negative prognostic indicator of future problems and plasticity in key neural networks may be amenable to alteration during this period in development [20,65]. Furthermore, with the exception of symptom severity [20,66,67], younger age [67] and positive family history [68], few psycho‐ social indexes have consistently identified who responds favorably to an intervention [69], and very little is known as to which variables differentially predict response across types of interventions. Recent work has taken initial steps towards using brain imaging methods to identify biological markers for use in tailoring treatment for adolescent depression. In the only study to date that has published data on predictive imaging for adolescent depression, Forbes et al. [70] examined reward-related brain functioning in adolescent MDD before treatment with either CBT (n=7) or CBT plus a selective serotonin reuptake inhibitor (n=6). Due to the small number, the treatment arms were combined. Greater striatal activity during reward outcome predicted higher general severity after treatment, whereas greater striatal activation during reward anticipation predicted lower anxiety after treatment.

Inclusion of broader populations characterized as suffering from internalizing disorders may provide additional insights into relevant brain mechanisms for prevention. As previously mentioned, internalizing disorders have high rates of co-morbidity with one another, and although research to date has focused on depression and anxiety disorders, future research may be needed to delineate the biological underpinnings that account for such overlap. This work may help us refine particular psychological and pharmacological treatments. Similarly, expanding prediction studies to include internalizing problems outside of those classified as mood or anxiety disorders are also needed. Particularly, Eating Disorders have been charac‐ terized as belonging to the internalizing construct [71]; however, while imaging research has begun to characterize the neurobiological underpinnings of Eating Disorders [72-75], research has yet to examine neurobiological predictors of treatment response in this population.

While research reviewed above employed the use of fMRI, PET, and SPECT imaging techni‐ ques, the study of predictive biomarkers of treatment outcome should be expanded with the use of other neuroimaging methods. For example, the use of spectroscopy would provide evidence of pretreatment chemical and metabolite profiles predictive of treatment outcome. Similarly, resting state fMRI methods might be particularly useful, potentially elucidating our understanding of how different patterns of functional connectivity within and between neural circuits relate to treatment outcome or treatment resistance. In addition, it is expected that future research will increasingly employ the use of multi-modal approaches in predictive treatment research, helping to identify other biological markers not capable of being assessed via neuroimaging techniques. For example, current efforts are underway to more definitively assess biological markers for treatment response across treatments in adults with MDD (CBT, duloxetine, escitalopram) using multi-modal techniques including resting fMRI, neuroendo‐ crine assessments, immune markers and measures of gene expression [64]. Additionally, neurobiological predictors of treatment response that have been identified thus far are not sufficiently strong enough nor have they been sufficiently replicated to warrant changes in clinical decision making at this juncture. Perhaps and understanding of broader brain net‐ works will be enhanced by profiling numerous brain functions and structures that, in compi‐ lation, will more aptly predict treatment response.

a robust predictor of treatment response in larger controlled studies, one potential implication of thistypeofresearchcouldbethatindividualpatientspresentingwithMDDmayundergoanMRI tomeasurepregenualandsubgenualACCactivity,whichcouldinturnbeusedtoguidewhether theindividualisreferredforCognitiveBehavioralTherapyorpharmacotherapy.Currently,such an approach is likely cost prohibitive and may not be sufficiently feasible given the constraints of data acquisition, preprocessing and analysis. Alternatively, once neuroimaging markers that predict treatment outcome are well established, neuroimaging technology used to identify brain regions and functions associated with treatment outcome may be used to aid in the develop‐ ment or refinement of proxy biomarkers, such as neuropsychological functioning or serum markers, that could feasibly measure prediction and be disseminated for wide-spread applica‐

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Here we have focused on neurobiological factors that can be measured at baseline to predict treatment. However, increased understanding of what aspects of neurobiological factors change over the course of treatment may also serve to enhance our understanding of the pathophysiology of internalizing problems and aid in identifying neurobiological factors that are likely to predict treatment outcomes. A recent review of the literature on changes with treatment concludes that a functional normalization of the fear network occurs with recovery across treatments [81]. Specifically, evidence suggests that both psychotherapy and psycho‐ pharmacology each in specific ways result in normalization of activity in the target structures (respectively, "top-down" and "bottom-up" effects). Methodologies that capitalize on considering both prediction of and change associated with treatment outcomes are needed. Advanced techniques, such as those used in neuroimaging research, offer tremendous benefit to our society in that they provide the capability to improve our understanding of the patho‐ physiology underlying internalizing problems and may eventually offer guidance in regards to treatment selection, allowing providers to choose only those treatments that are most likely to be maximally effective for a given individual. This area of research is still developing. The concept of neural network medicine envisions a time to come when treatments will be used to target a neural network rather than simply components within the network. While personal‐ ized medicine in psychiatry is still at an early stage, "it has a very promising future" (Costa e

and Kathryn R. Cullen3

1 Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA

2 Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA

3 Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA

tion of personalized psychiatric care.

Silva, in press).

**Author details**

Leah M. Jappe1\*, Bonnie Klimes-Dougan2

\*Address all correspondence to: japp0005@umn.edu

An exciting advance that has the potential to improve personalized care is recent work incorporating machine-learning approaches to classify groups—disease versus no disease, or responders versus non-responders. Machine learning approaches are "brain reading" or "brain decoding" methods. Instead of analyzing the brain voxel by voxel, data from groups of voxels are used to train a computer program to distinguish different classes of data (e.g., treatment responders from treatment non-responders) and provide maps which indicate the levels by which different brain regions are accurately involved in the classification [76]. In a study that analyzed grey and white matter volumes, using a support vector machine (SVM) approach, Gong and colleagues [77] showed they were able to predict response versus nonresponse based on gray matter with 70% accuracy and based on white matter with 65% accuracy. Another study that used SVM measured responses to sad faces with fMRI before CBT in 16 unmedicated depressed adults. Brain regions implicated in clinical remission included ACC, superior and middle frontal cortices, paracentral cortex, superior parietal cortex, precuneus, and cerebellum, with 71% sensitivity and 86% specificity of response prediction [78]. A third SVM study found that the pattern of brain activity during sad facial processing correctly classified patients' clinical response at baseline, prior to the initiation of treatment, at trend levels of significance [23]. SVM approaches are still new in the field and the value of such non-traditional statistical approaches still needs to be weighed.

Practicalconstraintsmustbeconsideredasfutureeffortsaimtotranslateknowledgeofneurobio‐ logical predictors of treatment response into clinical practice. In addition to providing reliable data with high sensitivity and specificity, ideally a biomarker would be low in cost, easy to collect and simple to analyze [79]. It is possible that these approaches could be mechanized sufficiently toreducecostsandincreasefeasibilitysothatoneday,routineclinicalassessmentwillincludethe collection of data via neuroimaging technology [80]. For example, if activity in the ACC remains a robust predictor of treatment response in larger controlled studies, one potential implication of thistypeofresearchcouldbethatindividualpatientspresentingwithMDDmayundergoanMRI tomeasurepregenualandsubgenualACCactivity,whichcouldinturnbeusedtoguidewhether theindividualisreferredforCognitiveBehavioralTherapyorpharmacotherapy.Currently,such an approach is likely cost prohibitive and may not be sufficiently feasible given the constraints of data acquisition, preprocessing and analysis. Alternatively, once neuroimaging markers that predict treatment outcome are well established, neuroimaging technology used to identify brain regions and functions associated with treatment outcome may be used to aid in the develop‐ ment or refinement of proxy biomarkers, such as neuropsychological functioning or serum markers, that could feasibly measure prediction and be disseminated for wide-spread applica‐ tion of personalized psychiatric care.

Here we have focused on neurobiological factors that can be measured at baseline to predict treatment. However, increased understanding of what aspects of neurobiological factors change over the course of treatment may also serve to enhance our understanding of the pathophysiology of internalizing problems and aid in identifying neurobiological factors that are likely to predict treatment outcomes. A recent review of the literature on changes with treatment concludes that a functional normalization of the fear network occurs with recovery across treatments [81]. Specifically, evidence suggests that both psychotherapy and psycho‐ pharmacology each in specific ways result in normalization of activity in the target structures (respectively, "top-down" and "bottom-up" effects). Methodologies that capitalize on considering both prediction of and change associated with treatment outcomes are needed.

Advanced techniques, such as those used in neuroimaging research, offer tremendous benefit to our society in that they provide the capability to improve our understanding of the patho‐ physiology underlying internalizing problems and may eventually offer guidance in regards to treatment selection, allowing providers to choose only those treatments that are most likely to be maximally effective for a given individual. This area of research is still developing. The concept of neural network medicine envisions a time to come when treatments will be used to target a neural network rather than simply components within the network. While personal‐ ized medicine in psychiatry is still at an early stage, "it has a very promising future" (Costa e Silva, in press).

### **Author details**

While research reviewed above employed the use of fMRI, PET, and SPECT imaging techni‐ ques, the study of predictive biomarkers of treatment outcome should be expanded with the use of other neuroimaging methods. For example, the use of spectroscopy would provide evidence of pretreatment chemical and metabolite profiles predictive of treatment outcome. Similarly, resting state fMRI methods might be particularly useful, potentially elucidating our understanding of how different patterns of functional connectivity within and between neural circuits relate to treatment outcome or treatment resistance. In addition, it is expected that future research will increasingly employ the use of multi-modal approaches in predictive treatment research, helping to identify other biological markers not capable of being assessed via neuroimaging techniques. For example, current efforts are underway to more definitively assess biological markers for treatment response across treatments in adults with MDD (CBT, duloxetine, escitalopram) using multi-modal techniques including resting fMRI, neuroendo‐ crine assessments, immune markers and measures of gene expression [64]. Additionally, neurobiological predictors of treatment response that have been identified thus far are not sufficiently strong enough nor have they been sufficiently replicated to warrant changes in clinical decision making at this juncture. Perhaps and understanding of broader brain net‐ works will be enhanced by profiling numerous brain functions and structures that, in compi‐

An exciting advance that has the potential to improve personalized care is recent work incorporating machine-learning approaches to classify groups—disease versus no disease, or responders versus non-responders. Machine learning approaches are "brain reading" or "brain decoding" methods. Instead of analyzing the brain voxel by voxel, data from groups of voxels are used to train a computer program to distinguish different classes of data (e.g., treatment responders from treatment non-responders) and provide maps which indicate the levels by which different brain regions are accurately involved in the classification [76]. In a study that analyzed grey and white matter volumes, using a support vector machine (SVM) approach, Gong and colleagues [77] showed they were able to predict response versus nonresponse based on gray matter with 70% accuracy and based on white matter with 65% accuracy. Another study that used SVM measured responses to sad faces with fMRI before CBT in 16 unmedicated depressed adults. Brain regions implicated in clinical remission included ACC, superior and middle frontal cortices, paracentral cortex, superior parietal cortex, precuneus, and cerebellum, with 71% sensitivity and 86% specificity of response prediction [78]. A third SVM study found that the pattern of brain activity during sad facial processing correctly classified patients' clinical response at baseline, prior to the initiation of treatment, at trend levels of significance [23]. SVM approaches are still new in the field and

the value of such non-traditional statistical approaches still needs to be weighed.

Practicalconstraintsmustbeconsideredasfutureeffortsaimtotranslateknowledgeofneurobio‐ logical predictors of treatment response into clinical practice. In addition to providing reliable data with high sensitivity and specificity, ideally a biomarker would be low in cost, easy to collect and simple to analyze [79]. It is possible that these approaches could be mechanized sufficiently toreducecostsandincreasefeasibilitysothatoneday,routineclinicalassessmentwillincludethe collection of data via neuroimaging technology [80]. For example, if activity in the ACC remains

lation, will more aptly predict treatment response.

292 Functional Brain Mapping and the Endeavor to Understand the Working Brain

Leah M. Jappe1\*, Bonnie Klimes-Dougan2 and Kathryn R. Cullen3

\*Address all correspondence to: japp0005@umn.edu


### **References**

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**Chapter 16**

**Mental Function and Obesity**

Nobuko Yamada-Goto, Goro Katsuura and

Additional information is available at the end of the chapter

Obesity is defined as a high body mass index (BMI) with a large amount of adiposity. A chronic excess energy intake above energy expenditure leads to abnormal or excessive fat accumulation. Normally, humans and other mammals have an extraordinary ability to match food intake to energy expenditure over long periods so that body weight and adiposity are maintained at near-constant levels. The precise mechanism of the natural course of obesity is yet unclear. After findings on the hypothalamus as the center of ener‐ gy regulation in 1940's, the central nervous system came to the forefront of attention in the pathophysiology of obesity. Recent global epidemic of obesity is one of the largest health problems in the world. Clinical studies have revealed that obesity is comorbid with several forms of mental disorder [3-5]. Epidemiological studies show that obesity is strongly related to cognitive impairment, including Alzheimer's disease and mood disor‐ der [6, 7]. Obesity is also positively correlated with several other forms of mental disorder in general population samples. These findings suggest that obesity can affect mental func‐ tion and change neural plasticity. Also, such mental disorder might cause further progres‐ sion of obesity. Moreover, there is the possibility that mental disorder acts as a trigger of the development of obesity. Understanding the bidirectional interaction of obesity and mental disorder should help prevent and treat obesity. This review is aimed at highlight‐ ing the mental functions related to obesity, from basic research including our recent

> © 2013 Yamada-Goto et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

> © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

and reproduction in any medium, provided the original work is properly cited.

Kazuwa Nakao

**1. Introduction**

works to clinical findings.

http://dx.doi.org/10.5772/56228


### **Chapter 16**

## **Mental Function and Obesity**

Nobuko Yamada-Goto, Goro Katsuura and Kazuwa Nakao

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56228

### **1. Introduction**

[74] Frank GK, Bailer UF, Henry S, Wagner A, Kaye WH. Neuroimaging studies in eating

[75] Kaye WH, Frank GK, Bailer UF, Henry SE. Neurobiology of anorexia nervosa: clini‐ cal implications of alterations of the function of serotonin and other neuronal sys‐

[76] Brammer M. The role of neuroimaging in diagnosis and personalized medicine – cur‐ rent position and likely future directions. Dialogues in Clinical Neuroscience

[77] Gong Q, Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, et al. Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage 2011

[78] Costafreda SG, Khanna A, Mourao-Miranda J, Fu CH. Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. Neurore‐

[79] Macaluso M, Drevets WC, Preskorn SH. How biomarkers will change psychiatry. Part II: Biomarker selection and potential inflammatory markers of depression. J Psy‐

[80] Carrig MM, Kolden GG, Strauman TJ. Using functional magnetic resonance imaging in psychotherapy research: a brief introduction to concepts, methods, and task selec‐

[81] Quide Y, Witteveen AB, El-Hage W, Veltman DJ, Olff M. Differences between effects of psychological versus pharmacological treatments on functional and morphological brain alterations in anxiety disorders and major depressive disorder: a systematic re‐

disorders. CNS Spectr 2004 Jul;9(7):539-548.

300 Functional Brain Mapping and the Endeavor to Understand the Working Brain

2009;11:389-396.

Apr 15;55(4):1497-1503.

port 2009 May 6;20(7):637-641.

chiatr Pract 2012 Jul;18(4):281-286.

tion. Psychother Res 2009 Jul;19(4-5):409-417.

view. Neurosci Biobehav Rev 2012 Jan;36(1):626-644.

tems. Int J Eat Disord 2005;37 Suppl:S15-9; discussion S20-1.

Obesity is defined as a high body mass index (BMI) with a large amount of adiposity. A chronic excess energy intake above energy expenditure leads to abnormal or excessive fat accumulation. Normally, humans and other mammals have an extraordinary ability to match food intake to energy expenditure over long periods so that body weight and adiposity are maintained at near-constant levels. The precise mechanism of the natural course of obesity is yet unclear. After findings on the hypothalamus as the center of ener‐ gy regulation in 1940's, the central nervous system came to the forefront of attention in the pathophysiology of obesity. Recent global epidemic of obesity is one of the largest health problems in the world. Clinical studies have revealed that obesity is comorbid with several forms of mental disorder [3-5]. Epidemiological studies show that obesity is strongly related to cognitive impairment, including Alzheimer's disease and mood disor‐ der [6, 7]. Obesity is also positively correlated with several other forms of mental disorder in general population samples. These findings suggest that obesity can affect mental func‐ tion and change neural plasticity. Also, such mental disorder might cause further progres‐ sion of obesity. Moreover, there is the possibility that mental disorder acts as a trigger of the development of obesity. Understanding the bidirectional interaction of obesity and mental disorder should help prevent and treat obesity. This review is aimed at highlight‐ ing the mental functions related to obesity, from basic research including our recent works to clinical findings.

© 2013 Yamada-Goto et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **2. Definition of obesity**

### **2.1. Definition of obesity in the world**

The International Association for the study of Obesity (IASO)/International Obesity Taskforce (IOTF) analysis (2010) estimates that approximately 1.0 billion adults are currently overweight, and a further 475 million are obese in the world today [8].

**3. Pathophysiology of obesity**

disorder is observed in obesity [3-6, 24 ].

**3.2. Clinical aspects related to psychiatry in obesity**

The BMI classification scheme for weight status is based on data obtained from large epide‐ miological studies that evaluate the relationship between BMI and mortality [17]. Epidemio‐ logical studies consistently suggested that lowest overall mortality in adults is associated with a BMI in the range of 20 to 23 kg/m2 [18]. A very high degree of obesity (BMI ≧ 35 kg/m2

likely to be linked to higher mortality rates, but the relationship between more modest degrees of being overweight and mortality is unclear [4, 18-21]. On the other hand, the positive correlation between obesity and many health problems both independently and in association with other diseases are clearly observed. In adults, the health complications associated with obesity increase linearly with increasing BMI until the age of 75 years [18, 22]. Both men and women who have a BMI ≧ 30 kg/m<sup>2</sup> are considered obese and are generally at higher risk for adverse health events than are those who are considered to be overweight. In particular, obesity is associated with the development of type 2 diabetes mellitus, coronary heart disease, an increased incidence of certain forms of cancer (colon, breast, esophageal, uterine, ovarian, kidney, and pancreatic), respiratory complications (obstructive sleep apnea), and osteoarthritis of large and small joints [23]. Also, high prevalence of cognitive impairment and mental

From the viewpoint of the endocrinologist, obesity is often comorbid with eating disorders, especially binge-eatingdisorder, which is thoughtto bepresentin 20-40% of obesepatients [25]. Many lines of evidence suggest that obesity and depression often comorbid and might be functionally related to each other[3, 26-30]. High rates of obesity among individuals with binge eating disorder, bipolar disorder, major depressive disorder, anxiety disorders, schizophre‐ nia,personalitydisorders,andotherdiagnoseswerealsoobserved[3,5,27,31].Thelinkbetween suchmentaldisorder andobesityis likelytobebidirectional:obesitycanleadtomentaldisorder and, in turn, mental disorder can be an obstacle to treatments of obesity and attaining longterm weight-loss goals, thereby contributing to weight gain [25]. Evidence also indicates that obesity negatively impacts on prognosis of many kind of illness. These relationships appear to be especially strong for women and individuals with more severe obesity (BMI ≧35 kg/m<sup>2</sup>

Associations between obesity and psychiatric illness are also documented in men but in more moderately overweight individuals [5]. Obesity is also associated with significant psychoso‐ cial impairment. Obese individuals are subject to weight-based stigmatization in a variety of settings, and generally report poorer quality of life compared with lean individuals [4, 5].

From the viewpoint of the psychiatrist, obesity is defined as eating disorder. Anorexia nervosa, bulimia nervosa, eating disorders not otherwise specified, and obesity are categorized as eating disorder according to the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV TR [32]. Most of the patients of anorexia nervosa and bulimia nervosa are women. Even with the gender specificity, eating disorders are thought to share dysregulation of common neuronal pathways with obesity [33]. Some population of obesity is characterized as mental disorder

) seems

303

Mental Function and Obesity http://dx.doi.org/10.5772/56228

) [5].

**3.1. Mortality and complications**

Being overweight or obesity are defined as having abnormal or excessive fat accumulation that presents a risk to health. The World Health Organization (WHO) defines obesity for adults based on overweight and obesity ranges determined by body mass index (BMI), a person's weight (in kilograms) divided by the square of height (in meters). An adult with a BMI under 18.5 kg/m2 is considered underweight. An adult with a BMI between 18.5 kg/m2 and 24.9 kg/m2 is considered to be in the normal range. An adult with a BMI between 25 kg/m2 and 29.9 kg/m2 is consideredoverweight.AnadultwithaBMIof30kg/m2orhigheris consideredobese.Among the obese, an adult with a BMI between 30kg/m2 and 34.9 kg/m2 is considered to be obese class I, between 35kg/m2 and 39.9 kg/m2 to be obese class II, and an adult with a BMI of 40 kg/m2 or higher to be obese class III [9]. BMI provides the most useful population-level measure of being overweight andobesity as itis the same for bothsexes andfor all ages of adults. However,WHO points out that it should be considered as a rough guide because it may not correspond to the same degree of fatness in different individuals. Moreover, it is well known that there is ethnic diversity in the physiology of obesity. The appropriateness of WHO criteria in non-Caucasian populations has been questioned. It was reported that South Asian, East Asian, and African-American developed diabetes at a higherrate, at an earlier age, and at lowerranges of BMIthan their white counterparts [10]. In 2000, *The Asia-Pacific Perspective: Redefining Obesity and Its Treatment* recommended different ranges for the Asia-Pacific regions based on risk factors and morbidities. They suggested that in Asians, the cut-offs for being overweight should be 23 kg/ m2 and obesity 25 kg/m2 , which are lower than the WHO criteria [11].

#### **2.2. Definition of obesity in East Asia**

Substantial differences in national and local environments with genetic variances produce the wide variation in obesity prevalence in the world. The prevalence of obesity in adults is lower in East Asia including Japan compared with the USA [12]. In East Asia, China, Japan, South Korea and Taiwan have their own criteria of overweight and obesity. In Japan, according to the Japan Society for the Study of Obesity 2011 (JASSO), the BMI values considered as being underweight or in the normal range are the same as the WHO criteria [13]. However, an adult with a BMI of 25 kg/m2 or higher is considered obese in Japan. Among the obese, an adult with a BMI between 25 kg/m2 and 29.9 kg/m2 is considered to be obese grade 1, between 30kg/m2 and 34.9 kg/m2 to be obese grade 2, between 35kg/m2 and 39.9 kg/m2 to be obese grade 3, and a BMI of 40 kg/m2 or higher to be obese grade 4 in Japan. An adult with a BMI of 35 kg/m2 or higher is considered to have morbid obesity in Japan. In China, an adult with a BMI of 24 kg/ m2 or higher is considered to be overweight, and an adult with a BMI of 28 kg/m2 or higher is considered to be obese [14]. In South Korea, an adult with a BMI of 25 kg/m2 or higher is considered to be obese [15]. In Taiwan, an adult with a BMI of 24 kg/m2 or higher is considered to be overweight, and an adult with a BMI of 27 kg/m2 or higher is considered to be obese [16].

### **3. Pathophysiology of obesity**

### **3.1. Mortality and complications**

**2. Definition of obesity**

18.5 kg/m2

m2

I, between 35kg/m2

and obesity 25 kg/m2

with a BMI of 25 kg/m2

and 34.9 kg/m2

a BMI between 25 kg/m2

**2.2. Definition of obesity in East Asia**

**2.1. Definition of obesity in the world**

and a further 475 million are obese in the world today [8].

302 Functional Brain Mapping and the Endeavor to Understand the Working Brain

the obese, an adult with a BMI between 30kg/m2

and 39.9 kg/m2

The International Association for the study of Obesity (IASO)/International Obesity Taskforce (IOTF) analysis (2010) estimates that approximately 1.0 billion adults are currently overweight,

Being overweight or obesity are defined as having abnormal or excessive fat accumulation that presents a risk to health. The World Health Organization (WHO) defines obesity for adults based on overweight and obesity ranges determined by body mass index (BMI), a person's weight (in kilograms) divided by the square of height (in meters). An adult with a BMI under

is considered underweight. An adult with a BMI between 18.5 kg/m2

is consideredoverweight.AnadultwithaBMIof30kg/m2orhigheris consideredobese.Among

higher to be obese class III [9]. BMI provides the most useful population-level measure of being overweight andobesity as itis the same for bothsexes andfor all ages of adults. However,WHO points out that it should be considered as a rough guide because it may not correspond to the same degree of fatness in different individuals. Moreover, it is well known that there is ethnic diversity in the physiology of obesity. The appropriateness of WHO criteria in non-Caucasian populations has been questioned. It was reported that South Asian, East Asian, and African-American developed diabetes at a higherrate, at an earlier age, and at lowerranges of BMIthan their white counterparts [10]. In 2000, *The Asia-Pacific Perspective: Redefining Obesity and Its Treatment* recommended different ranges for the Asia-Pacific regions based on risk factors and morbidities. They suggested that in Asians, the cut-offs for being overweight should be 23 kg/

, which are lower than the WHO criteria [11].

Substantial differences in national and local environments with genetic variances produce the wide variation in obesity prevalence in the world. The prevalence of obesity in adults is lower in East Asia including Japan compared with the USA [12]. In East Asia, China, Japan, South Korea and Taiwan have their own criteria of overweight and obesity. In Japan, according to the Japan Society for the Study of Obesity 2011 (JASSO), the BMI values considered as being underweight or in the normal range are the same as the WHO criteria [13]. However, an adult

a BMI of 40 kg/m2 or higher to be obese grade 4 in Japan. An adult with a BMI of 35 kg/m2

m2 or higher is considered to be overweight, and an adult with a BMI of 28 kg/m2

higher is considered to have morbid obesity in Japan. In China, an adult with a BMI of 24 kg/

considered to be obese [14]. In South Korea, an adult with a BMI of 25 kg/m2 or higher is considered to be obese [15]. In Taiwan, an adult with a BMI of 24 kg/m2 or higher is considered

and 29.9 kg/m2

to be overweight, and an adult with a BMI of 27 kg/m2

to be obese grade 2, between 35kg/m2

or higher is considered obese in Japan. Among the obese, an adult with

and 39.9 kg/m2

is considered to be obese grade 1, between 30kg/m2

or higher is considered to be obese [16].

to be obese grade 3, and

or

or higher is

and 34.9 kg/m2

to be obese class II, and an adult with a BMI of 40 kg/m2

is considered to be in the normal range. An adult with a BMI between 25 kg/m2

and 24.9 kg/m2

and 29.9 kg/m2

or

is considered to be obese class

The BMI classification scheme for weight status is based on data obtained from large epide‐ miological studies that evaluate the relationship between BMI and mortality [17]. Epidemio‐ logical studies consistently suggested that lowest overall mortality in adults is associated with a BMI in the range of 20 to 23 kg/m2 [18]. A very high degree of obesity (BMI ≧ 35 kg/m2 ) seems likely to be linked to higher mortality rates, but the relationship between more modest degrees of being overweight and mortality is unclear [4, 18-21]. On the other hand, the positive correlation between obesity and many health problems both independently and in association with other diseases are clearly observed. In adults, the health complications associated with obesity increase linearly with increasing BMI until the age of 75 years [18, 22]. Both men and women who have a BMI ≧ 30 kg/m<sup>2</sup> are considered obese and are generally at higher risk for adverse health events than are those who are considered to be overweight. In particular, obesity is associated with the development of type 2 diabetes mellitus, coronary heart disease, an increased incidence of certain forms of cancer (colon, breast, esophageal, uterine, ovarian, kidney, and pancreatic), respiratory complications (obstructive sleep apnea), and osteoarthritis of large and small joints [23]. Also, high prevalence of cognitive impairment and mental disorder is observed in obesity [3-6, 24 ].

#### **3.2. Clinical aspects related to psychiatry in obesity**

From the viewpoint of the endocrinologist, obesity is often comorbid with eating disorders, especially binge-eatingdisorder, which is thoughtto bepresentin 20-40% of obesepatients [25]. Many lines of evidence suggest that obesity and depression often comorbid and might be functionally related to each other[3, 26-30]. High rates of obesity among individuals with binge eating disorder, bipolar disorder, major depressive disorder, anxiety disorders, schizophre‐ nia,personalitydisorders,andotherdiagnoseswerealsoobserved[3,5,27,31].Thelinkbetween suchmentaldisorder andobesityis likelytobebidirectional:obesitycanleadtomentaldisorder and, in turn, mental disorder can be an obstacle to treatments of obesity and attaining longterm weight-loss goals, thereby contributing to weight gain [25]. Evidence also indicates that obesity negatively impacts on prognosis of many kind of illness. These relationships appear to be especially strong for women and individuals with more severe obesity (BMI ≧35 kg/m<sup>2</sup> ) [5]. Associations between obesity and psychiatric illness are also documented in men but in more moderately overweight individuals [5]. Obesity is also associated with significant psychoso‐ cial impairment. Obese individuals are subject to weight-based stigmatization in a variety of settings, and generally report poorer quality of life compared with lean individuals [4, 5].

From the viewpoint of the psychiatrist, obesity is defined as eating disorder. Anorexia nervosa, bulimia nervosa, eating disorders not otherwise specified, and obesity are categorized as eating disorder according to the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV TR [32]. Most of the patients of anorexia nervosa and bulimia nervosa are women. Even with the gender specificity, eating disorders are thought to share dysregulation of common neuronal pathways with obesity [33]. Some population of obesity is characterized as mental disorder with "compulsive food consumption" similar to drug addiction and suggested to be included as a mental disorder in the DSM-V [5]. The pathophysiology of anorexia nervosa draws attention as it is thought to be the opposite phenotype of obesity [Figure 1]. Functional magnetic resonance image (fMRI) study showed that brain reward circuits are more responsive to unexpected food stimuli and more sensitive in dopamine-related pathways in anorexia nervosa, but are less responsive and less sensitive in obese women [33]. Moreover, a recent fMRI study suggested that self starvation in anorexia nervosa may be driven by inappropri‐ ately assigned desire and pleasure associated with food restriction, somehow related to dependence [34]. They might perpetuate and reinforce the desire to not eat to change persistent stress, such as low self-esteem and social rejection into a positively experienced state [35]. Bulimia nervosa is another severe eating disorder characterized by the presence of episodic binge eating followed by extreme behaviors to avoid weight gain, such as self-induced vomiting, use of laxative or excessive exercise [32]. Individuals with bulimia nervosa present with fear of gaining weight, as well as food and body weight-related preoccupations, are at normal or often high-normal weight. While they are eating, they feel pleasure and arousal followed by guilt and remorse. These abnormal eating behaviors observed in anorexia nervosa and bulimia nervosa are also difficult to treat and contain life-long risk of relapse [36].

How about the personality of obesity? Psychological processes contribute to an individual's body shape. Body weight reflects our behaviors and lifestyle and contributes to the way we perceive ourselves and others. Personality traits are defined by cognitive, emotional, and behavioral patterns that are likely to contribute to unhealthy weight and difficulties with weight management. It is quite difficult to clarify personal traits, but there are many clinical studies on the personality of obesity using certain questionnaires [37-41]. Overweight indi‐ viduals are prone to depressive state, have a poor body image, are evaluated negatively by others, and are ascribed traits based on their body size [42-45]. From the Baltimore Longitu‐ dinal Study of Aging (BLSA), which is a longitudinal study of more than 50 years on a large number of people (n = 1,988), high neuroticism and low conscientiousness, which are related to difficulty with impulse control, were associated with weight fluctuations [40]. Low agree‐ ableness and impulsivity-related traits predicted a greater increase in BMI across the adult life span in the same study [40]. Personality traits are reported to be a useful tool for predicting diet-induced weight loss and management, which may offer ways to achieve appropriate

Mental Function and Obesity http://dx.doi.org/10.5772/56228 305

To date, however, there is no evidence to support a direct interaction between obesity and these personality traits. It is not clear that how these mental disorders and personality traits

Adiposity causes chronic low-grade systemic inflammation, which in conjunction with a high calorie diet may contribute to diseases associated with obesity [48-49]. A growing body of evidence implicates immune cell-mediated tissue inflammation as an important mechanism linking obesity to insulin resistance in metabolically active organs, such as the liver, skeletal muscle, and adipose tissue [48-49]. Peripheral inflammation passes through or bypasses the blood-brain barrier [50-51], and stimulation of neural afferents at the site of local peripheral inflammation induces an inflammatory reaction within the central nervous system [52-53]. The saturated free fatty acids, palmitic acids and lauric acid, have been shown to trigger inflam‐ mation in cultured macrophages [54]. Saturated long-chain fatty acids were demonstrated to activate inflammatory signaling in astrocytes [55]. Microglia, macrophage-like cells of the central nervous system that are activated by pro-inflammatory signals causing local produc‐ tion of specific interleukins and cytokines, play a pivotal role in brain inflammation [48-49, 53, 55-57]. Experimental studies in animals have confirmed neurologic vulnerability to obesity and a high-fat diet and further demonstrated that diet-induced metabolic dysfunction leads to increased brain inflammation, reactive gliosis, and vulnerability to injury, especially in the hypothalamus [49, 56, 58-59]. Hypothalamic inflammation contributes to obesity pathogenesis through the development of central leptin resistance [49, 56]. Leptin resistance is a physiolog‐ ical condition in which high concentrations of leptin neither reduce food intake nor increase energy expenditure, as observed in obese humans and a rodent model of diet-induced obesity (DIO) [60]. Leptin resistance is considered to be a central dogma for obesity [61]. Immunerelated molecules, including proinflammatory cytokines, IL-1β, TNF-α, and IL-6, altered expression levels of many genes in the hypothalamus [49, 56, 58]. Activation of both Jnk and the inhibitor of nuclear factor kappa-B kinase subunit β(IKKβ)/ nuclear factor-κB (NF-κB)

weight loss and management strategies for individuals [46-47].

are related to the natural course of obesity.

**3.3. Brain inflammation and obesity**

**Figure 1. Postulated shared mechanisms related to reward circuits of anorexia nervosa and obesity.** The sense of hunger regulated by reward circuits might be the key component of obesity and anorexia nervosa.

How about the personality of obesity? Psychological processes contribute to an individual's body shape. Body weight reflects our behaviors and lifestyle and contributes to the way we perceive ourselves and others. Personality traits are defined by cognitive, emotional, and behavioral patterns that are likely to contribute to unhealthy weight and difficulties with weight management. It is quite difficult to clarify personal traits, but there are many clinical studies on the personality of obesity using certain questionnaires [37-41]. Overweight indi‐ viduals are prone to depressive state, have a poor body image, are evaluated negatively by others, and are ascribed traits based on their body size [42-45]. From the Baltimore Longitu‐ dinal Study of Aging (BLSA), which is a longitudinal study of more than 50 years on a large number of people (n = 1,988), high neuroticism and low conscientiousness, which are related to difficulty with impulse control, were associated with weight fluctuations [40]. Low agree‐ ableness and impulsivity-related traits predicted a greater increase in BMI across the adult life span in the same study [40]. Personality traits are reported to be a useful tool for predicting diet-induced weight loss and management, which may offer ways to achieve appropriate weight loss and management strategies for individuals [46-47].

To date, however, there is no evidence to support a direct interaction between obesity and these personality traits. It is not clear that how these mental disorders and personality traits are related to the natural course of obesity.

#### **3.3. Brain inflammation and obesity**

with "compulsive food consumption" similar to drug addiction and suggested to be included as a mental disorder in the DSM-V [5]. The pathophysiology of anorexia nervosa draws attention as it is thought to be the opposite phenotype of obesity [Figure 1]. Functional magnetic resonance image (fMRI) study showed that brain reward circuits are more responsive to unexpected food stimuli and more sensitive in dopamine-related pathways in anorexia nervosa, but are less responsive and less sensitive in obese women [33]. Moreover, a recent fMRI study suggested that self starvation in anorexia nervosa may be driven by inappropri‐ ately assigned desire and pleasure associated with food restriction, somehow related to dependence [34]. They might perpetuate and reinforce the desire to not eat to change persistent stress, such as low self-esteem and social rejection into a positively experienced state [35]. Bulimia nervosa is another severe eating disorder characterized by the presence of episodic binge eating followed by extreme behaviors to avoid weight gain, such as self-induced vomiting, use of laxative or excessive exercise [32]. Individuals with bulimia nervosa present with fear of gaining weight, as well as food and body weight-related preoccupations, are at normal or often high-normal weight. While they are eating, they feel pleasure and arousal followed by guilt and remorse. These abnormal eating behaviors observed in anorexia nervosa and bulimia nervosa are also difficult to treat and contain life-long risk of relapse [36].

304 Functional Brain Mapping and the Endeavor to Understand the Working Brain

**Figure 1. Postulated shared mechanisms related to reward circuits of anorexia nervosa and obesity.** The sense

of hunger regulated by reward circuits might be the key component of obesity and anorexia nervosa.

Adiposity causes chronic low-grade systemic inflammation, which in conjunction with a high calorie diet may contribute to diseases associated with obesity [48-49]. A growing body of evidence implicates immune cell-mediated tissue inflammation as an important mechanism linking obesity to insulin resistance in metabolically active organs, such as the liver, skeletal muscle, and adipose tissue [48-49]. Peripheral inflammation passes through or bypasses the blood-brain barrier [50-51], and stimulation of neural afferents at the site of local peripheral inflammation induces an inflammatory reaction within the central nervous system [52-53]. The saturated free fatty acids, palmitic acids and lauric acid, have been shown to trigger inflam‐ mation in cultured macrophages [54]. Saturated long-chain fatty acids were demonstrated to activate inflammatory signaling in astrocytes [55]. Microglia, macrophage-like cells of the central nervous system that are activated by pro-inflammatory signals causing local produc‐ tion of specific interleukins and cytokines, play a pivotal role in brain inflammation [48-49, 53, 55-57]. Experimental studies in animals have confirmed neurologic vulnerability to obesity and a high-fat diet and further demonstrated that diet-induced metabolic dysfunction leads to increased brain inflammation, reactive gliosis, and vulnerability to injury, especially in the hypothalamus [49, 56, 58-59]. Hypothalamic inflammation contributes to obesity pathogenesis through the development of central leptin resistance [49, 56]. Leptin resistance is a physiolog‐ ical condition in which high concentrations of leptin neither reduce food intake nor increase energy expenditure, as observed in obese humans and a rodent model of diet-induced obesity (DIO) [60]. Leptin resistance is considered to be a central dogma for obesity [61]. Immunerelated molecules, including proinflammatory cytokines, IL-1β, TNF-α, and IL-6, altered expression levels of many genes in the hypothalamus [49, 56, 58]. Activation of both Jnk and the inhibitor of nuclear factor kappa-B kinase subunit β(IKKβ)/ nuclear factor-κB (NF-κB) pathway as well as induction of endoplasmic reticulum stress underlie these responses and parallel the onset of reduced hypothalamic leptin sensitivity in rodent models of DIO [56, 58]. High-fat feeding increases suppressor of cytokine signaling 3 (SOCS3) and protein tyrosine phosphatase-1B (PTP1B) in the rodent hypothalamus [56, 58, 62]. Up-regulation of SOCS3, a member of a protein family originally characterized as negative feedback regulators of inflammation, inhibits insulin and leptin signaling by direct binding to their cognate receptors and targeting insulin receptor substrate (IRS) proteins for proteasomal degaradation [58]. The PTP1B is a signal termination molecule that inhibits both leptin and insulin signaling, also thought to be involved in leptin resistance [58, 62]. Diet-induced PTP1B overexpression in multiple tissues including the hypothalamus in obesity is regulated by inflammation [62]. Recent studies with animals and humans have shown that other brain structures, such as the hippocampus and orbitofrontal cortex, are also affected [53, 57, 63-64]. These inflammatory changes induced by obesity and high-fat diet might be reversible from the results of animal studies. Resveratrol, an adenosine monophosphate-activated protein kinase (AMPK) activator and potent anti-inflammatory agent, attenuated peripheral and central inflammation in the hippocampus and improved memory deficit in mice fed a high-fat diet [57]. In another study, moderate and regular treadmill running exercise markedly decreased hypothalamic inflam‐ mation in high-fat diet fed mice [59]. Evidence of brain inflammation in human obesity has been accumulating based on biologic data and imaging studies by using MRI [46, 56].

is observed with a blunted effect of leptin in suppressing food intake and increasing energy expenditure, which is termed "leptin resistance" [61]. Based on these observations, we postulated that the development of depression associated with obesity might be due in part

Mental Function and Obesity http://dx.doi.org/10.5772/56228 307

Here we review our recent study on the central leptin action in depression associated with obesity [1]. The forced swimming test (FST) is widely accepted as a task that induces depressive behavior in depression research and has good reliability and high predictive validity for assessment of the depressive state and the detection of potential antidepressant-like activity in experimental animals. In this test, animals display "despair" behavior as observed as immobility and escape-oriented behaviors, in particular, by swimming [71-72]. Normal mice fed a control diet (CD) displayed such immobility and stress-induced despair in the FST. Subcutaneous administration of leptin significantly decreased the immobility time compared with saline treatment [Figure 2(A); 1]. Icv injection of leptin significantly decreased the immobility time of CD mice in the FST [Figure 2(B); 1]. DIO mice fed a 60% high-fat diet (HFD) for 16 weeks exhibited more depressive behavior compared with CD mice without exaggerated response of plasma corticosterone levels [Figure 2(C); 1]. Subcutaneous administration of leptin did not decrease the prolonged immobility time in DIO mice [Figure 2(D); 1]. Icv injection of leptin did not decrease the immobility time of DIO mice in the FST [1]. Moreover, in response to leptin, DIO mice did not exhibit an increase in the number of c-Fos-immunor‐ eactive cells in the hippocampus, whereas leptin administration in CD mice has a significantly increased number of c-Fos immunoreactive cells in the hippocampus [1]. To examine whether the increased immobility time of DIO mice in the FST can be restored by diet substitution from HFD to CD, the diet of the DIO mice was changed from HFD to CD for the next 3 weeks. This led to significant reductions in body weight and fat weight and to the normalization of plasma levels of glucose, insulin, and leptin [1]. The immobility time in the FST in mice now given CD was significantly decreased and identical to that of the CD mice [1]. Moreover, subcutaneous administration of leptin significantly decreased the immobility time of FST in mice switched to CD [1]. These results are compatible with a previous report that diet substitution from HFD to CD in DIO mice restores leptin sensitivity as an anorexigenic action [73]. Brain-derived neurotrophic factor (BDNF) in the hippocampus is considered to play an important role in control of the depressive state. Injection of BDNF into the hippocampus in experimental animals has antidepressant effects in the FST, and this antidepressant effect induced by BDNF is inhibited by K252a, an inhibitor of the BDNF receptor tyrosine kinase B (TrkB) [74]. Low BDNF levels are reported in the hippocampus of humans with depression [75]. These findings support the hypothesis that decreased BDNF/TrkB signaling may induce depression. In our study, the hippocampal BDNF concentrations in DIO mice were significantly decreased compared with those of CD mice [Figure 2(E); 1]. Subcutaneous administration of leptin significantly increased BDNF concentrations in the hippocampus of CD mice but not in DIO mice [Figure 2(E); 1]. In summary, as shown in Figure 2F, in the lean state, leptin helps maintain normal body weight by acting on the arcuate nucleus of the hypothalamus (ARC), and provides an antidepressant-like action via hippocampal BDNF, whereas in the obese state, impaired leptin action even with a high concentration in plasma, may lead to rodent and

to impaired leptin activity in the hippocampus.

human obesity occurring together with depression [Figure 2(F); 1].

### **4. Mental disorders of obesity**

#### **4.1. Depression and other mood disorders**

Obesity is associated with an increased risk of developing depression and a higher likelihood of current depression [3, 27-30]. Most obese individuals tend to have higher scores in depres‐ sion, the projected increase in the rates of being overweight and obesity in future years could generate a parallel increase in obesity-related depression. According to the DSM-IV, an episode of major depressive disorder can be classified clinically as depression with melancholic features and depression with atypical features. Unlike melancholic depression, which is characterized by a loss of appetite or weight, atypical depression and seasonal depression are characterized by decreased activity and increased appetite and weight. Obesity among these groups is sometimes a result of the ingestion of "palatable food", which contains high amounts of fat and sugar [65]. Also, major depression in female adolescence is linked with an increased risk of obesity in adulthood [66]. To explain this mutual relationship between obesity and depression, the focus of research has been on hormones and neuropeptides, which have been implicated in both energy regulation and cognition/mood [67]. Among them, the involvement of leptin has been the subject of much attention as it has been implicated in depression associated with obesity [1]. Leptin is reported to induce an antidepressant-like activity in the hippocampus, which is considered to be an important region for regulation of the depressive state, but not in the hypothalamus of rats [68]. Decreased plasma or CSF leptin levels were observed in major depressive disorder patient group compared with controls independent of BMI [69-70]. These findings suggested that impairment of leptin action might contribute the physiology of depression. In obese rodents and humans, a high concentration of plasma leptin is observed with a blunted effect of leptin in suppressing food intake and increasing energy expenditure, which is termed "leptin resistance" [61]. Based on these observations, we postulated that the development of depression associated with obesity might be due in part to impaired leptin activity in the hippocampus.

pathway as well as induction of endoplasmic reticulum stress underlie these responses and parallel the onset of reduced hypothalamic leptin sensitivity in rodent models of DIO [56, 58]. High-fat feeding increases suppressor of cytokine signaling 3 (SOCS3) and protein tyrosine phosphatase-1B (PTP1B) in the rodent hypothalamus [56, 58, 62]. Up-regulation of SOCS3, a member of a protein family originally characterized as negative feedback regulators of inflammation, inhibits insulin and leptin signaling by direct binding to their cognate receptors and targeting insulin receptor substrate (IRS) proteins for proteasomal degaradation [58]. The PTP1B is a signal termination molecule that inhibits both leptin and insulin signaling, also thought to be involved in leptin resistance [58, 62]. Diet-induced PTP1B overexpression in multiple tissues including the hypothalamus in obesity is regulated by inflammation [62]. Recent studies with animals and humans have shown that other brain structures, such as the hippocampus and orbitofrontal cortex, are also affected [53, 57, 63-64]. These inflammatory changes induced by obesity and high-fat diet might be reversible from the results of animal studies. Resveratrol, an adenosine monophosphate-activated protein kinase (AMPK) activator and potent anti-inflammatory agent, attenuated peripheral and central inflammation in the hippocampus and improved memory deficit in mice fed a high-fat diet [57]. In another study, moderate and regular treadmill running exercise markedly decreased hypothalamic inflam‐ mation in high-fat diet fed mice [59]. Evidence of brain inflammation in human obesity has been accumulating based on biologic data and imaging studies by using MRI [46, 56].

306 Functional Brain Mapping and the Endeavor to Understand the Working Brain

Obesity is associated with an increased risk of developing depression and a higher likelihood of current depression [3, 27-30]. Most obese individuals tend to have higher scores in depres‐ sion, the projected increase in the rates of being overweight and obesity in future years could generate a parallel increase in obesity-related depression. According to the DSM-IV, an episode of major depressive disorder can be classified clinically as depression with melancholic features and depression with atypical features. Unlike melancholic depression, which is characterized by a loss of appetite or weight, atypical depression and seasonal depression are characterized by decreased activity and increased appetite and weight. Obesity among these groups is sometimes a result of the ingestion of "palatable food", which contains high amounts of fat and sugar [65]. Also, major depression in female adolescence is linked with an increased risk of obesity in adulthood [66]. To explain this mutual relationship between obesity and depression, the focus of research has been on hormones and neuropeptides, which have been implicated in both energy regulation and cognition/mood [67]. Among them, the involvement of leptin has been the subject of much attention as it has been implicated in depression associated with obesity [1]. Leptin is reported to induce an antidepressant-like activity in the hippocampus, which is considered to be an important region for regulation of the depressive state, but not in the hypothalamus of rats [68]. Decreased plasma or CSF leptin levels were observed in major depressive disorder patient group compared with controls independent of BMI [69-70]. These findings suggested that impairment of leptin action might contribute the physiology of depression. In obese rodents and humans, a high concentration of plasma leptin

**4. Mental disorders of obesity**

**4.1. Depression and other mood disorders**

Here we review our recent study on the central leptin action in depression associated with obesity [1]. The forced swimming test (FST) is widely accepted as a task that induces depressive behavior in depression research and has good reliability and high predictive validity for assessment of the depressive state and the detection of potential antidepressant-like activity in experimental animals. In this test, animals display "despair" behavior as observed as immobility and escape-oriented behaviors, in particular, by swimming [71-72]. Normal mice fed a control diet (CD) displayed such immobility and stress-induced despair in the FST. Subcutaneous administration of leptin significantly decreased the immobility time compared with saline treatment [Figure 2(A); 1]. Icv injection of leptin significantly decreased the immobility time of CD mice in the FST [Figure 2(B); 1]. DIO mice fed a 60% high-fat diet (HFD) for 16 weeks exhibited more depressive behavior compared with CD mice without exaggerated response of plasma corticosterone levels [Figure 2(C); 1]. Subcutaneous administration of leptin did not decrease the prolonged immobility time in DIO mice [Figure 2(D); 1]. Icv injection of leptin did not decrease the immobility time of DIO mice in the FST [1]. Moreover, in response to leptin, DIO mice did not exhibit an increase in the number of c-Fos-immunor‐ eactive cells in the hippocampus, whereas leptin administration in CD mice has a significantly increased number of c-Fos immunoreactive cells in the hippocampus [1]. To examine whether the increased immobility time of DIO mice in the FST can be restored by diet substitution from HFD to CD, the diet of the DIO mice was changed from HFD to CD for the next 3 weeks. This led to significant reductions in body weight and fat weight and to the normalization of plasma levels of glucose, insulin, and leptin [1]. The immobility time in the FST in mice now given CD was significantly decreased and identical to that of the CD mice [1]. Moreover, subcutaneous administration of leptin significantly decreased the immobility time of FST in mice switched to CD [1]. These results are compatible with a previous report that diet substitution from HFD to CD in DIO mice restores leptin sensitivity as an anorexigenic action [73]. Brain-derived neurotrophic factor (BDNF) in the hippocampus is considered to play an important role in control of the depressive state. Injection of BDNF into the hippocampus in experimental animals has antidepressant effects in the FST, and this antidepressant effect induced by BDNF is inhibited by K252a, an inhibitor of the BDNF receptor tyrosine kinase B (TrkB) [74]. Low BDNF levels are reported in the hippocampus of humans with depression [75]. These findings support the hypothesis that decreased BDNF/TrkB signaling may induce depression. In our study, the hippocampal BDNF concentrations in DIO mice were significantly decreased compared with those of CD mice [Figure 2(E); 1]. Subcutaneous administration of leptin significantly increased BDNF concentrations in the hippocampus of CD mice but not in DIO mice [Figure 2(E); 1]. In summary, as shown in Figure 2F, in the lean state, leptin helps maintain normal body weight by acting on the arcuate nucleus of the hypothalamus (ARC), and provides an antidepressant-like action via hippocampal BDNF, whereas in the obese state, impaired leptin action even with a high concentration in plasma, may lead to rodent and human obesity occurring together with depression [Figure 2(F); 1].

Given the high comorbidity of metabolic disorders, such as diabetes and obesity, with depression, several lines of evidence suggest that insulin signaling in the brain is also an important regulator. Clinical investigations show the relationship between insulin resistance and depression, but the underlying mechanisms are still unclear [76-77]. Ghrelin is also play a potential role in defense against the consequences of stress, including stress-induced depression and anxiety and prevent their manifestation in experimental animals [82]. These findings suggest that both leptin and ghrelin involve in mood regulation and might have antidepressant-like effect. The target differences being treated by leptin or ghrelin in human

Mental Function and Obesity http://dx.doi.org/10.5772/56228 309

What kind of treatment is effective on depression associated with obesity? One clinical study demonstrated the efficacy of a treatment combining behavioral weight management and cognitive behavioral therapy for obese adults with depression [81]. According to systematic review and meta-analysis on intentional weight loss and changes in symptoms of depression, obese individuals in weight loss trials experienced reduction in depression symptoms [80].

Epidemiologic studies have demonstrated that the incidence of cognitive impairment is higher in obese individuals than in individuals with normal body weight [6, 24]. From the study of Anstey et al., risks of cognitive impairment appeared to be highest for those with underweight and obese BMI in midlife [81]. Increasing evidence suggests that obesity is associated with impairment of certain cognitive functions, such as executive function, attention, visuomotor skills, and memory [6, 82]. A higher prevalence of attention deficit hyperactivity disorder, Alzheimer's disease and other cognitive impairment, cortical atrophy, and white matter disease is observed in obese individuals [83-84]. The mechanisms by which obesity results in cognitive impairment, however, are uncertain. Postulated mechanisms include the effects of hyperglycemia, hyperinsulinemia, poor sleep with obstructive sleep apnea, and vascular damage to the central nervous system [7, 85]. Moreover, adiposity is thought to have a direct effect on neuronal degradation [24]. C reactive protein, as well as inflammatory markers, is increased in subjects with greater adiposity and is associated with later-life cognitive impair‐ ment [86]. White matter lesions and cerebral atrophy are more common in adults with a high BMI, and midlife measures of central obesity predict poor performance on tests measuring executive function and visuomotor skills [83-84, 87. In animal studies, chronic dietary fat intake, especially saturated fatty acid intake, contributes to deficits in hippocampus- and amygdala-dependent learning and memory in rodents with diet-induced obesity by changes in neuronal plasticity [2, 88]. Neural plasticity, long-term structural alterations of synapses, are regulated by several synaptic molecules including neurotrophic factors, such as BDNF,

and have been demonstrated to be essential for hippocampal functions [89].

In our recent study, cognitive behaviors in DIO mice in fear-conditioning test including both contextual and cued elements that preferentially depend on the hippocampus and amygdala, respectively, was significantly impaired [Figure 3(A); 2]. Fear-conditioning test is the method which assesses memory and learing by freezing behavior induced by electric foot shock.

depression are not known, yet.

This finding is compatible with our experimental data [1].

**4.2. Cognitive impairment and Alzheimer's disease**

**Figure 2. Central leptin action in depression associated with obesity** (A) Effect of subcutaneous administration of leptin (0.3, 1, 3 mg/kg) and desipramine (DMI) (7.5 mg/kg) in CD mice on immobility time in the FST. (B) Effect of intra‐ cerebroventricular administration of leptin (1 μg/2 μl per mouse) on immobility time in CD mice in the FST. (C) Depres‐ sive behavior in DIO mice in the FST. (D) Antidepressant effects of subcutaneous administration of leptin (0.3, 1, 3 mg/kg) and DMI (7.5 mg/kg) in DIO mice. (E) Effect of subcutaneous administration of leptin (3 mg/kg) in CD and DIO mice on the hippocampal BDNF concentrations. (F) The schematic diagram of normal body weight regulation and anti‐ depressant-like effect of leptin in lean, and overweight/obese and depression resulting in leptin resistance in obesity. Data points represent the mean ± SEM. Significantly different: \**p* < 0.05, \*\**p* < 0.01. CD mice: control mice given CE-2 as a control diet (CLEA Japan, Inc., Tokyo, Japan), DIO mice: diet-induced obese mice given a high-fat diet (HFD) (no. D12492; Research Diets, Inc., New Brunswick, NJ) containing 60% fat of total calories, predominantly in the form of lard.

Given the high comorbidity of metabolic disorders, such as diabetes and obesity, with depression, several lines of evidence suggest that insulin signaling in the brain is also an important regulator. Clinical investigations show the relationship between insulin resistance and depression, but the underlying mechanisms are still unclear [76-77]. Ghrelin is also play a potential role in defense against the consequences of stress, including stress-induced depression and anxiety and prevent their manifestation in experimental animals [82]. These findings suggest that both leptin and ghrelin involve in mood regulation and might have antidepressant-like effect. The target differences being treated by leptin or ghrelin in human depression are not known, yet.

What kind of treatment is effective on depression associated with obesity? One clinical study demonstrated the efficacy of a treatment combining behavioral weight management and cognitive behavioral therapy for obese adults with depression [81]. According to systematic review and meta-analysis on intentional weight loss and changes in symptoms of depression, obese individuals in weight loss trials experienced reduction in depression symptoms [80]. This finding is compatible with our experimental data [1].

### **4.2. Cognitive impairment and Alzheimer's disease**

**Figure 2. Central leptin action in depression associated with obesity** (A) Effect of subcutaneous administration of leptin (0.3, 1, 3 mg/kg) and desipramine (DMI) (7.5 mg/kg) in CD mice on immobility time in the FST. (B) Effect of intra‐ cerebroventricular administration of leptin (1 μg/2 μl per mouse) on immobility time in CD mice in the FST. (C) Depres‐ sive behavior in DIO mice in the FST. (D) Antidepressant effects of subcutaneous administration of leptin (0.3, 1, 3 mg/kg) and DMI (7.5 mg/kg) in DIO mice. (E) Effect of subcutaneous administration of leptin (3 mg/kg) in CD and DIO mice on the hippocampal BDNF concentrations. (F) The schematic diagram of normal body weight regulation and anti‐ depressant-like effect of leptin in lean, and overweight/obese and depression resulting in leptin resistance in obesity. Data points represent the mean ± SEM. Significantly different: \**p* < 0.05, \*\**p* < 0.01. CD mice: control mice given CE-2 as a control diet (CLEA Japan, Inc., Tokyo, Japan), DIO mice: diet-induced obese mice given a high-fat diet (HFD) (no. D12492; Research Diets, Inc., New Brunswick, NJ) containing 60% fat of total calories, predominantly in the form of lard.

308 Functional Brain Mapping and the Endeavor to Understand the Working Brain

Epidemiologic studies have demonstrated that the incidence of cognitive impairment is higher in obese individuals than in individuals with normal body weight [6, 24]. From the study of Anstey et al., risks of cognitive impairment appeared to be highest for those with underweight and obese BMI in midlife [81]. Increasing evidence suggests that obesity is associated with impairment of certain cognitive functions, such as executive function, attention, visuomotor skills, and memory [6, 82]. A higher prevalence of attention deficit hyperactivity disorder, Alzheimer's disease and other cognitive impairment, cortical atrophy, and white matter disease is observed in obese individuals [83-84]. The mechanisms by which obesity results in cognitive impairment, however, are uncertain. Postulated mechanisms include the effects of hyperglycemia, hyperinsulinemia, poor sleep with obstructive sleep apnea, and vascular damage to the central nervous system [7, 85]. Moreover, adiposity is thought to have a direct effect on neuronal degradation [24]. C reactive protein, as well as inflammatory markers, is increased in subjects with greater adiposity and is associated with later-life cognitive impair‐ ment [86]. White matter lesions and cerebral atrophy are more common in adults with a high BMI, and midlife measures of central obesity predict poor performance on tests measuring executive function and visuomotor skills [83-84, 87. In animal studies, chronic dietary fat intake, especially saturated fatty acid intake, contributes to deficits in hippocampus- and amygdala-dependent learning and memory in rodents with diet-induced obesity by changes in neuronal plasticity [2, 88]. Neural plasticity, long-term structural alterations of synapses, are regulated by several synaptic molecules including neurotrophic factors, such as BDNF, and have been demonstrated to be essential for hippocampal functions [89].

In our recent study, cognitive behaviors in DIO mice in fear-conditioning test including both contextual and cued elements that preferentially depend on the hippocampus and amygdala, respectively, was significantly impaired [Figure 3(A); 2]. Fear-conditioning test is the method which assesses memory and learing by freezing behavior induced by electric foot shock. Freezing was defined as the absence of all movement except for respiration. BDNF content in the cerebral cortex and hippocampus of DIO mice was significantly lower than that in CD mice [Figure 3(B); 2]. Its receptor, full-length TrkB in the amygdala of DIO mice was significantly decreased compared to that in CD mice, although not in the cerebral cortex, hippocampus and hypothalamus [Figure 3(C); 2]. By contrast, neurotrophin-3 (NT-3), which is reported to act in the opposite direction to BDNF on neurite outgrowth and neural activities, was present at significantly higher levels in the hippocampus, amygdala and hypothalamus of DIO mice than that in CD mice [90-91, Figure 3(B); 2]. Its receptor, full-length TrkC, was not significantly different between CD and DIO mice [Figure 3(C); 2].

Severallinesofelectrophysiologicalandbehavioralevidencedemonstratethatleptinandinsulin enhancehippocampal synapticplasticityandimprovelearningandmemory[7,92].Electrophy‐ siological studies in genetically obese Zucker rats with leptin-receptor deficiency demonstrat‐ ed that long-term potentiation (LTP) of the hippocampal CA1 region, which is closely related to learning and the formation of memory and is regulated by *N*-methyl-D-aspartate (NMDA) and 2-amino-3-(3-hydroxy-5-methyl-isoxazol-4-yl)propanoic acid (AMPA) receptors, is markedly impaired compared to that of lean rats [93]. Streptozotocin-treated insulin deficient rats are reported to exhibit impaired cognition in the water maze test, which is dependent on the hippocampus [94].Therefore,itis likely thatimpairment ofthe actions ofleptinorinsulinmight be attributable to cognitive deficits in obesity and diabetes mellitus [61, 95].

### **5. Dysregulation of hunger in obesity**

### **5.1. Metabolic hunger**

Food intake and energy expenditure are controlled by complex, redundant, and distributed neural systems that reflect the fundamental biologic importance of an adequate nutrient supply and energy balance. Metabolic hunger is regulated by a homeostatic metabolic status designed to preserve energy balance and maintain minimal levels of adiposity. The hypothal‐ amus and caudal brainstem play crucial roles in this homeostatic function. The hypothalamus serves to integrate nutrition and information from orexigenic and anorexigenic peptides that are sensitive to circulating leptin and other hormones [96-97]. The role of the hypothalamus in regulating food intake and body weight was established in 1940 by the classic experiments of Hetherington and Ranson [98]. Their destruction experiments demonstrated that the ventromedial hypothalamus resulted in hyperphagia and obesity [98]. Anand and Brobeck, in 1951, demonstrated that lesions of the lateral hypothalamus caused loss of feeding, inanition, and even death by starvation [99]. Thus, the concept arose of the lateral hypothalamic are serving as a "feeding center" and the ventromedial nucleus as a "satiety center" [100].

After more than 60 years since the Hetherington and Ranson experiments, much more precise mechanisms and the network between peripheral signals and the brain have been elucidated [97, 101]. Input signals such as sight, smell and taste allow the brain to decide whether or not it should engage in ingestive behavior. Once put into the mouth, foods elicit taste and mechanical sensations that send neural signals via mainly vagal afferents to the **Figure 3. Impairment of fear-conditioning responses and changes of brain neurotrophic factors in diet-induced obese mice.** (A) Fear-conditioning responses in CD (closed circles) and DIO (open circles) mice. Freezing percentages of CD and DIO mice in the contextual conditioning test were measured every minute for 5 min. Freezing percentages of CD and DIO mice in the cued conditioning test were measured every minute for 3 min. (B) Content of brain-derived neuro‐ trophic factor (BDNF) and neurotrophin-3 (NT-3) in the cerebral cortex, hippocampus, amygdala and hypothalamus in CD and DIO mice. (C) Expression of full-length TrkB and TrkC in the cerebral cortex, hippocampus, amygdala and hypo‐ thalamus in CD and DIO mice. Data points represent the mean ± SEM. Significantly different from CD mice: \* *p* < 0.05, \*\*

Mental Function and Obesity http://dx.doi.org/10.5772/56228 311

*p* < 0.01. GAPDH: glyceraldehyde3-phosphate dehydrogenase.

Freezing was defined as the absence of all movement except for respiration. BDNF content in the cerebral cortex and hippocampus of DIO mice was significantly lower than that in CD mice [Figure 3(B); 2]. Its receptor, full-length TrkB in the amygdala of DIO mice was significantly decreased compared to that in CD mice, although not in the cerebral cortex, hippocampus and hypothalamus [Figure 3(C); 2]. By contrast, neurotrophin-3 (NT-3), which is reported to act in the opposite direction to BDNF on neurite outgrowth and neural activities, was present at significantly higher levels in the hippocampus, amygdala and hypothalamus of DIO mice than that in CD mice [90-91, Figure 3(B); 2]. Its receptor, full-length TrkC, was not significantly

Severallinesofelectrophysiologicalandbehavioralevidencedemonstratethatleptinandinsulin enhancehippocampal synapticplasticityandimprovelearningandmemory[7,92].Electrophy‐ siological studies in genetically obese Zucker rats with leptin-receptor deficiency demonstrat‐ ed that long-term potentiation (LTP) of the hippocampal CA1 region, which is closely related to learning and the formation of memory and is regulated by *N*-methyl-D-aspartate (NMDA) and 2-amino-3-(3-hydroxy-5-methyl-isoxazol-4-yl)propanoic acid (AMPA) receptors, is markedly impaired compared to that of lean rats [93]. Streptozotocin-treated insulin deficient rats are reported to exhibit impaired cognition in the water maze test, which is dependent on the hippocampus [94].Therefore,itis likely thatimpairment ofthe actions ofleptinorinsulinmight

Food intake and energy expenditure are controlled by complex, redundant, and distributed neural systems that reflect the fundamental biologic importance of an adequate nutrient supply and energy balance. Metabolic hunger is regulated by a homeostatic metabolic status designed to preserve energy balance and maintain minimal levels of adiposity. The hypothal‐ amus and caudal brainstem play crucial roles in this homeostatic function. The hypothalamus serves to integrate nutrition and information from orexigenic and anorexigenic peptides that are sensitive to circulating leptin and other hormones [96-97]. The role of the hypothalamus in regulating food intake and body weight was established in 1940 by the classic experiments of Hetherington and Ranson [98]. Their destruction experiments demonstrated that the ventromedial hypothalamus resulted in hyperphagia and obesity [98]. Anand and Brobeck, in 1951, demonstrated that lesions of the lateral hypothalamus caused loss of feeding, inanition, and even death by starvation [99]. Thus, the concept arose of the lateral hypothalamic are serving as a "feeding center" and the ventromedial nucleus as a "satiety center" [100].

After more than 60 years since the Hetherington and Ranson experiments, much more precise mechanisms and the network between peripheral signals and the brain have been elucidated [97, 101]. Input signals such as sight, smell and taste allow the brain to decide whether or not it should engage in ingestive behavior. Once put into the mouth, foods elicit taste and mechanical sensations that send neural signals via mainly vagal afferents to the

be attributable to cognitive deficits in obesity and diabetes mellitus [61, 95].

different between CD and DIO mice [Figure 3(C); 2].

310 Functional Brain Mapping and the Endeavor to Understand the Working Brain

**5. Dysregulation of hunger in obesity**

**5.1. Metabolic hunger**

**Figure 3. Impairment of fear-conditioning responses and changes of brain neurotrophic factors in diet-induced obese mice.** (A) Fear-conditioning responses in CD (closed circles) and DIO (open circles) mice. Freezing percentages of CD and DIO mice in the contextual conditioning test were measured every minute for 5 min. Freezing percentages of CD and DIO mice in the cued conditioning test were measured every minute for 3 min. (B) Content of brain-derived neuro‐ trophic factor (BDNF) and neurotrophin-3 (NT-3) in the cerebral cortex, hippocampus, amygdala and hypothalamus in CD and DIO mice. (C) Expression of full-length TrkB and TrkC in the cerebral cortex, hippocampus, amygdala and hypo‐ thalamus in CD and DIO mice. Data points represent the mean ± SEM. Significantly different from CD mice: \* *p* < 0.05, \*\* *p* < 0.01. GAPDH: glyceraldehyde3-phosphate dehydrogenase.

brainstem and/or hormonal signals through the bloodstream to the brain [97]. Gut-tobrain communication is increasingly recognized as playing an important role not just in the determination of meal size but also in overall food intake [97]. Once absorbed, macronu‐ trients are partitioned into either storage or immediate metabolism in various tissues [97]. The information from peripheral tissue including the gastric tract is relayed to the brain, especially to the hypothalamus and the brainstem by hormones [leptin, insulin, amylin, peptide YY (PYY), ghrelin, glucagon-like peptide-1 (GLP-1), and cholecystokinin (CCK)] and nutrient signals [glucose, free fatty acid, and amino acid] [97, 101]. Leptin, insulin and amylin deliver long-term afferent signals, PYY, GLP-1, and CCK deliver short-term meal related afferent signals and work for satiation, and ghrelin stimulate feeding. Vagal afferent neurons, whose cell bodies lie in the nodose ganglia, relay information from enteroendo‐ crine cells of the intestinal epithelium and the enteric nervous system directly to the nucleus of the solitary tract in the brainstem [102]. During periods of hunger, the hypothalamus regulates the activity of the autonomic nervous system to promote fat release from white adipose tissue and trigger glucogenesis in the liver. These changes in peripheral nutrient levels lead to a decrease in the levels of thyroid hormones, insulin and leptin, and to an increase in the level of ghrelin and corticosteroids, which increase food-seeking behavior through their effect on the brain [101]. Through these pathways, an almost stable body weight can be maintained even under unpredictable and unstable environments.

(α-MSH), is an endogenous ligand of melanocortin 3 receptor (MC3R) and melanocortin 4 receptor (MC4R) in the brain [108]. AgRP is an inverse agonist of the brain MC3R and MC4R, completely dependent on the melanocortin receptors for its action, has an orexigenic effect on food intake and decreases energy expenditure [109]. Mutations in the MC4R in humans, the most commonly known monogenic cause of human obesity, have been associated with obesity, hyperphagia, tall -stature and hyperinsulinemia [110-113]. Common variants near MC4R were reported to influence fat mass, weight and obesity risk at the population level from genomewide association data from people of European descent [114]. Mutations in MC3R have been associated with obesity, hyper leptinemia and relative hypephagia [115]. Mutations in POMC and AgRP have been also reported in human obesity [116-118]. Mutation of leptin, which target is thought to be mainly the melanocortin circuitry in the brain, leptin receptor, and prohormone convertase-I were also reported in humans with severe early-onset obesity and intense hyperphagia [118-121]. The findings that HFD altered levels of POMC, AgRP and MC4R mRNA expression in the hypothalamus and changed the response to melanocortin agonist in experimental animals [122-123], speculate that dysregulation of melanocortin system may also

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Several lines of evidence have indicated that energy regulations are also modulated by extrahypothalamic brain areas originally related to regulation of emotion and cognition, such as the NAc, amygdala, hippocampus and cerebral cortex [124]. These findings suggest that maintaining energy homeostasis and regulating emotion and cognition share common brain regions, as well as bidirectional interaction between energy regulation and emotional/ cognitive functions. The regulation of food intake by the hypothalamus interacts with reward and motivational neurocircuity to modify eating behavior. Such a cognitive-hedonic pathway permits us to adjust our feeding behavior to environment & lifestyle, palatability, liking/ wanting/emotion, cues, availability, physical activity, and fuel availability [97]. Reward circuitry, which is mainly regulated by the midbrain dopamine system from the VTA to the NAc, is the main pathway of hedonic hunger. This system is the main pathway in drug addiction and part of the motivational system that regulates responses to natural reinforcers such as drink, sex, social interaction and food [125]. This dopamine neuron express κopioid receptors and receive projection of γ-aminobutyric acid (GABA) and dynorphin from the NAc [125]. Dopamine signaling within mesolimbic neurons mediates the willingness to engage in rewarding behaviors or "wanting", whereas the pleasure associated with a particular reward or "liking" is attributed to mesolimbic opioid action [126]. Memory and learning, mood, Top/ Down inhibition, interoception, gustatory integration, and salience attribution interact with the reward circuitry [Figure 4; 105]. Top/Down inhibition of feeding depends heavily on the prefrontal cortex, including orbitofrontal cortex and cingulate gyrus [105]. The amygdala ascribes emotional attributes including fear, together with memory and learning circuitry, and generates conditioned responses [2]. The hippocampus is also involved in emotion, memory

happen in human obesity.

and learning circuitry [2, 105].

**5.2. Hedonic hunger**

The ARC in the hypothalamus is the gateway of above hormones and signals in the brain [97, 101, 103]. From the ARC, the first-order neuronal network was observed of anorexigenic neuropeptides, proopiomelanocortin (POMC) and cocaine-amphetamine rerated transcript (CART), orexigenic neuropeptide, NPY and Agouti-related protein (AgRP) to other nuclei in the hypothalamus, the paraventricular hypothalamus (PVN), lateral hypothalamus (LH), and ventromedial hypothalamus (VMH) [97, 103]. These nuclei have a second-order neuronal network of output projection to other sites of the brain which regulate endocrine responses, autonomic responses, cognitive processing response plan, procurement actions, reward memory, aversive memory, social screen, competing behaviors, oro-and locomotor control, and autonomic control of peripheral tissue [97, 103]. Among these nucleus in the hypothala‐ mus, LH works as a relaying point, connecting the hypothalamus with mesolimbic dopamine system and higher brain functions. Melanin-concentrating hormone in the LH projects to the Nucleus accumbense (NAc) and many other brain areas including the amygdala, hippocam‐ pus, and cerebral cortex, and orexin in the LH project to the ventral tegmental area (VTA) and many other brain areas including the amygdala, hippocampus, and cerebral cortex [104]. From recent studies, first order neurons, which receive peripheral information and regulate food intake, are suspected to be present in other regions of the hypothalamus and extra-hypothal‐ amus [1, 97, 105, 106]. Many hormones and neuropeptides, which were previously thought to energy regulator, have turned to regulate other higher brain functions, too.

In human obesity, genetic predisposition is expressed mainly on the central melanocortin system. Downstream targets of the central melanocortin system are implicated in food intake, meal choice, satiety and energy expenditure [107]. POMC is a large precursor protein that is processed into a variety of smaller products, including alpha melanocyte stimulating hormone (α-MSH), is an endogenous ligand of melanocortin 3 receptor (MC3R) and melanocortin 4 receptor (MC4R) in the brain [108]. AgRP is an inverse agonist of the brain MC3R and MC4R, completely dependent on the melanocortin receptors for its action, has an orexigenic effect on food intake and decreases energy expenditure [109]. Mutations in the MC4R in humans, the most commonly known monogenic cause of human obesity, have been associated with obesity, hyperphagia, tall -stature and hyperinsulinemia [110-113]. Common variants near MC4R were reported to influence fat mass, weight and obesity risk at the population level from genomewide association data from people of European descent [114]. Mutations in MC3R have been associated with obesity, hyper leptinemia and relative hypephagia [115]. Mutations in POMC and AgRP have been also reported in human obesity [116-118]. Mutation of leptin, which target is thought to be mainly the melanocortin circuitry in the brain, leptin receptor, and prohormone convertase-I were also reported in humans with severe early-onset obesity and intense hyperphagia [118-121]. The findings that HFD altered levels of POMC, AgRP and MC4R mRNA expression in the hypothalamus and changed the response to melanocortin agonist in experimental animals [122-123], speculate that dysregulation of melanocortin system may also happen in human obesity.

#### **5.2. Hedonic hunger**

brainstem and/or hormonal signals through the bloodstream to the brain [97]. Gut-tobrain communication is increasingly recognized as playing an important role not just in the determination of meal size but also in overall food intake [97]. Once absorbed, macronu‐ trients are partitioned into either storage or immediate metabolism in various tissues [97]. The information from peripheral tissue including the gastric tract is relayed to the brain, especially to the hypothalamus and the brainstem by hormones [leptin, insulin, amylin, peptide YY (PYY), ghrelin, glucagon-like peptide-1 (GLP-1), and cholecystokinin (CCK)] and nutrient signals [glucose, free fatty acid, and amino acid] [97, 101]. Leptin, insulin and amylin deliver long-term afferent signals, PYY, GLP-1, and CCK deliver short-term meal related afferent signals and work for satiation, and ghrelin stimulate feeding. Vagal afferent neurons, whose cell bodies lie in the nodose ganglia, relay information from enteroendo‐ crine cells of the intestinal epithelium and the enteric nervous system directly to the nucleus of the solitary tract in the brainstem [102]. During periods of hunger, the hypothalamus regulates the activity of the autonomic nervous system to promote fat release from white adipose tissue and trigger glucogenesis in the liver. These changes in peripheral nutrient levels lead to a decrease in the levels of thyroid hormones, insulin and leptin, and to an increase in the level of ghrelin and corticosteroids, which increase food-seeking behavior through their effect on the brain [101]. Through these pathways, an almost stable body

312 Functional Brain Mapping and the Endeavor to Understand the Working Brain

weight can be maintained even under unpredictable and unstable environments.

energy regulator, have turned to regulate other higher brain functions, too.

In human obesity, genetic predisposition is expressed mainly on the central melanocortin system. Downstream targets of the central melanocortin system are implicated in food intake, meal choice, satiety and energy expenditure [107]. POMC is a large precursor protein that is processed into a variety of smaller products, including alpha melanocyte stimulating hormone

The ARC in the hypothalamus is the gateway of above hormones and signals in the brain [97, 101, 103]. From the ARC, the first-order neuronal network was observed of anorexigenic neuropeptides, proopiomelanocortin (POMC) and cocaine-amphetamine rerated transcript (CART), orexigenic neuropeptide, NPY and Agouti-related protein (AgRP) to other nuclei in the hypothalamus, the paraventricular hypothalamus (PVN), lateral hypothalamus (LH), and ventromedial hypothalamus (VMH) [97, 103]. These nuclei have a second-order neuronal network of output projection to other sites of the brain which regulate endocrine responses, autonomic responses, cognitive processing response plan, procurement actions, reward memory, aversive memory, social screen, competing behaviors, oro-and locomotor control, and autonomic control of peripheral tissue [97, 103]. Among these nucleus in the hypothala‐ mus, LH works as a relaying point, connecting the hypothalamus with mesolimbic dopamine system and higher brain functions. Melanin-concentrating hormone in the LH projects to the Nucleus accumbense (NAc) and many other brain areas including the amygdala, hippocam‐ pus, and cerebral cortex, and orexin in the LH project to the ventral tegmental area (VTA) and many other brain areas including the amygdala, hippocampus, and cerebral cortex [104]. From recent studies, first order neurons, which receive peripheral information and regulate food intake, are suspected to be present in other regions of the hypothalamus and extra-hypothal‐ amus [1, 97, 105, 106]. Many hormones and neuropeptides, which were previously thought to Several lines of evidence have indicated that energy regulations are also modulated by extrahypothalamic brain areas originally related to regulation of emotion and cognition, such as the NAc, amygdala, hippocampus and cerebral cortex [124]. These findings suggest that maintaining energy homeostasis and regulating emotion and cognition share common brain regions, as well as bidirectional interaction between energy regulation and emotional/ cognitive functions. The regulation of food intake by the hypothalamus interacts with reward and motivational neurocircuity to modify eating behavior. Such a cognitive-hedonic pathway permits us to adjust our feeding behavior to environment & lifestyle, palatability, liking/ wanting/emotion, cues, availability, physical activity, and fuel availability [97]. Reward circuitry, which is mainly regulated by the midbrain dopamine system from the VTA to the NAc, is the main pathway of hedonic hunger. This system is the main pathway in drug addiction and part of the motivational system that regulates responses to natural reinforcers such as drink, sex, social interaction and food [125]. This dopamine neuron express κopioid receptors and receive projection of γ-aminobutyric acid (GABA) and dynorphin from the NAc [125]. Dopamine signaling within mesolimbic neurons mediates the willingness to engage in rewarding behaviors or "wanting", whereas the pleasure associated with a particular reward or "liking" is attributed to mesolimbic opioid action [126]. Memory and learning, mood, Top/ Down inhibition, interoception, gustatory integration, and salience attribution interact with the reward circuitry [Figure 4; 105]. Top/Down inhibition of feeding depends heavily on the prefrontal cortex, including orbitofrontal cortex and cingulate gyrus [105]. The amygdala ascribes emotional attributes including fear, together with memory and learning circuitry, and generates conditioned responses [2]. The hippocampus is also involved in emotion, memory and learning circuitry [2, 105].

questionnaires include the "3Cs" of addiction, compulsive use, attempts to cut down, contin‐ ued use despite consequences, among others [127]. The most common symptoms were (1) persistent desire or repeated unsuccessful attempts to cut down, (2) continued use despite problems, and (3) much time spent to obtain food, eat, or recover from eating [127]. Meule et al reported that prevalence of food addiction diagnoses differed between weight classes such that overweight and obese participants had higher prevalence than normal weight participants [Figure 6; 128]. These "compulsive food consumption" is difficult to modify, and even if weight loss is achieved, the neural plasticity "fixed" by palatable food leads individuals to crave palatable food and thus substantially regain weight. "Fear of hunger" which accelerates "hedonic eating of palatable food" might cause compulsive food consumption in obesity [35]. Moreover, a weakened Top/Down inhibition signal for food cravings and inadequate sensing of ingested nutrients resulting in hyperphagia of obesity has been detected in fMRI studies [105]. Also, from the finding that obese patients have been shown to have decreased D2 receptor level in striatum by positron emission tomography (PET) imaging, obesity has been described as a reward deficiency syndrome, where deficiency of dopamine signaling results in compensatory over eating [105, 125]. fMRI studies demonstrated that obese patients have an increased "motivation" or "wanting" for food intake, actual food intake is associated with decreased "liking" [130]. It is not known that these functional changes are the results of obesity

Mental Function and Obesity http://dx.doi.org/10.5772/56228 315

**Figure 5. Hypothesis of obesity as an analogy of drug addiction.** Addictive drugs are both rewarding and reinforc‐ ing. Repeated use of addictive drugs produces multiple changes in the brain that may lead to addiction. Withdrawal occurs when drug-taking stops. Withdrawal symptoms drive one to reuse the drug. Excessive consumption of hyper‐ palatable foods might parallel to drug addiction. Repeated taking of palatable food produces multiple changes in the brain that may lead to obesity. After weight loss was achieved in obese patients, they usually regain their weight.

or the cause of obesity.

**Figure 4. Schematic diagram potential interactions between metabolic hunger and hedonic hunger which reg‐ ulate food intake.** Food intake is controlled by complex neural system that reflects the fundamental biological impor‐ tance of adequate nutrient supply and balance. Metabolic hunger regulated by homeostatic metabolic status designed to preserve energy balance and protect minimal levels adiposity. The hypothalamus plays crucial roles in the metabolic hunger. Reward circuit which is mainly regulated by the midbrain dopamine system from the VTA to NAc, is the main pathway of hedonic hunger. Memory and learning and mood interact with reward circuits. Circulating sig‐ nals of energy availability, leptin, ghrelin, glucose, and insulin are thought to regulate food intake mainly via the hypo‐ thalamus, but recent studies show that they also regulate food intake via many extra-hypothalamic regions. VTA: ventral tegmental area, NAc: nucleus accumbense.

Chronic excessive consumption of palatable foods predisposes some individuals to obesity via an increased likelihood and reinforcement of overeating. Excessive activity of hedonic hunger in obesity might lead to the ingestion of more food, independent of metabolic hunger. Several recent models have emphasized the role of the dysregulation of hedonic hunger in the development and maintenance of obesity. Such "compulsive food consumption" was recently explained by an analogy to drug addiction as previously described [Figure 5]. Drug addiction is defined as the loss of control over drug use, or the compulsive seeking and taking of drugs despite adverse consequences [125]. Once formed, an addiction can be a life-long condition in which individuals show intense drug craving and increased risk for relapse after years and even decades of abstinence [125]. This means that addiction involves extremely stable changes in the brain that are responsible for these long-lived behavioral abnormalities [125]. The hypothesis of obesity treating as an analogy of drug addiction is supported by evidence for a food addiction diagnosis according to the Yale Food Addiction Scales [127-129] and fMRI in humans [92]. There are several questionnaires for the assessment of food addiction. Such questionnaires include the "3Cs" of addiction, compulsive use, attempts to cut down, contin‐ ued use despite consequences, among others [127]. The most common symptoms were (1) persistent desire or repeated unsuccessful attempts to cut down, (2) continued use despite problems, and (3) much time spent to obtain food, eat, or recover from eating [127]. Meule et al reported that prevalence of food addiction diagnoses differed between weight classes such that overweight and obese participants had higher prevalence than normal weight participants [Figure 6; 128]. These "compulsive food consumption" is difficult to modify, and even if weight loss is achieved, the neural plasticity "fixed" by palatable food leads individuals to crave palatable food and thus substantially regain weight. "Fear of hunger" which accelerates "hedonic eating of palatable food" might cause compulsive food consumption in obesity [35]. Moreover, a weakened Top/Down inhibition signal for food cravings and inadequate sensing of ingested nutrients resulting in hyperphagia of obesity has been detected in fMRI studies [105]. Also, from the finding that obese patients have been shown to have decreased D2 receptor level in striatum by positron emission tomography (PET) imaging, obesity has been described as a reward deficiency syndrome, where deficiency of dopamine signaling results in compensatory over eating [105, 125]. fMRI studies demonstrated that obese patients have an increased "motivation" or "wanting" for food intake, actual food intake is associated with decreased "liking" [130]. It is not known that these functional changes are the results of obesity or the cause of obesity.

**Figure 4. Schematic diagram potential interactions between metabolic hunger and hedonic hunger which reg‐ ulate food intake.** Food intake is controlled by complex neural system that reflects the fundamental biological impor‐ tance of adequate nutrient supply and balance. Metabolic hunger regulated by homeostatic metabolic status designed to preserve energy balance and protect minimal levels adiposity. The hypothalamus plays crucial roles in the metabolic hunger. Reward circuit which is mainly regulated by the midbrain dopamine system from the VTA to NAc, is the main pathway of hedonic hunger. Memory and learning and mood interact with reward circuits. Circulating sig‐ nals of energy availability, leptin, ghrelin, glucose, and insulin are thought to regulate food intake mainly via the hypo‐ thalamus, but recent studies show that they also regulate food intake via many extra-hypothalamic regions. VTA:

Chronic excessive consumption of palatable foods predisposes some individuals to obesity via an increased likelihood and reinforcement of overeating. Excessive activity of hedonic hunger in obesity might lead to the ingestion of more food, independent of metabolic hunger. Several recent models have emphasized the role of the dysregulation of hedonic hunger in the development and maintenance of obesity. Such "compulsive food consumption" was recently explained by an analogy to drug addiction as previously described [Figure 5]. Drug addiction is defined as the loss of control over drug use, or the compulsive seeking and taking of drugs despite adverse consequences [125]. Once formed, an addiction can be a life-long condition in which individuals show intense drug craving and increased risk for relapse after years and even decades of abstinence [125]. This means that addiction involves extremely stable changes in the brain that are responsible for these long-lived behavioral abnormalities [125]. The hypothesis of obesity treating as an analogy of drug addiction is supported by evidence for a food addiction diagnosis according to the Yale Food Addiction Scales [127-129] and fMRI in humans [92]. There are several questionnaires for the assessment of food addiction. Such

ventral tegmental area, NAc: nucleus accumbense.

314 Functional Brain Mapping and the Endeavor to Understand the Working Brain

**Figure 5. Hypothesis of obesity as an analogy of drug addiction.** Addictive drugs are both rewarding and reinforc‐ ing. Repeated use of addictive drugs produces multiple changes in the brain that may lead to addiction. Withdrawal occurs when drug-taking stops. Withdrawal symptoms drive one to reuse the drug. Excessive consumption of hyper‐ palatable foods might parallel to drug addiction. Repeated taking of palatable food produces multiple changes in the brain that may lead to obesity. After weight loss was achieved in obese patients, they usually regain their weight.

increases rewarding properties of food while diminishing satiety, a combination that potently

Mental Function and Obesity http://dx.doi.org/10.5772/56228 317

Ghrelin is recognized as the only known orexigenic peptide hormone and synthesized mainly by a distinct group of endocrine cells located within the gastric oxyntic mucosa [136]. The mechanisms by which ghrelin promotes food intake are multifaceted and include not only stimulating intake of food via homeostatic mechanisms but also enhancing the rewarding properties of pleasurable food [139-140]. Ghrelin shifts food preference toward palatable sweet and fatty food [139]. Ghrelin can directly affect dopaminergic VTA neuronal activity and increase motivational aspect of reward [139]. Intra-VTA administration of ghrelin modulates intake of freely available regular chow, food preference, motivated food reward behavior, and increases body weight [139]. Orexin signaling is required in these ghrelin's action on food reward [140]. Ghrelin also reported to mediates stress-induced

Insulin is produced by pancreatic β-cells, controls plasma glucose levels, increases in propor‐ tion to fat mass, consequently relay information about peripheral fat stores to central effectors in the hypothalamus to modify food intake and energy expenditure. Neurons in the ARC of the hypothalamus express insulin receptors and regulate energy homeostasis. The receptors for insulin are also present in brain reward circuitry, which are thought to be projected from LH in the hypothalamus [126, 142-143]. Insulin works as satiety hormone similar to leptin, and also attenuates food reward similar to leptin, substantially suppresses food intake [126, 144]. Insulin signaling and dopamine signaling via dopamine 2 receptor (D2R) work in tandem to regulate dopamine transporter plasma membrane expression and function [145]. Brain insulin resistance which is often accompanied with obesity also exists in brain regions regulating appetite and reward [146]. Dysregulation of brain insulin signaling might alter dopamine reward pathways resulting in changing motivation for food since these pathways are insulin sensitive [145]. Jastreboff et al demonstrated a fMRI study that in obese individuals, food craving, insulin, and HOMA-IR levels correlated positively with neural activity in corticolim‐ bic-striatal brain regions including the striatum, insula, and thalamus during favorite-food and stress cues [147]. These findings strongly suggest that the relationship between insulin resistance and food craving in obese individuals mediated by activity in motivation-reward regions [147]. Centrally administered insulin also diminishes both sucrose preference and the effect of fasting to increase the rewarding properties of electrical pleasure-center stimulation

GLP-1 is secreted from the L cells of intestinal tract in response to nutrients. GLP-1 is also produced in the NTS of the brainstem, resulting in the activation of GLP-1receptor (GLP-1R) expressed on both dendritic terminals of vagal afferent fibers innervating the organs of the

increases food intake [124].

food-reward behavior in mice [141].

similar to leptin [136-137].

*5.3.4. GLP-1*

*5.3.2. Ghrelin*

*5.3.3. Insulin*

**Figure 6. Percentage of food addiction diagnosis according to the Yale Food Addiction Scale as a function of weight category.** This graph is made from the data of Table 1. (Meule, A., Medical Hypotheses, 2012;79(4):508-511) [128]. These are aggregated data from three studies done by Meule, A. et al, in which the Yale Food Addiction Scale was used and BMI was assessed. Participants were classified in weight categories according to the guidelines of WHO. The prevalence of food addiction diagnosis was significantly increased in overweight/obese individuals compared with normal weight individuals.

Stress is reported to modulate the reward circuit. Stress affects feeding behavior in humans in both directions, with some individuals increasing their food intake while others eat less [131]. An overall increased consumption of caloric dense and highly palatable foods following stress compared to non-stressed controls is reported, independent of stress-induced hyperphagia or hypephagia [131]. Susceptibility to stress and stress-induced hyperphagia are observed in obese individuals [132]. Depression, other mood disorders, and cognitive impairment also affect the feeding behavior of obese individuals. Direct interaction between stress-mediated mood and reward circuits in rodent was reported by Vialou et al [133].

#### **5.3. Hormones and neurotransmitter in metabolic hunger and hedonic hunger**

#### *5.3.1. Leptin*

Leptin is one of the most important adipocyte-derived hormones and circulate in proportion to body fat mass, enter the brain, and act on neurocircuit that govern food intake and energy expenditure [124]. The long form of the leptin receptor (Ob-Rb) expresses in numerous regions including the hypothalamus, VTA, and NAc. Through both direct and indirect actions, leptin diminishes perception of food reward (the palatability of food) while enhancing the response to satiety signals generated during food consumption that inhibit feeding and lead to meal termination [124]. Administrations of leptin in the VTA directly regulate mesolimbic dopamine system [134-135]. Centrally administered leptin diminishes both sucrose preference and the effect of fasting to increase the rewarding properties of electrical pleasure-center stimulation [136-137]. The effect of weight loss to lower leptin levels and hence to reduce leptin signaling increases rewarding properties of food while diminishing satiety, a combination that potently increases food intake [124].

#### *5.3.2. Ghrelin*

Ghrelin is recognized as the only known orexigenic peptide hormone and synthesized mainly by a distinct group of endocrine cells located within the gastric oxyntic mucosa [136]. The mechanisms by which ghrelin promotes food intake are multifaceted and include not only stimulating intake of food via homeostatic mechanisms but also enhancing the rewarding properties of pleasurable food [139-140]. Ghrelin shifts food preference toward palatable sweet and fatty food [139]. Ghrelin can directly affect dopaminergic VTA neuronal activity and increase motivational aspect of reward [139]. Intra-VTA administration of ghrelin modulates intake of freely available regular chow, food preference, motivated food reward behavior, and increases body weight [139]. Orexin signaling is required in these ghrelin's action on food reward [140]. Ghrelin also reported to mediates stress-induced food-reward behavior in mice [141].

#### *5.3.3. Insulin*

**Figure 6. Percentage of food addiction diagnosis according to the Yale Food Addiction Scale as a function of weight category.** This graph is made from the data of Table 1. (Meule, A., Medical Hypotheses, 2012;79(4):508-511) [128]. These are aggregated data from three studies done by Meule, A. et al, in which the Yale Food Addiction Scale was used and BMI was assessed. Participants were classified in weight categories according to the guidelines of WHO. The prevalence of food addiction diagnosis was significantly increased in overweight/obese individuals compared

Stress is reported to modulate the reward circuit. Stress affects feeding behavior in humans in both directions, with some individuals increasing their food intake while others eat less [131]. An overall increased consumption of caloric dense and highly palatable foods following stress compared to non-stressed controls is reported, independent of stress-induced hyperphagia or hypephagia [131]. Susceptibility to stress and stress-induced hyperphagia are observed in obese individuals [132]. Depression, other mood disorders, and cognitive impairment also affect the feeding behavior of obese individuals. Direct interaction between stress-mediated

Leptin is one of the most important adipocyte-derived hormones and circulate in proportion to body fat mass, enter the brain, and act on neurocircuit that govern food intake and energy expenditure [124]. The long form of the leptin receptor (Ob-Rb) expresses in numerous regions including the hypothalamus, VTA, and NAc. Through both direct and indirect actions, leptin diminishes perception of food reward (the palatability of food) while enhancing the response to satiety signals generated during food consumption that inhibit feeding and lead to meal termination [124]. Administrations of leptin in the VTA directly regulate mesolimbic dopamine system [134-135]. Centrally administered leptin diminishes both sucrose preference and the effect of fasting to increase the rewarding properties of electrical pleasure-center stimulation [136-137]. The effect of weight loss to lower leptin levels and hence to reduce leptin signaling

mood and reward circuits in rodent was reported by Vialou et al [133].

316 Functional Brain Mapping and the Endeavor to Understand the Working Brain

**5.3. Hormones and neurotransmitter in metabolic hunger and hedonic hunger**

with normal weight individuals.

*5.3.1. Leptin*

Insulin is produced by pancreatic β-cells, controls plasma glucose levels, increases in propor‐ tion to fat mass, consequently relay information about peripheral fat stores to central effectors in the hypothalamus to modify food intake and energy expenditure. Neurons in the ARC of the hypothalamus express insulin receptors and regulate energy homeostasis. The receptors for insulin are also present in brain reward circuitry, which are thought to be projected from LH in the hypothalamus [126, 142-143]. Insulin works as satiety hormone similar to leptin, and also attenuates food reward similar to leptin, substantially suppresses food intake [126, 144]. Insulin signaling and dopamine signaling via dopamine 2 receptor (D2R) work in tandem to regulate dopamine transporter plasma membrane expression and function [145]. Brain insulin resistance which is often accompanied with obesity also exists in brain regions regulating appetite and reward [146]. Dysregulation of brain insulin signaling might alter dopamine reward pathways resulting in changing motivation for food since these pathways are insulin sensitive [145]. Jastreboff et al demonstrated a fMRI study that in obese individuals, food craving, insulin, and HOMA-IR levels correlated positively with neural activity in corticolim‐ bic-striatal brain regions including the striatum, insula, and thalamus during favorite-food and stress cues [147]. These findings strongly suggest that the relationship between insulin resistance and food craving in obese individuals mediated by activity in motivation-reward regions [147]. Centrally administered insulin also diminishes both sucrose preference and the effect of fasting to increase the rewarding properties of electrical pleasure-center stimulation similar to leptin [136-137].

#### *5.3.4. GLP-1*

GLP-1 is secreted from the L cells of intestinal tract in response to nutrients. GLP-1 is also produced in the NTS of the brainstem, resulting in the activation of GLP-1receptor (GLP-1R) expressed on both dendritic terminals of vagal afferent fibers innervating the organs of the peritoneal cavity, as well as the pancreaticβ-cells [148-149]. Activation of the GLP-1R promotes glucose dependent insulin secretion, slowing of gastric emptying, and glucose-dependent inhibition of glucagon secretion, together facilitating the rapid clearance, storage, and nor‐ malization of blood glucose [149]. GLP-1 has anorectic effects, and regulation of short and longterm food intake and body weight [148]. GLP-1Rs are expressed especially in the NTS and in the hypothalamic nuclei [155]. GLP-1 neurons in the NTS are characterized to project to the PVN and the DMH in the hypothalamus [150]. Peripheral GLP-1 regulates long-term energy balance interacting with leptin [150]. Central GLP-1 is a critical downstream mediator of leptin action [155]. Cells in both the VTA and the NAc clearly express the GLP-1R [147-148]. They receive GLP-1-positive fibers which are likely coming from the NTS and potentially contribute to the regulation of reward behavior [151-152]. Peripheral and central administration of a longacting GLP-1 receptor agonists, liraglutide and Exendin-4, suppress food reward and motiva‐ tion in rats, resulting in reduce appetite and body weight [148].

regain [156]. Moderate-intensity physical activity between 150 and 250 min/week alone will provide only modest weight loss and prevent weight gain. Greater amount of physical activity over 250 min/week have been associated with clinically significant weight loss [156]. Resistance training increase fat-free mass and increase loss of fat mass but does not enhance weight loss [156]. For weight control, multiple short bouts of activity, as brief as 10 min, throughout the day are as effective as 1 long bout (>40 min) [157]. Behavior therapy is a set of principles and techniques for helping obese individuals modify eating, activity, and thinking habits that contribute to their excess weight [156, 158]. Setting specific goal and self-monitoring are the most important components of behavioral treatment [156]. Self-monitoring contains, daily monitoring of food intake and physical activity by use of paper or electronic diaries, weekly monitoring of weight, structured curriculum of behavior change, and regular feedback from an interventionist [156]. Frequent self-monitoring is a consistent predictor of both short- and long-term weight losses [159]. Frequency and duration of treatment contact is another important component of lifestyle modification [156]. Among many lifestyle modification programs, the LEARN program developed by Dr. Kelly Brownell of Yale University, is often recommended by health professionals in the USA and UK. It is designed to produce permanent change in five areas of life (lifestyle, exercise, attitudes, relationships and nutrition) for living and maintaining a healthy body weight. It also includes a master list of various lifestyle techniques, personal charts and forms, a fast food guide, calorie guide, a Weight Loss Readi‐

Mental Function and Obesity http://dx.doi.org/10.5772/56228 319

Cooper et al developed a new CBT for obese women based on the evidence of their CBT for bulimia nervosa [112]. It targets patients' overeating, low level of activity, and focuses on processes hypothesized to hinder successful weight maintenance [160]. CBT was successful at achieving change in participants' acceptance of body shape. The great majority of the partici‐ pants lost weight while taking CBT but within the observation period regain it. It seems that sustained behavior change in people with obesity is remarkably difficult to achieve, unlike the situation with people with eating disorders. However, CBT is still valuable for its validity and

After Orlistat (pancreatic lipase inhibitor) was approved 13 years ago, on 1999, safety concerns or lack of efficacy have doomed past applications. Fenfluramine, serotonin re-uptake inhibitor and increases the release of serotonin, is withdrawn by US Food and Drug Administration (FDA) with side effects of hallucinations, valvulopathy, pulmonary hypertension. Sibutra‐ mine, noradrenalin and serotonin re-uptake inhibitor is withdrawn by FDA with side effects of increased risk of heart attack and stroke in patients with high risk of cardiovascular disorders. Rimonabant (SR141716; CB1 receptor antagonist/inverse agonist) is withdrawn by European Medicines Agency with side effects of risk of suicide [101]. In this year, Belviq (lorcaserin; selective 5-HT2C receptor agonist, [161-163]) and Qsymia (a combination drug of phentermine; a sympathomimetic amine anorectic, and topiramate extended-release; an

ness Test, and a comprehensive index [153, 158].

safety and there is still room for improvement.

*5.4.2. Cognitive behavioral therapy*

*5.4.3. Medication*

#### **5.4. Weight management strategy in obesity**

On the basis of the observation that a 10% loss of body weight frequently produces substantial beneficial change in health risk factors, even in the very obese, a 10% weight loss has been offered as a clinical definition of weight loss success [153]. Long-term success in voluntary weight loss is clearly possible but quite difficult. Lifestyle modification sometimes with cognitive behavioral therapy (CBT) is essential part of the strategy of weight management in obesity. Medications and bariatric surgery are supportive therapy. Recent new findings from successful bariatric surgery might help us to get new strategy.

### *5.4.1. Lifestyle modification*

The health and psychosocial benefits of sustained weight loss are well established, even tough, these natural incentives are not sufficient to motivate long-term behavior change [153]. There is a lifestyle patterns associated with lean or obese population. From the study done by University of Minnesota, 5 meaningful lifestyle and weight control behavioral factors were identified [154]. Current lesser BMI and greater % weight loss are associated with good habits: regularity of meals, not watching television with meal or snuck, having intentional strategies for weight control, not eating away from home, greater fruit and vegetable intake [154]. These results strongly suggested that lifestyle modification is essential for weight loss and weight control. Lifestyle modification includes 3 primary components: diet, exercise, and behavior therapy. About dietary interventions, there are 4 well-known diets: low-carbohydrate, low-fat (including balanced calorie-restricted), Mediterranean, and low-glycemic load regimens [155]. Numerous trials have examined these diets. In summary, caloric restriction rather than macronutrient composition is the key determinant of weight loss [155]. The optimal dietary macronutrient composition for improving specific comorbid complication will be determined by further researches. About exercise, physical activity is associated with improvements in body composition and metabolic conditions independent of weight loss. For weight loss, physical activity alone is of limited benefit and much better with diet restrictions. However, physical activity appears to be critical for long-term weight loss and prevention of weight regain [156]. Moderate-intensity physical activity between 150 and 250 min/week alone will provide only modest weight loss and prevent weight gain. Greater amount of physical activity over 250 min/week have been associated with clinically significant weight loss [156]. Resistance training increase fat-free mass and increase loss of fat mass but does not enhance weight loss [156]. For weight control, multiple short bouts of activity, as brief as 10 min, throughout the day are as effective as 1 long bout (>40 min) [157]. Behavior therapy is a set of principles and techniques for helping obese individuals modify eating, activity, and thinking habits that contribute to their excess weight [156, 158]. Setting specific goal and self-monitoring are the most important components of behavioral treatment [156]. Self-monitoring contains, daily monitoring of food intake and physical activity by use of paper or electronic diaries, weekly monitoring of weight, structured curriculum of behavior change, and regular feedback from an interventionist [156]. Frequent self-monitoring is a consistent predictor of both short- and long-term weight losses [159]. Frequency and duration of treatment contact is another important component of lifestyle modification [156]. Among many lifestyle modification programs, the LEARN program developed by Dr. Kelly Brownell of Yale University, is often recommended by health professionals in the USA and UK. It is designed to produce permanent change in five areas of life (lifestyle, exercise, attitudes, relationships and nutrition) for living and maintaining a healthy body weight. It also includes a master list of various lifestyle techniques, personal charts and forms, a fast food guide, calorie guide, a Weight Loss Readi‐ ness Test, and a comprehensive index [153, 158].

#### *5.4.2. Cognitive behavioral therapy*

Cooper et al developed a new CBT for obese women based on the evidence of their CBT for bulimia nervosa [112]. It targets patients' overeating, low level of activity, and focuses on processes hypothesized to hinder successful weight maintenance [160]. CBT was successful at achieving change in participants' acceptance of body shape. The great majority of the partici‐ pants lost weight while taking CBT but within the observation period regain it. It seems that sustained behavior change in people with obesity is remarkably difficult to achieve, unlike the situation with people with eating disorders. However, CBT is still valuable for its validity and safety and there is still room for improvement.

### *5.4.3. Medication*

peritoneal cavity, as well as the pancreaticβ-cells [148-149]. Activation of the GLP-1R promotes glucose dependent insulin secretion, slowing of gastric emptying, and glucose-dependent inhibition of glucagon secretion, together facilitating the rapid clearance, storage, and nor‐ malization of blood glucose [149]. GLP-1 has anorectic effects, and regulation of short and longterm food intake and body weight [148]. GLP-1Rs are expressed especially in the NTS and in the hypothalamic nuclei [155]. GLP-1 neurons in the NTS are characterized to project to the PVN and the DMH in the hypothalamus [150]. Peripheral GLP-1 regulates long-term energy balance interacting with leptin [150]. Central GLP-1 is a critical downstream mediator of leptin action [155]. Cells in both the VTA and the NAc clearly express the GLP-1R [147-148]. They receive GLP-1-positive fibers which are likely coming from the NTS and potentially contribute to the regulation of reward behavior [151-152]. Peripheral and central administration of a longacting GLP-1 receptor agonists, liraglutide and Exendin-4, suppress food reward and motiva‐

On the basis of the observation that a 10% loss of body weight frequently produces substantial beneficial change in health risk factors, even in the very obese, a 10% weight loss has been offered as a clinical definition of weight loss success [153]. Long-term success in voluntary weight loss is clearly possible but quite difficult. Lifestyle modification sometimes with cognitive behavioral therapy (CBT) is essential part of the strategy of weight management in obesity. Medications and bariatric surgery are supportive therapy. Recent new findings from

The health and psychosocial benefits of sustained weight loss are well established, even tough, these natural incentives are not sufficient to motivate long-term behavior change [153]. There is a lifestyle patterns associated with lean or obese population. From the study done by University of Minnesota, 5 meaningful lifestyle and weight control behavioral factors were identified [154]. Current lesser BMI and greater % weight loss are associated with good habits: regularity of meals, not watching television with meal or snuck, having intentional strategies for weight control, not eating away from home, greater fruit and vegetable intake [154]. These results strongly suggested that lifestyle modification is essential for weight loss and weight control. Lifestyle modification includes 3 primary components: diet, exercise, and behavior therapy. About dietary interventions, there are 4 well-known diets: low-carbohydrate, low-fat (including balanced calorie-restricted), Mediterranean, and low-glycemic load regimens [155]. Numerous trials have examined these diets. In summary, caloric restriction rather than macronutrient composition is the key determinant of weight loss [155]. The optimal dietary macronutrient composition for improving specific comorbid complication will be determined by further researches. About exercise, physical activity is associated with improvements in body composition and metabolic conditions independent of weight loss. For weight loss, physical activity alone is of limited benefit and much better with diet restrictions. However, physical activity appears to be critical for long-term weight loss and prevention of weight

tion in rats, resulting in reduce appetite and body weight [148].

318 Functional Brain Mapping and the Endeavor to Understand the Working Brain

successful bariatric surgery might help us to get new strategy.

**5.4. Weight management strategy in obesity**

*5.4.1. Lifestyle modification*

After Orlistat (pancreatic lipase inhibitor) was approved 13 years ago, on 1999, safety concerns or lack of efficacy have doomed past applications. Fenfluramine, serotonin re-uptake inhibitor and increases the release of serotonin, is withdrawn by US Food and Drug Administration (FDA) with side effects of hallucinations, valvulopathy, pulmonary hypertension. Sibutra‐ mine, noradrenalin and serotonin re-uptake inhibitor is withdrawn by FDA with side effects of increased risk of heart attack and stroke in patients with high risk of cardiovascular disorders. Rimonabant (SR141716; CB1 receptor antagonist/inverse agonist) is withdrawn by European Medicines Agency with side effects of risk of suicide [101]. In this year, Belviq (lorcaserin; selective 5-HT2C receptor agonist, [161-163]) and Qsymia (a combination drug of phentermine; a sympathomimetic amine anorectic, and topiramate extended-release; an antiepileptic drug, [164-166]) were approved by FDA as new weight-loss drugs. Contrave, a combination of two well-established drugs, naltrexone and bupropion, in a sustained release formulation (SR), is also under-consideration [167]. The average body weight loss is around 10%, which is not so large even with instructed diet and exercise, and they are effective only while taking them. Orlistat 30-360 mg/day can reduce nearly 10% of body weight from baseline compared with 5–6% of those in the placebo-treated groups [168]. Belviq in conjunction with a lifestyle modification program can reduce body weight from baseline, –2.7%, –4.6%, –5.6% for placebo, 10mg BID, and 10 mg QD, respectively [161]. Qsymia, controlled-release phen‐ termine/topiramate, in conjunction with a lifestyle modification program reduced body weight from baseline, –1.8%, –9.3%, and –10.5% for placebo, 7.5 mg phentermine/46 mg controlled release topiramate, and 15 mg phentermine/92 mg controlled release topiramate, respectively [164]. Contrave can reduce body weight from baseline, –1.3%, –5.0%, and –6.1% for placebo, 16 mg naltrexone plus 360 mg bupropion, and 32 mg naltrexone plus 360 mg bupropion, respectively [167].

Metabolic & Bariatric Surgery (ASMBS) Guidelines reported weight loss as percentage of excess body weight after bariatric surgery are, gastric banding; 29-87% for 1-2 follow-up years, 45-72% for 3-6 follow-up years, 14-60% for 7-10 follow-up years, Roux-en gastric bypass; 48-85% for 1-2 follow-up years, 53-77% for 3-6 follow-up years, 25-68% for 7-10 follow-up years, sleeve gastrectomy; 33-58% for 1-2 follow-up years, 66% for 3-6 follow-up years [173]. Selected criteria for bariatric surgery are certified by AACE/TOS/ASMBS Guidelines [173]. Patients with uncontrolled, severe psychiatric illness are excluded. As already discussed above, psychiatric and personality disorders are frequent in obese patients, particularly in morbidly obese patients before bariatric surgery. The procedure needs comprehension of risks, benefits, expected outcomes, alternatives, and lifestyle changes required with bariatric surgery. A psychological assessment is surely required before proposing such intervention. Literature reviews and numerous empirical studies have described significant improvements in psycho‐ social functioning after bariatric surgery [174-178]. Patients typically report decreases in symptoms of anxiety and depression and significant improvements in health-related quality of life [179-183]. Patients also typically report improvements in body image as well as marital and sexual functioning [184-186]. On the other hand, a negative psychological response to bariatric surgery also has been reported [29, 187-188]. For some patients, improvements in psychosocial status dissipate 2-3 years postoperatively [196, 197]. Other studies have docu‐ mented suicides postoperatively [189-190]. Postoperative eating behavior is also documented. Some patients struggle to adhere to the recommended postoperative eating plan [173]. Among psychological factors improving after surgery, eating disorders have inconsistently been reported to disappear or not, consecutively to bariatric surgery [178, 192-194]. Bariatric surgery may lead to a physical impossibility of consuming unusually large amounts of food as required by binge eating disorders diagnosis criteria. However, loss of control on eating or grazing (frequently eating relatively small amounts of food) can appear or re-appear after surgery [178]. For that reason, eating behavior should not only be screened before, but also periodically after surgery [195]. Psychological factors assessed in patients before surgery did not have an impact on weight loss 2 years after surgery [178]. Increased caloric consumption above patients' postoperative caloric demands may contribute to suboptimal weight loss or even weight regain, which may begin as early as the second postoperative year [187, 190, 196-197]. To maintain long-term weight reduction after surgery, combination of the programs focusing on lifestyle modification as for non-bariatric obese patients is important [178, 195]. The changes in energy intake and energy expenditure after bariatric surgery may be affected by alternations in gut and adipocyte hormones [130, 198]. The reduced appetite seen after bariatric surgery has been attributed to changes in gut hormones, such as PYY, ghrelin, and GLP-1 [130]. But it is not clear how these hormonal changes affecting on mental status and the substantial outcome of weight control. A decrease in preference for both of sweet taste and high calorie foods has been demonstrated in animal models. The effect of bariatric surgery on the hedonic system in humans has been consistent with decreased activation of the hedonic system being demon‐ strated by fMRI and decreased preference for intake of high energy foods also being observed post-surgery [130]. The effect of bariatric surgery on dopamine signaling, which is involved in the hedonic system, is still not clear. Various studies utilizing questionnaires have demon‐ strated increased satiety and decreased hunger after bariatric surgery [130]. Understanding of

Mental Function and Obesity http://dx.doi.org/10.5772/56228 321

Besides Orlistat, most pharmacotherapies for obesity have been to target pathways that promote satiety. Dietrich and Horvath raised the interesting hypothesis that hunger promotes a healthier and longer life, and compounds that target satiety pathways will ultimately promote the homeostatic mechanisms that are related to metabolic overload and therefore chronic disorders [101]. Also, it seems almost impossible to alter only feeding behavior and energy expenditure without affecting on many other brain functions. New targets of antiobesity drugs are needed with much safety and efficacy. Recently, from the observation of type 2 diabetes treated by GLP-1 analogs, liraglutide and Exendin-4, which reduce appetite and body weight, has drawn attention as anti-obesity drug. A randomised, double-blind, placebocontrolled study of liraglutide showed that treatment with liraglutide, in addition to an energydeficit diet and exercise program, led to a sustained, clinically relevant, dose-dependent weight loss that was significantly greater than that with placebo and orlistat [169]. In this study, 76% of individuals treated with high-dose liraglutide, 3.0 mg/day, lost more than 5% weight, and almost 30% of individuals treated with liraglutide 3.0 mg/day lost more than 10% weight after 20 weeks of treatment [169]. Further study on the same patients group done by the same group, high-dose liraglutide (2.4/3.0 mg/day) with a diet and exercise program was successfully sustained weight loss for 2 years [170]. Moreover, Simmons et al reported that Exendin-4 resulted in considerable reduction of body weight in a patient with severe hypothalamic obesity from hypothalamic germ cell tumor [171]

#### *5.4.4. Surgery*

On the other hand, use of bariatric surgery for severe obesity has increased dramatically. The most common operations are adjustable gastric banding, Roux-en-Y gastric bypass and sleevegastrectomy. Bariatric surgery demonstrated significant and durable weight loss as well as improvement in obesity-related comorbities [172]. Although, there is no large, adequately powered, long-term randomized controlled trials of clinical efficacy and safety of bariatric surgery compared with standard care, diet and exercise, yet. The American Association of Clinical Endocrinologists (AACE)/ The Obesity Society (TOS)/ the American Society for

Metabolic & Bariatric Surgery (ASMBS) Guidelines reported weight loss as percentage of excess body weight after bariatric surgery are, gastric banding; 29-87% for 1-2 follow-up years, 45-72% for 3-6 follow-up years, 14-60% for 7-10 follow-up years, Roux-en gastric bypass; 48-85% for 1-2 follow-up years, 53-77% for 3-6 follow-up years, 25-68% for 7-10 follow-up years, sleeve gastrectomy; 33-58% for 1-2 follow-up years, 66% for 3-6 follow-up years [173]. Selected criteria for bariatric surgery are certified by AACE/TOS/ASMBS Guidelines [173]. Patients with uncontrolled, severe psychiatric illness are excluded. As already discussed above, psychiatric and personality disorders are frequent in obese patients, particularly in morbidly obese patients before bariatric surgery. The procedure needs comprehension of risks, benefits, expected outcomes, alternatives, and lifestyle changes required with bariatric surgery. A psychological assessment is surely required before proposing such intervention. Literature reviews and numerous empirical studies have described significant improvements in psycho‐ social functioning after bariatric surgery [174-178]. Patients typically report decreases in symptoms of anxiety and depression and significant improvements in health-related quality of life [179-183]. Patients also typically report improvements in body image as well as marital and sexual functioning [184-186]. On the other hand, a negative psychological response to bariatric surgery also has been reported [29, 187-188]. For some patients, improvements in psychosocial status dissipate 2-3 years postoperatively [196, 197]. Other studies have docu‐ mented suicides postoperatively [189-190]. Postoperative eating behavior is also documented. Some patients struggle to adhere to the recommended postoperative eating plan [173]. Among psychological factors improving after surgery, eating disorders have inconsistently been reported to disappear or not, consecutively to bariatric surgery [178, 192-194]. Bariatric surgery may lead to a physical impossibility of consuming unusually large amounts of food as required by binge eating disorders diagnosis criteria. However, loss of control on eating or grazing (frequently eating relatively small amounts of food) can appear or re-appear after surgery [178]. For that reason, eating behavior should not only be screened before, but also periodically after surgery [195]. Psychological factors assessed in patients before surgery did not have an impact on weight loss 2 years after surgery [178]. Increased caloric consumption above patients' postoperative caloric demands may contribute to suboptimal weight loss or even weight regain, which may begin as early as the second postoperative year [187, 190, 196-197]. To maintain long-term weight reduction after surgery, combination of the programs focusing on lifestyle modification as for non-bariatric obese patients is important [178, 195]. The changes in energy intake and energy expenditure after bariatric surgery may be affected by alternations in gut and adipocyte hormones [130, 198]. The reduced appetite seen after bariatric surgery has been attributed to changes in gut hormones, such as PYY, ghrelin, and GLP-1 [130]. But it is not clear how these hormonal changes affecting on mental status and the substantial outcome of weight control. A decrease in preference for both of sweet taste and high calorie foods has been demonstrated in animal models. The effect of bariatric surgery on the hedonic system in humans has been consistent with decreased activation of the hedonic system being demon‐ strated by fMRI and decreased preference for intake of high energy foods also being observed post-surgery [130]. The effect of bariatric surgery on dopamine signaling, which is involved in the hedonic system, is still not clear. Various studies utilizing questionnaires have demon‐ strated increased satiety and decreased hunger after bariatric surgery [130]. Understanding of

antiepileptic drug, [164-166]) were approved by FDA as new weight-loss drugs. Contrave, a combination of two well-established drugs, naltrexone and bupropion, in a sustained release formulation (SR), is also under-consideration [167]. The average body weight loss is around 10%, which is not so large even with instructed diet and exercise, and they are effective only while taking them. Orlistat 30-360 mg/day can reduce nearly 10% of body weight from baseline compared with 5–6% of those in the placebo-treated groups [168]. Belviq in conjunction with a lifestyle modification program can reduce body weight from baseline, –2.7%, –4.6%, –5.6% for placebo, 10mg BID, and 10 mg QD, respectively [161]. Qsymia, controlled-release phen‐ termine/topiramate, in conjunction with a lifestyle modification program reduced body weight from baseline, –1.8%, –9.3%, and –10.5% for placebo, 7.5 mg phentermine/46 mg controlled release topiramate, and 15 mg phentermine/92 mg controlled release topiramate, respectively [164]. Contrave can reduce body weight from baseline, –1.3%, –5.0%, and –6.1% for placebo, 16 mg naltrexone plus 360 mg bupropion, and 32 mg naltrexone plus 360 mg bupropion,

320 Functional Brain Mapping and the Endeavor to Understand the Working Brain

Besides Orlistat, most pharmacotherapies for obesity have been to target pathways that promote satiety. Dietrich and Horvath raised the interesting hypothesis that hunger promotes a healthier and longer life, and compounds that target satiety pathways will ultimately promote the homeostatic mechanisms that are related to metabolic overload and therefore chronic disorders [101]. Also, it seems almost impossible to alter only feeding behavior and energy expenditure without affecting on many other brain functions. New targets of antiobesity drugs are needed with much safety and efficacy. Recently, from the observation of type 2 diabetes treated by GLP-1 analogs, liraglutide and Exendin-4, which reduce appetite and body weight, has drawn attention as anti-obesity drug. A randomised, double-blind, placebocontrolled study of liraglutide showed that treatment with liraglutide, in addition to an energydeficit diet and exercise program, led to a sustained, clinically relevant, dose-dependent weight loss that was significantly greater than that with placebo and orlistat [169]. In this study, 76% of individuals treated with high-dose liraglutide, 3.0 mg/day, lost more than 5% weight, and almost 30% of individuals treated with liraglutide 3.0 mg/day lost more than 10% weight after 20 weeks of treatment [169]. Further study on the same patients group done by the same group, high-dose liraglutide (2.4/3.0 mg/day) with a diet and exercise program was successfully sustained weight loss for 2 years [170]. Moreover, Simmons et al reported that Exendin-4 resulted in considerable reduction of body weight in a patient with severe hypothalamic

On the other hand, use of bariatric surgery for severe obesity has increased dramatically. The most common operations are adjustable gastric banding, Roux-en-Y gastric bypass and sleevegastrectomy. Bariatric surgery demonstrated significant and durable weight loss as well as improvement in obesity-related comorbities [172]. Although, there is no large, adequately powered, long-term randomized controlled trials of clinical efficacy and safety of bariatric surgery compared with standard care, diet and exercise, yet. The American Association of Clinical Endocrinologists (AACE)/ The Obesity Society (TOS)/ the American Society for

respectively [167].

*5.4.4. Surgery*

obesity from hypothalamic germ cell tumor [171]

the precise physiology of bariatric surgery could pave the way for the design of newer therapies to combat the epidemic of obesity [199].

**References**

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### **6. Conclusion and future perspectives**

Mental disorder is a critical dimension of obesity. It causes obesity, affects the development of obesity, and results of obesity. It varies among individuals, and does not simply parallel BMI. Evidence suggests a pathophysiologic relevance between obesity and mental disorder. We hypothesize that there is also common vulnerability towards metabolic dysregulation and mental disorder [Figure 7]. Although clinical findings continue to be accumulated, the precise mechanisms remain unclear. A better understanding of how mental function is modulated in the development of obesity, weight reduction, and weight regain should contribute to the development of effective treatments for obesity. In our laboratory, we are going to obtain new findings of "hunger" from animal experiments, which will promote new strategy for treatment of obesity and mental disorder complicated with obesity.

**Figure 7. Schematic mutual interaction of obesity and mental disorder.** The prevalence of cognitive impairment, schizophrenia, depression, and eating disorder increases in obesity. The prevalence of metabolic dysregulation, such as insulin resistance, hypertension, and dyslipidemia, in other words, metabolic syndrome and obesity are often co‐ morbid in mental disorder. These findings speculate that there are mutual interaction between obesity and mental disorder, common vulnerability and treatment possibility towards obesity and mental disorder.

### **Author details**

Nobuko Yamada-Goto\* , Goro Katsuura and Kazuwa Nakao

\*Address all correspondence to: nobukito@kuhp.kyoto-u.ac.jp

Department of Medicine and Clinical Science, Kyoto University Graduate School of Medi‐ cine, Shogoin Kawahara-cho, Sakyo-ku, Kyoto, Japan

### **References**

the precise physiology of bariatric surgery could pave the way for the design of newer therapies

Mental disorder is a critical dimension of obesity. It causes obesity, affects the development of obesity, and results of obesity. It varies among individuals, and does not simply parallel BMI. Evidence suggests a pathophysiologic relevance between obesity and mental disorder. We hypothesize that there is also common vulnerability towards metabolic dysregulation and mental disorder [Figure 7]. Although clinical findings continue to be accumulated, the precise mechanisms remain unclear. A better understanding of how mental function is modulated in the development of obesity, weight reduction, and weight regain should contribute to the development of effective treatments for obesity. In our laboratory, we are going to obtain new findings of "hunger" from animal experiments, which will promote new strategy for treatment

**Figure 7. Schematic mutual interaction of obesity and mental disorder.** The prevalence of cognitive impairment, schizophrenia, depression, and eating disorder increases in obesity. The prevalence of metabolic dysregulation, such as insulin resistance, hypertension, and dyslipidemia, in other words, metabolic syndrome and obesity are often co‐ morbid in mental disorder. These findings speculate that there are mutual interaction between obesity and mental

disorder, common vulnerability and treatment possibility towards obesity and mental disorder.

\*Address all correspondence to: nobukito@kuhp.kyoto-u.ac.jp

cine, Shogoin Kawahara-cho, Sakyo-ku, Kyoto, Japan

, Goro Katsuura and Kazuwa Nakao

Department of Medicine and Clinical Science, Kyoto University Graduate School of Medi‐

**Author details**

Nobuko Yamada-Goto\*

to combat the epidemic of obesity [199].

**6. Conclusion and future perspectives**

322 Functional Brain Mapping and the Endeavor to Understand the Working Brain

of obesity and mental disorder complicated with obesity.


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[196] Sjöström L, Lindroos AK, Peltonen M, Torgerson J, Bouchard C, Carlsson B, Dahlg‐ ren S, Larsson B, Narbro K, Sjöström CD, Sullivan M, Wedel H; Swedish Obese Sub‐ jects Study Scientific Group. Lifestyle, diabetes, and cardiovascular risk factors 10 years after bariatric surgery. N Engl J Med. 2004;351(26):2683-2693. http://dx.doi.org/

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**Section 3**

**Experimental and Clinical Applications of**

**Functional Neuroimaging**

**Experimental and Clinical Applications of Functional Neuroimaging**

**Chapter 17**

**Surgical Resection of Tumors Infiltrating Left Insula and**

Tumors involving the insular lobe and perisylvian opercula of the dominant hemisphere are frequently managed conservatively regardless of their nature and clinical evolution, even if impending infiltration of nearby eloquent areas endangers their function. Our and other authors' experience (Duffau 2009, Duffau et al, 2000; 2001; 2006; 2009; Lang et al, 2001; Kim et al, 2002; Moshel et al, 2008; Saito et al, 2010; Sanai et al, 2010; Signorelli et al, 2010; 2011; Simon et al, 2009; Skrap et al, 2012; Yasargil et al, 1992; Wu et al, 2011; Zentner et al, 1996) demonstrate that wide surgical resection of these lesions are nonetheless feasible since tumor burden often displaces eloquent sites at the tumor boundaries (Duffau 2000; Duffau et al, 2000; 2001; 2006; 2009; Signorelli et al, 2010; 2011) and compensatory areas take over the lost function of infiltrated nervous tissue. However, accurate anatomic and functional knowledge of the sylvian fissure and structures located nearby is essential to perform any surgical act in this area, in order to decrease the risks of postoperative permanent deficits (Duffau 2009; Duffau et al, 2009; Moshel et al, 2009; Signorelli et al, 2010; 2011). Here we report our recent experience with tumors infiltrating left insula and perisylvian opercula and point out technical details helpful in guiding surgery through this region, with the purpose of locating and respecting

> © 2013 Signorelli et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Perisylvian Opercula — Utility of Anatomic Landmarks**

**Implemented by Intraoperative Functional Brain**

Francesco Signorelli, Domenico Chirchiglia, Rodolfo Maduri, Giuseppe Barbagallo and

Additional information is available at the end of the chapter

neural and vascular structures and eloquent sites.

**Mapping**

Jacques Guyotat

**1. Introduction**

http://dx.doi.org/10.5772/56568

**Chapter 17**

**Surgical Resection of Tumors Infiltrating Left Insula and Perisylvian Opercula — Utility of Anatomic Landmarks Implemented by Intraoperative Functional Brain Mapping**

Francesco Signorelli, Domenico Chirchiglia, Rodolfo Maduri, Giuseppe Barbagallo and Jacques Guyotat

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56568

**1. Introduction**

Tumors involving the insular lobe and perisylvian opercula of the dominant hemisphere are frequently managed conservatively regardless of their nature and clinical evolution, even if impending infiltration of nearby eloquent areas endangers their function. Our and other authors' experience (Duffau 2009, Duffau et al, 2000; 2001; 2006; 2009; Lang et al, 2001; Kim et al, 2002; Moshel et al, 2008; Saito et al, 2010; Sanai et al, 2010; Signorelli et al, 2010; 2011; Simon et al, 2009; Skrap et al, 2012; Yasargil et al, 1992; Wu et al, 2011; Zentner et al, 1996) demonstrate that wide surgical resection of these lesions are nonetheless feasible since tumor burden often displaces eloquent sites at the tumor boundaries (Duffau 2000; Duffau et al, 2000; 2001; 2006; 2009; Signorelli et al, 2010; 2011) and compensatory areas take over the lost function of infiltrated nervous tissue. However, accurate anatomic and functional knowledge of the sylvian fissure and structures located nearby is essential to perform any surgical act in this area, in order to decrease the risks of postoperative permanent deficits (Duffau 2009; Duffau et al, 2009; Moshel et al, 2009; Signorelli et al, 2010; 2011). Here we report our recent experience with tumors infiltrating left insula and perisylvian opercula and point out technical details helpful in guiding surgery through this region, with the purpose of locating and respecting neural and vascular structures and eloquent sites.

© 2013 Signorelli et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **2. Patients and methods**

Our series includes 5 patients harboring a high grade and 10 patients harboring a low grade tumor involving left insula and perisylvian opercula, operated on between 2007 and 2011 at two institutions: the Neurosurgical Department at the Hôpital Neurologique et Neurochirur‐ gical "Pierre Wertheimer" in Lyon, France, and the Neurosurgical Department of the Univer‐ sity Hospital of Catanzaro, Italy. They were 8 males and 7 females (mean age 50.1 years) who presented with phasic troubles in 8 cases and seizures in all cases. Preoperative antiepileptic treatment was effective in all patients but one, although 3 other patients presented with more than 1 seizure/month. Aphasia was completely regressive in four patients, all LGG, and partially regressive in one HGG patient after administration of antiedema therapy and seizure control, while in 3 other HGG cases it was progressive at a thorough preoperative neuropsy‐ chologic evaluation which comprised Montreal-Toulouse and Boston tests (Dordain et al, 1983) repeated at 1-month. They were nonetheless judged to be good candidates for, and keen and motivated to undergo intraoperative language mapping.

electrode with tips 5 mm apart, which delivered biphasic square-wave pulses (1 ms per phase) with a frequency of 60 pulses per second. Cortical stimulation was started at 1 mA and the optimal current level for stimulation was set equal to that provoking segmental movements on the contralateral upper limb or face. The effective current intensity varied from 1 mA to 6 mA. Language tasks included counting, verbal and auditory naming (auditory task was used when testing anterior temporal lobe sites). Moreover, reading tasks were added when testing parietal or posterior temporal opercula. Neuronavigation was used for all patients for defining tumor boundaries and anatomic relationships with neural and vascular structures. Cranioto‐ my was planned to include the whole perisylvian area from pars orbitalis of the third frontal gyrus to the postcentral sulcus, in order to expose the anterior (vallecula) and middle part (insular fossa) of the sylvian fissure, exposing also the superior temporal gyrus (T1). After performing ESM aimed at locating cortical language and sensorimotor areas, the superficial part of the lesion, which constantly infiltrated one or more of frontal, parietal and temporal opercula, was removed as to gain easy access to the depth of sylvian fissure, which was opened up to the postcentral sulcus with no need of retractors. In all our cases the tumor displaced M2 branches centrifugally, indicating to the surgeon the site on the insular surface where to start tumor debulking, after accomplishment of ESM in search of possible language areas. The removal of insular gyri, when not harboring language areas, was conducted medially up to the putamen, generally visible under the microscope as a gray, compact tissue with white strips located at the center of insula (Yasargyl et al, 1992), which we never found infiltrated in case of low grade tumors. However, while pushing medially tumor removal, we alternated surgical resection to subcortical stimulation starting at a distance of 2 cm laterally to the posterior limb of the internal capsule, as seen on neuronavigation, in order to identify and preserve subcort‐ ical motor pathways (Duffau 2009; Signorelli et al, 2010; 2011; Simon et al, 2009;). Subcortical stimulation is especially useful when pushing tumor resection above superior insular sulcus, where pyramidal fibers coursing through corona radiata are more superficial and anatomic landmarks to them lack. High attention was paid when pushing resection below the lenticular nucleus, at the level of the inferior limiting sulcus, where sublenticular fibers of the posterior limb of the internal capsule contain, in a forward-backward direction, the auditory and the optic radiations (Signorelli et al, 2010). At the level of the anterior part of the external capsule subcortical stimulation allowed the identification of the inferior occipito-frontal fasciculus inducing semantic paraphasias (Duffau 2009), which delimited the deep boundaries of tumor resection anteriorly. The temporal part of the same fasciculus marked the boundaries of the resection at the level of the temporal stem, preventing to open the temporal horn of the ventricle (Duffau 2009; Duffau et a, 2009). Of utmost importance is the recognition of the vascular anatomy. Short branches from MCA to the infiltrated insula can be interrupted because they supply the tumor, paying attention not to avulse them from the main vessel at the origin, which can lead to a lesion of the parent vessel wall. However, long perforators, supplying corona radiata, have to be respected to avoid ischemic injury to functional white matter (Duffau 2009; Lang et al, 2001; Moshel et al, 2008; Signorelli et al, 2010; 2011). During removal of limen insulae high attention has to be paid to lenticulostriate arteries, which originates mostly from the medial or superior aspect of MCA 6 mm or less around bifurcation and sometimes from

Surgical Resection of Tumors Infiltrating Left Insula and Perisylvian…

http://dx.doi.org/10.5772/56568

347

Motor deficit was a presenting symptom in two patients. Moreover, in all HGG patients there were symptoms of intracranial hypertension (ICHT). ICHT had an acute onset in one patient which presented to our department with an intratumoral hemorrhage. This last patient displayed a right sensorimotor deficit and a right homonymous hemianopia. Surgical indica‐ tion was established in lesions with a MRI appearance of LGG in two cases because of clinical and/or radiological tumor progression and in the other eight cases at the time of diagnosis.. All patients were right handed according to the Edimburgh Handedness Inventory (Oldfield, 1971). Gadolinium-enhanced T1-, T2- and FLAIR-weighted images revealed in all cases the infiltration of left insula. The tumor involved also fronto-parietal and temporal opercula in 9 cases, while frontal and temporal opercula or just parietal or temporal operculum were infiltrated respectively in three, two and one case. Moreover, the tumor infiltrated other paralimbic structures (i.e. fronto-orbital and/or temporo-polar areas) in four cases and limbic structures in two cases. In order to elucidate the relationships of the tumor with the vascular tree of left middle cerebral artery (MCA), in particular with lenticulostriate arteries, left carotid angiography was obtained for two patient. The other 13 patients underwent angio-CT scan and/or MRI angiography. The most lateral lenticulostiate branch was shown in 3 out of 15 cases originating from the post-bifurcation tract of M1, no more than 6 mm distal from the major bifurcation, while in the other cases it originated before or at the level of the MCA bifurcation but never from M2, in accordance with other author's experience (Moshel et al, 2008). Particular attention was also paid to the venograms, to determine the course of the superficial sylvian veins, which can hinder a wide dissection of the sylvian fissure, although generally sylvian fissure was approached subpially.

#### **2.1. Surgical procedure**

All patients underwent awake craniotomy using electrical stimulation mapping (ESM) of sensorimotor and language pathways, whose technique was described in detail elsewhere (Signorelli et al, 2010; 2011). Briefly, we applied a bipolar cortico-subcortical stimulation by an electrode with tips 5 mm apart, which delivered biphasic square-wave pulses (1 ms per phase) with a frequency of 60 pulses per second. Cortical stimulation was started at 1 mA and the optimal current level for stimulation was set equal to that provoking segmental movements on the contralateral upper limb or face. The effective current intensity varied from 1 mA to 6 mA. Language tasks included counting, verbal and auditory naming (auditory task was used when testing anterior temporal lobe sites). Moreover, reading tasks were added when testing parietal or posterior temporal opercula. Neuronavigation was used for all patients for defining tumor boundaries and anatomic relationships with neural and vascular structures. Cranioto‐ my was planned to include the whole perisylvian area from pars orbitalis of the third frontal gyrus to the postcentral sulcus, in order to expose the anterior (vallecula) and middle part (insular fossa) of the sylvian fissure, exposing also the superior temporal gyrus (T1). After performing ESM aimed at locating cortical language and sensorimotor areas, the superficial part of the lesion, which constantly infiltrated one or more of frontal, parietal and temporal opercula, was removed as to gain easy access to the depth of sylvian fissure, which was opened up to the postcentral sulcus with no need of retractors. In all our cases the tumor displaced M2 branches centrifugally, indicating to the surgeon the site on the insular surface where to start tumor debulking, after accomplishment of ESM in search of possible language areas. The removal of insular gyri, when not harboring language areas, was conducted medially up to the putamen, generally visible under the microscope as a gray, compact tissue with white strips located at the center of insula (Yasargyl et al, 1992), which we never found infiltrated in case of low grade tumors. However, while pushing medially tumor removal, we alternated surgical resection to subcortical stimulation starting at a distance of 2 cm laterally to the posterior limb of the internal capsule, as seen on neuronavigation, in order to identify and preserve subcort‐ ical motor pathways (Duffau 2009; Signorelli et al, 2010; 2011; Simon et al, 2009;). Subcortical stimulation is especially useful when pushing tumor resection above superior insular sulcus, where pyramidal fibers coursing through corona radiata are more superficial and anatomic landmarks to them lack. High attention was paid when pushing resection below the lenticular nucleus, at the level of the inferior limiting sulcus, where sublenticular fibers of the posterior limb of the internal capsule contain, in a forward-backward direction, the auditory and the optic radiations (Signorelli et al, 2010). At the level of the anterior part of the external capsule subcortical stimulation allowed the identification of the inferior occipito-frontal fasciculus inducing semantic paraphasias (Duffau 2009), which delimited the deep boundaries of tumor resection anteriorly. The temporal part of the same fasciculus marked the boundaries of the resection at the level of the temporal stem, preventing to open the temporal horn of the ventricle (Duffau 2009; Duffau et a, 2009). Of utmost importance is the recognition of the vascular anatomy. Short branches from MCA to the infiltrated insula can be interrupted because they supply the tumor, paying attention not to avulse them from the main vessel at the origin, which can lead to a lesion of the parent vessel wall. However, long perforators, supplying corona radiata, have to be respected to avoid ischemic injury to functional white matter (Duffau 2009; Lang et al, 2001; Moshel et al, 2008; Signorelli et al, 2010; 2011). During removal of limen insulae high attention has to be paid to lenticulostriate arteries, which originates mostly from the medial or superior aspect of MCA 6 mm or less around bifurcation and sometimes from

**2. Patients and methods**

fissure was approached subpially.

**2.1. Surgical procedure**

Our series includes 5 patients harboring a high grade and 10 patients harboring a low grade tumor involving left insula and perisylvian opercula, operated on between 2007 and 2011 at two institutions: the Neurosurgical Department at the Hôpital Neurologique et Neurochirur‐ gical "Pierre Wertheimer" in Lyon, France, and the Neurosurgical Department of the Univer‐ sity Hospital of Catanzaro, Italy. They were 8 males and 7 females (mean age 50.1 years) who presented with phasic troubles in 8 cases and seizures in all cases. Preoperative antiepileptic treatment was effective in all patients but one, although 3 other patients presented with more than 1 seizure/month. Aphasia was completely regressive in four patients, all LGG, and partially regressive in one HGG patient after administration of antiedema therapy and seizure control, while in 3 other HGG cases it was progressive at a thorough preoperative neuropsy‐ chologic evaluation which comprised Montreal-Toulouse and Boston tests (Dordain et al, 1983) repeated at 1-month. They were nonetheless judged to be good candidates for, and keen

Motor deficit was a presenting symptom in two patients. Moreover, in all HGG patients there were symptoms of intracranial hypertension (ICHT). ICHT had an acute onset in one patient which presented to our department with an intratumoral hemorrhage. This last patient displayed a right sensorimotor deficit and a right homonymous hemianopia. Surgical indica‐ tion was established in lesions with a MRI appearance of LGG in two cases because of clinical and/or radiological tumor progression and in the other eight cases at the time of diagnosis.. All patients were right handed according to the Edimburgh Handedness Inventory (Oldfield, 1971). Gadolinium-enhanced T1-, T2- and FLAIR-weighted images revealed in all cases the infiltration of left insula. The tumor involved also fronto-parietal and temporal opercula in 9 cases, while frontal and temporal opercula or just parietal or temporal operculum were infiltrated respectively in three, two and one case. Moreover, the tumor infiltrated other paralimbic structures (i.e. fronto-orbital and/or temporo-polar areas) in four cases and limbic structures in two cases. In order to elucidate the relationships of the tumor with the vascular tree of left middle cerebral artery (MCA), in particular with lenticulostriate arteries, left carotid angiography was obtained for two patient. The other 13 patients underwent angio-CT scan and/or MRI angiography. The most lateral lenticulostiate branch was shown in 3 out of 15 cases originating from the post-bifurcation tract of M1, no more than 6 mm distal from the major bifurcation, while in the other cases it originated before or at the level of the MCA bifurcation but never from M2, in accordance with other author's experience (Moshel et al, 2008). Particular attention was also paid to the venograms, to determine the course of the superficial sylvian veins, which can hinder a wide dissection of the sylvian fissure, although generally sylvian

All patients underwent awake craniotomy using electrical stimulation mapping (ESM) of sensorimotor and language pathways, whose technique was described in detail elsewhere (Signorelli et al, 2010; 2011). Briefly, we applied a bipolar cortico-subcortical stimulation by an

and motivated to undergo intraoperative language mapping.

346 Functional Brain Mapping and the Endeavor to Understand the Working Brain

early M1 branches (Signorelli et al 2010). Lesion or even manipulation of them can lead to ischemic damage of the internal capsule.

grossly infiltrated nervous tissue. The insular cortical areas whose stimulation evoked abdominal sensations such as nausea, borborygmi, belching (2 patients), chewing and tongue movements without speech arrest (4 patients) were not considered eloquent sites and removed because infiltrated by tumor. In one case ESM caused intraoperative partial tonic-clonic seizures, rapidly stopped pouring cold serum on the cortex. A gross total removal was achieved in all 6 patients that did not display infiltration of perisylvian or insular functional cortex and subcortical motor or language pathways. Stimulation of uncinate fasciculus during removal of an infiltrated limen insulae was done in 8 patients and was always uneventful. In all patients stimulation of the infiltrated white matter at the anterolateral border of the frontal horn of the left lateral ventricle (i.e. the subcallosal fasciculus) triggered limited spontaneous speech and/or perseverations with preservation of normal articulation and at the level of the anterior part of the external capsule as well as at the level of the temporal stem induced semantic paraphasias, which delimited the deep boundaries of tumor resection. Moreover, ESM was used to identify motor pathways inside corona radiata above the insular superior

Surgical Resection of Tumors Infiltrating Left Insula and Perisylvian…

http://dx.doi.org/10.5772/56568

349

limiting sulcus, which represented the posterosuperior limit of tumor resection.

Ten patients had an immediate postoperative phasic aggravation, which lasted 1 to 2 months. At an overall mean follow up of 33 months (14-56 months) 10 patients are alive and keep a good quality of life, as assessed by the EORTC QLQ-C30 (Aaronson et al, 1993). One of them presents a tumor relapse, which causes an impairment of language performances, but she is still autonomous. Seven patients keep the same functional status they had before intervention, while two patients display an improvement of their neuropsychological performance after surgery. Three of the five patients diagnosed with a HGG died after a mean survival period of 16.7 months. Two of them had a mean HQSP (high quality survival period) of 18 months, while the last patient had a postoperative nucleo-capsular infarct, due to lenticulostriate arteries damage, engendering a definitive motor and phasic aggravation. Two other HGG patients, with a follow-up of 23 and 6 months respectively, are autonomous and have a good quality of life. For what concerns seizures outcome, 9 patients were ameliorated and 6 had no variation as regards to their preoperative status. On the postoperative MRI resection was in 6 cases grossly total, in 6 cases subtotal and in three cases partial owing to tumoral infiltration

Several well designed controlled studies indicate that the degree of surgical resection of brain gliomas, including those in highly eloquent areas, affects survival and quality of life of patients (Duffau 2009; Ius et al, 2012; Sanai et al, 2010) and there are some good reasons to treat aggressively such tumors: cytoreduction is effective in reducing the mass effect of the lesion and it can be assumed that it reduces also the contingent of neoplastic cells that can reproduce and give origin to tumor recurrence and invasion of eloquent areas or take anaplastic trans‐ formation (Duffau 2009; Ius et al, 2012; Sanai et al, 2010). Moreover, there are evidences that

**3.2. Clinical results**

of functional tissue.

**4. Discussion**

**Figure 1. A, B,C**: Preoperative FLAIR MR images of a low grade glioma infiltrating the left operculo-insular region and the fronto-orbital, including the perforated substance (white arrow), temporopolar and hyppocampal regions, type 5 B of Yasargil classification (Yasargil et al, 1992). **D**: Postoperative T1 gadolinium-weighted and **E,F**: Postoperative FLAIR MR images, showing the subtotal removal of the lesion. The boundaries of the resection are set based on ana‐ tomical (perforated substance, white arrow) as well as neurofunctional (subcallosal fasciculus, yellow arrow; inferior occipitofrontal fasciculus, green arrow; arcuate fasciculus, blue arrow) criteria.

### **3. Results**

#### **3.1. Electrophysiological results**

ESM of the insular cortex surface resulted in speech arrest in 6 patients In 9 patients insula was free of language sites, as it was in all cases the cortex of opercular clefts and of superior and inferior insular clefts. For what concerns the location of eloquent sites at the level of the convexity, ESM located essential language sites on immediately perisylvian tumoral tissue in just one patient, while in the rest of cases functional areas were displaced at the periphery of grossly infiltrated nervous tissue. The insular cortical areas whose stimulation evoked abdominal sensations such as nausea, borborygmi, belching (2 patients), chewing and tongue movements without speech arrest (4 patients) were not considered eloquent sites and removed because infiltrated by tumor. In one case ESM caused intraoperative partial tonic-clonic seizures, rapidly stopped pouring cold serum on the cortex. A gross total removal was achieved in all 6 patients that did not display infiltration of perisylvian or insular functional cortex and subcortical motor or language pathways. Stimulation of uncinate fasciculus during removal of an infiltrated limen insulae was done in 8 patients and was always uneventful. In all patients stimulation of the infiltrated white matter at the anterolateral border of the frontal horn of the left lateral ventricle (i.e. the subcallosal fasciculus) triggered limited spontaneous speech and/or perseverations with preservation of normal articulation and at the level of the anterior part of the external capsule as well as at the level of the temporal stem induced semantic paraphasias, which delimited the deep boundaries of tumor resection. Moreover, ESM was used to identify motor pathways inside corona radiata above the insular superior limiting sulcus, which represented the posterosuperior limit of tumor resection.

### **3.2. Clinical results**

early M1 branches (Signorelli et al 2010). Lesion or even manipulation of them can lead to

**Figure 1. A, B,C**: Preoperative FLAIR MR images of a low grade glioma infiltrating the left operculo-insular region and the fronto-orbital, including the perforated substance (white arrow), temporopolar and hyppocampal regions, type 5 B of Yasargil classification (Yasargil et al, 1992). **D**: Postoperative T1 gadolinium-weighted and **E,F**: Postoperative FLAIR MR images, showing the subtotal removal of the lesion. The boundaries of the resection are set based on ana‐ tomical (perforated substance, white arrow) as well as neurofunctional (subcallosal fasciculus, yellow arrow; inferior

ESM of the insular cortex surface resulted in speech arrest in 6 patients In 9 patients insula was free of language sites, as it was in all cases the cortex of opercular clefts and of superior and inferior insular clefts. For what concerns the location of eloquent sites at the level of the convexity, ESM located essential language sites on immediately perisylvian tumoral tissue in just one patient, while in the rest of cases functional areas were displaced at the periphery of

occipitofrontal fasciculus, green arrow; arcuate fasciculus, blue arrow) criteria.

**3. Results**

**3.1. Electrophysiological results**

ischemic damage of the internal capsule.

348 Functional Brain Mapping and the Endeavor to Understand the Working Brain

Ten patients had an immediate postoperative phasic aggravation, which lasted 1 to 2 months. At an overall mean follow up of 33 months (14-56 months) 10 patients are alive and keep a good quality of life, as assessed by the EORTC QLQ-C30 (Aaronson et al, 1993). One of them presents a tumor relapse, which causes an impairment of language performances, but she is still autonomous. Seven patients keep the same functional status they had before intervention, while two patients display an improvement of their neuropsychological performance after surgery. Three of the five patients diagnosed with a HGG died after a mean survival period of 16.7 months. Two of them had a mean HQSP (high quality survival period) of 18 months, while the last patient had a postoperative nucleo-capsular infarct, due to lenticulostriate arteries damage, engendering a definitive motor and phasic aggravation. Two other HGG patients, with a follow-up of 23 and 6 months respectively, are autonomous and have a good quality of life. For what concerns seizures outcome, 9 patients were ameliorated and 6 had no variation as regards to their preoperative status. On the postoperative MRI resection was in 6 cases grossly total, in 6 cases subtotal and in three cases partial owing to tumoral infiltration of functional tissue.

### **4. Discussion**

Several well designed controlled studies indicate that the degree of surgical resection of brain gliomas, including those in highly eloquent areas, affects survival and quality of life of patients (Duffau 2009; Ius et al, 2012; Sanai et al, 2010) and there are some good reasons to treat aggressively such tumors: cytoreduction is effective in reducing the mass effect of the lesion and it can be assumed that it reduces also the contingent of neoplastic cells that can reproduce and give origin to tumor recurrence and invasion of eloquent areas or take anaplastic trans‐ formation (Duffau 2009; Ius et al, 2012; Sanai et al, 2010). Moreover, there are evidences that aggressive removal of insular tumors can improve seizures control, which are their most frequent clinical manifestation (Taillandier et al, 2009). Authors pleading for an aggressive treatment of such tumors mostly think that it should be realized early after diagnosis to prevent clinical impairment and improve survival and recurrence free period of patients (Duffau 2009; Sanai et al, 2010).

in an acute stage and at distance from surgical intervention. This could be explained by the fact that sensorimotor and language functions seem to be organised within multiple parallel networks. Beyond the recruitment of areas adjacent to the surgical cavity, the long term reshaping could be related to progressive involvement of regions within the hemisphere omolateral to the lesion as well as of the contralateral hemisphere (Duffau, 2006).. In these cases functional reshaping involves association areas belonging to the same functional network of the lesioned area as it is the case for dominant insula and perisilvyan language sites. However, mechanisms of compensation are limited. One of such limits is that reorganisation seems to be more effective in secondary than in primary areas, as for SMA (Duffau, 2006). Moreover, if a damaged area is compensated by another region, a lesion of this newly recruited region will induce a permanent deficit, as it could be the case for dominant insulo-opercular gliomas. Thus, surgical resection should avoid infringement of insula if there are arguments indicating that it took over, at least partially, the lost function of perisylvian opercula. Taking into account these data may guide treatment of cerebral tumors in the dominant deep perisylvian area, broadening the surgical indication and the extent of tumor removal while lessening the rate of postoperative permanent deficits, and be useful for defining prognosis and rehabilitation

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351

ESM: electrical stimulation mapping; HGG: high grade gliomas; ICHT: intracranial hyperten‐ sion; LGG: low grade gliomas; MCA: middle cerebral artery; MRI: magnetic resonance

1 "Magna Græcia" University, Department of Experimental and Clinical Medicine "G. Sal‐

2 Hospices Civils de Lyon, Hôpital Neurologique et Neurochirurgical, Department of Neu‐

3 "Magna Græcia" University, Department of Medical and Surgical Sciences, Chair of Neu‐

, Rodolfo Maduri1

, Giuseppe Barbagallo4

and

programs.

**Abbreviations**

**Author details**

Jacques Guyotat1

rosurgery, Lyon, France

rosurgery, Catanzaro, Italy

imaging; T1: superior temporal gyrus.

Francesco Signorelli1,2, Domenico Chirchiglia3

vatore", Chair of Neurosurgery, Catanzaro, Italy

4 University of Catania, Chair of Neurosurgery, Catania, Italy

Since the first report by Yasargil et al, other papers in the literature dealt with the surgical treatment of tumors infiltrating insular lobe (Duffau 2009, Duffau et al, 2000; 2001; 2006; 2009; Lang et al, 2001; Kim et al, 2002; Moshel et al, 2008; Saito et al, 2010; Sanai et al, 2010; Signorelli et al, 2010; 2011; Simon et al, 2009; Skrap et al, 2012; Yasargil et al, 1992; Wu et al, 2011; Zentner et al, 1996) and encompassed lesions with a variety of anatomical extensions. As a matter of fact, these series reported on purely insular tumors (type 3A of the Yasargyl's classification) as well as insulo-opercular (type 3B) and limbic-paralimbic lesions (type 5) involving both the dominant and the non-dominant hemisphere. Some authors reporting surgical removal of dominant-sided insular tumors did not find useful or did not employ awake surgery for language mapping (Hentschel et al, 2005; Lang et al, 2001; Simon et al, 2009; Yasargyl et al, 1992; Zentner et al, 1996), others demonstrated the utility of ESM mapping guided tumor resection, although seldom insula was found to harbor essential language sites (Duffau 2009; Duffau et al, 2001; 2009). In Duffau's series there were no permanent postoperative phasic deficits although he reported 10 cases of transient articulatory disorders (Duffau et al, 2000). In Hentschel and Lang's series there were 6 cases of transient speech troubles among patients with 3B tumors and in Zentner's series two of the 11 patients had a permanent postoperative aphasia (Hentschel et al, 2005; Zentner et al, 1996).

Our series, albeit small, is anatomically homogeneous in that focuses on tumors infiltrating the insular lobe of the dominant hemisphere and extended to the opercular region and, in six cases, also to adjacent deep perisylvian structures. Moreover, all patients were operated on while testing language function. The retrospective analysis restricted to these patients shows two basic findings: 6 out of 15 such patients, all harboring a LGG infiltrating the frontoparietal and temporal opercula, had speech arrest while stimulating insular cortex and these same patients did not have language sites on the opercular part invaded by the tumor. Conversely, the 9 patients for whom ESM of insular cortex did not trigger language troubles all harboured speech function on perisylvian opercula. They either had preoperative language troubles (4 cases), which did not hinder intraoperative language mapping, or a limited opercular infil‐ tration, and no phasic deficits (5 cases).Thus, it can be speculated that for the 6 LGG patients displaying language sites on insula, this region compensated the opercular infiltration due to a plasticity phenomenon, which can be considered at least in part responsible for the preop‐ erative regression of the phasic deficits. For the remaining patients the functional reorganiza‐ tion might not have occurred because of a limited opercular infiltration (1 patient) or because of a too extensive and rapid inactivation of perisylvian language sites by a high grade tumor. The compensatory role of left insula in case of infiltration of perisylvian language areas has already been pointed out as a function that must be preserved (Duffau et al, 2000). However, the compensatory potential of left insula seems to be highly variable on individual basis. There are mechanisms of cerebral plasticity taking place before the treatment of the lesion and both in an acute stage and at distance from surgical intervention. This could be explained by the fact that sensorimotor and language functions seem to be organised within multiple parallel networks. Beyond the recruitment of areas adjacent to the surgical cavity, the long term reshaping could be related to progressive involvement of regions within the hemisphere omolateral to the lesion as well as of the contralateral hemisphere (Duffau, 2006).. In these cases functional reshaping involves association areas belonging to the same functional network of the lesioned area as it is the case for dominant insula and perisilvyan language sites. However, mechanisms of compensation are limited. One of such limits is that reorganisation seems to be more effective in secondary than in primary areas, as for SMA (Duffau, 2006). Moreover, if a damaged area is compensated by another region, a lesion of this newly recruited region will induce a permanent deficit, as it could be the case for dominant insulo-opercular gliomas. Thus, surgical resection should avoid infringement of insula if there are arguments indicating that it took over, at least partially, the lost function of perisylvian opercula. Taking into account these data may guide treatment of cerebral tumors in the dominant deep perisylvian area, broadening the surgical indication and the extent of tumor removal while lessening the rate of postoperative permanent deficits, and be useful for defining prognosis and rehabilitation programs.

### **Abbreviations**

aggressive removal of insular tumors can improve seizures control, which are their most frequent clinical manifestation (Taillandier et al, 2009). Authors pleading for an aggressive treatment of such tumors mostly think that it should be realized early after diagnosis to prevent clinical impairment and improve survival and recurrence free period of patients (Duffau

Since the first report by Yasargil et al, other papers in the literature dealt with the surgical treatment of tumors infiltrating insular lobe (Duffau 2009, Duffau et al, 2000; 2001; 2006; 2009; Lang et al, 2001; Kim et al, 2002; Moshel et al, 2008; Saito et al, 2010; Sanai et al, 2010; Signorelli et al, 2010; 2011; Simon et al, 2009; Skrap et al, 2012; Yasargil et al, 1992; Wu et al, 2011; Zentner et al, 1996) and encompassed lesions with a variety of anatomical extensions. As a matter of fact, these series reported on purely insular tumors (type 3A of the Yasargyl's classification) as well as insulo-opercular (type 3B) and limbic-paralimbic lesions (type 5) involving both the dominant and the non-dominant hemisphere. Some authors reporting surgical removal of dominant-sided insular tumors did not find useful or did not employ awake surgery for language mapping (Hentschel et al, 2005; Lang et al, 2001; Simon et al, 2009; Yasargyl et al, 1992; Zentner et al, 1996), others demonstrated the utility of ESM mapping guided tumor resection, although seldom insula was found to harbor essential language sites (Duffau 2009; Duffau et al, 2001; 2009). In Duffau's series there were no permanent postoperative phasic deficits although he reported 10 cases of transient articulatory disorders (Duffau et al, 2000). In Hentschel and Lang's series there were 6 cases of transient speech troubles among patients with 3B tumors and in Zentner's series two of the 11 patients had a permanent postoperative

Our series, albeit small, is anatomically homogeneous in that focuses on tumors infiltrating the insular lobe of the dominant hemisphere and extended to the opercular region and, in six cases, also to adjacent deep perisylvian structures. Moreover, all patients were operated on while testing language function. The retrospective analysis restricted to these patients shows two basic findings: 6 out of 15 such patients, all harboring a LGG infiltrating the frontoparietal and temporal opercula, had speech arrest while stimulating insular cortex and these same patients did not have language sites on the opercular part invaded by the tumor. Conversely, the 9 patients for whom ESM of insular cortex did not trigger language troubles all harboured speech function on perisylvian opercula. They either had preoperative language troubles (4 cases), which did not hinder intraoperative language mapping, or a limited opercular infil‐ tration, and no phasic deficits (5 cases).Thus, it can be speculated that for the 6 LGG patients displaying language sites on insula, this region compensated the opercular infiltration due to a plasticity phenomenon, which can be considered at least in part responsible for the preop‐ erative regression of the phasic deficits. For the remaining patients the functional reorganiza‐ tion might not have occurred because of a limited opercular infiltration (1 patient) or because of a too extensive and rapid inactivation of perisylvian language sites by a high grade tumor. The compensatory role of left insula in case of infiltration of perisylvian language areas has already been pointed out as a function that must be preserved (Duffau et al, 2000). However, the compensatory potential of left insula seems to be highly variable on individual basis. There are mechanisms of cerebral plasticity taking place before the treatment of the lesion and both

2009; Sanai et al, 2010).

aphasia (Hentschel et al, 2005; Zentner et al, 1996).

350 Functional Brain Mapping and the Endeavor to Understand the Working Brain

ESM: electrical stimulation mapping; HGG: high grade gliomas; ICHT: intracranial hyperten‐ sion; LGG: low grade gliomas; MCA: middle cerebral artery; MRI: magnetic resonance imaging; T1: superior temporal gyrus.

### **Author details**

Francesco Signorelli1,2, Domenico Chirchiglia3 , Rodolfo Maduri1 , Giuseppe Barbagallo4 and Jacques Guyotat1

1 "Magna Græcia" University, Department of Experimental and Clinical Medicine "G. Sal‐ vatore", Chair of Neurosurgery, Catanzaro, Italy

2 Hospices Civils de Lyon, Hôpital Neurologique et Neurochirurgical, Department of Neu‐ rosurgery, Lyon, France

3 "Magna Græcia" University, Department of Medical and Surgical Sciences, Chair of Neu‐ rosurgery, Catanzaro, Italy

4 University of Catania, Chair of Neurosurgery, Catania, Italy

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**Chapter 18**

**Optimized Signal Separation for**

Additional information is available at the end of the chapter

Jürgen Dammers, Lukas Breuer, Giuseppe Tabbì and

"Why should we bother about connectivity in the age of functional imaging, at a time when magnets of ever increasing strength promise to detect the location of even the faintest thought? Isn't it enough to locate cortical areas engaged in deception, introspection, empathy? Do we really have to worry about their connections? The answer is "yes". In the case of the nervous system, the unit of relational architecture that allows the whole to exceed the sum of the parts is known as large-scale network. Its elucidation requires an elaborate understanding of connectivity patterns" [1]. Despite considerable advances in experimental techniques and in our understanding of animal anatomy over the last decades, the real connectivity of the human brain has essentially remained a mystery. It is the human brain's multiscale topology that poses a particular challenge to any neuroimaging technique and prevented the neuroscientists from

However, it is also the brain's architecture that allows different morphological entities to be defined at different scales depending on the spatial resolution provided by the available neuroimaging techniques and the scientific objectives. Consequently, a comprehensive description of neuronal networks and their intricate fiber connections requires a multimodal approach based on complementary imaging techniques targeting different levels of organiza‐

MR-based diffusion imaging is the most frequently used method to visualize fiber pathways in both the living and the postmortem human brain (for a comprehensive introduction to the field cf. [4,5]). Diffusion imaging contributes to the understanding of the macroscopic connec‐

and reproduction in any medium, provided the original work is properly cited.

© 2013 Dammers et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**3D-Polarized Light Imaging**

Markus Axer

**1. Introduction**

http://dx.doi.org/10.5772/55246

**1.1. Anatomical connectivity mapping**

unraveling the connectome so far.

tion (microscale, mesoscale, and macroscale) [2,3].

**Chapter 18**
