William J. Bosl

*Harvard Medical School, Children's Hospital Boston Informatics Program, Boston University School of Medicine Behavioral Neuroscience Program, USA* 

### **1. Introduction**

510 Biomedical Science, Engineering and Technology

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Weiss U., Time and Dose Dependent Influence of Magnesium-Aspartate-Hydrochloride Treatment Upon Hormonal and Enzymatic Changes as well as Alterations in Meat Quality Due to Slaughtering Stress in Pigs.Mg. Bull. 12/3, 1993, Neuroscience as a scientific discipline has enjoyed enormous growth and success in the past decade. Some have called the early 20th century the golden age of physics, the latter half of the 20th century a period when the genomic revolution blossomed and predict that the early 21st century will be a period when brain sciences achieve remarkable success. While understanding the basic mechanisms of the brain and how they relate to thought and behavior may be the foundation for applications to medicine, there is a great need for technological innovation if more than academic results are to be achieved. The need for neurotechnology is already great. Traumatic brain injury and damaged limbs require prosthetic devices that can be controlled in some way by willful volition. Ideally, direct connections between thought and action are desirable to restore natural functions. Mental health is a branch of medicine that has long been relegated to a secondary status within medicine. The reasons for this may be many, but they certainly include the difficulty of understanding and measuring brain activity in a quantitative way and relating those measurements to behavior and cognitive activity. As healthcare costs continue to spiral out of control in both developed and developing regions of the world, the need for engineers to become involved in neuroscience and neurotechnology research and development has never been greater. Innovative engineering ideas, with a view toward practical application and affordable cost have much to contribute to clinical applications of brain science. A key contribution of neuroengineering will be innovative methods for quantitative measurement of brain activity and mapping of those measurements to behavior and thought. The term psychiatric biomarkers will be used here in this broad sense to indicate quantitative measurements of the brain and the algorithms necessary to interpret them in psychiatric or psychological descriptions or diagnoses.

One important way in which psychiatric biomarkers differ from other physiological biomarkers is in that the mapping from biomarker to symptom or disease is much more complex. A biomarker for a specific cancer, for example, may be a gene mutation that is in some way directly involved in the disease progression itself. The relationship between biomarker and the manifestation of interest – cancer, in this case – is rather simple and direct. That is not to say that the gene or the gene expression patterns are simple to find, but only that the conceptual relationship between the marker and disease is simple to understand. In the case of psychiatric biomarkers, the phenomena of interest, thought and behavior, are complex, emergent phenomena of brain neural activity. The relationship between neural firing patterns and the communication deficits that are clearly evident in a person with a mental disorder is not at all clear, even if we posit that all thought is indeed dependent upon neural activity. The relationship in this case is somewhat like the relationship between letters of the alphabet and a metaphor in great literature. Certainly metaphors depend on spelling and grammar, but the concept is much more than spelling and grammar. Similarly, the complex patterns of neural activity that distinguish the way a person with autism responds to someone speaking directly to them from someone considered "normal" are quite complex.

Normal and abnormal behavior are differentiated by subtle, complex patterns of activity that a trained expert observes or discovers through systematic diagnostic tests. If brain function and behavior are mirrors of each other, as is commonly accepted (Cowan and Kandel, 2001; Hyman, 2007; Kandel, 1998; Singh and Rose, 2009), then biomarkers of mental disorders may be hidden in subtle, complex patterns of neurobiological data. There is a growing realization that the neurophysiological mechanisms that underlie brain function cannot be understood by pure reduction to physiological causes (Stam, 2005; Ward, 2003). The dynamics of the brain is inherently nonlinear, exhibiting emergent dynamics such as chaotic and transiently synchronized phenomena that may be central to understanding the mind-brain relationship (Varela et al., 2001) or the 'dynamic core' (Le Van Quyen, 2003). The behaviors and thoughts that characterize mental dysfunction may be emergent phenomena or complex patterns of physiological processes, especially neural processes. For example, major depression or the communication deficits present in a child with autism are emergent phenomena that reflect complex patterns in brain function that differ from some sociallydefined norm. The task of the neuroengineer is to create new technology to measure and interpret the patterns of brain activity that connect brain measurements to observed behavioral patterns.

A key challenge in cognitive neuropsychiatry is to discover the neural correlates underlying behavior. To be clinically useful, these discoveries must be accompanied by technology that enables brain activity to be measured and interpreted safely, inexpensively and easily. The explosive growth of neuroimaging studies that link functional brain activity to behavior promises exciting opportunities for measuring nonlinear brain activity that may indicate abnormalities or allow response to therapy to be monitored. While several imaging modalities are available for neuroscience *research*, most have significant limitations that prohibit their use as routine *clinical* tools. Cost and ease of use are essential qualities for clinically useful tools, which may not be as important or relevant in a scientific research context. Neuroengineers must be cognizant of these constraints when considering the intended use of the technology.

Measurements of brain electrical activity with electroencephalography (EEG) have long been a valuable source of information for neuroscience research, yet this rich resource may be under-utilized for clinical applications in neurology and psychiatry (Niedermeyer, 2003; Niedermeyer and Lopes da Silva, 2005). To fully exploit this data, methods for discovering subtle nonlinear patterns and deeper understanding of the relationship between emergent signal features, neurophysiology and behavior are needed. Near infrared spectroscopy (NIRS) has recently been introduced as a safe, portable alternative for measuring blood oxygen level dependent (BOLD) response in infants (Irani et al., 2007; Muehlemann et al., 2008). One of the primary advantages of NIRS, like EEG, is that it is safe for all ages, relatively inexpensive and portable. As a new brain-imaging tool, much remains to be discovered about the value and limitations of NIRS as a clinical instrument. In addition,

between neural firing patterns and the communication deficits that are clearly evident in a person with a mental disorder is not at all clear, even if we posit that all thought is indeed dependent upon neural activity. The relationship in this case is somewhat like the relationship between letters of the alphabet and a metaphor in great literature. Certainly metaphors depend on spelling and grammar, but the concept is much more than spelling and grammar. Similarly, the complex patterns of neural activity that distinguish the way a person with autism responds to someone speaking directly to them from someone

Normal and abnormal behavior are differentiated by subtle, complex patterns of activity that a trained expert observes or discovers through systematic diagnostic tests. If brain function and behavior are mirrors of each other, as is commonly accepted (Cowan and Kandel, 2001; Hyman, 2007; Kandel, 1998; Singh and Rose, 2009), then biomarkers of mental disorders may be hidden in subtle, complex patterns of neurobiological data. There is a growing realization that the neurophysiological mechanisms that underlie brain function cannot be understood by pure reduction to physiological causes (Stam, 2005; Ward, 2003). The dynamics of the brain is inherently nonlinear, exhibiting emergent dynamics such as chaotic and transiently synchronized phenomena that may be central to understanding the mind-brain relationship (Varela et al., 2001) or the 'dynamic core' (Le Van Quyen, 2003). The behaviors and thoughts that characterize mental dysfunction may be emergent phenomena or complex patterns of physiological processes, especially neural processes. For example, major depression or the communication deficits present in a child with autism are emergent phenomena that reflect complex patterns in brain function that differ from some sociallydefined norm. The task of the neuroengineer is to create new technology to measure and interpret the patterns of brain activity that connect brain measurements to observed

A key challenge in cognitive neuropsychiatry is to discover the neural correlates underlying behavior. To be clinically useful, these discoveries must be accompanied by technology that enables brain activity to be measured and interpreted safely, inexpensively and easily. The explosive growth of neuroimaging studies that link functional brain activity to behavior promises exciting opportunities for measuring nonlinear brain activity that may indicate abnormalities or allow response to therapy to be monitored. While several imaging modalities are available for neuroscience *research*, most have significant limitations that prohibit their use as routine *clinical* tools. Cost and ease of use are essential qualities for clinically useful tools, which may not be as important or relevant in a scientific research context. Neuroengineers must be cognizant of these constraints when considering the

Measurements of brain electrical activity with electroencephalography (EEG) have long been a valuable source of information for neuroscience research, yet this rich resource may be under-utilized for clinical applications in neurology and psychiatry (Niedermeyer, 2003; Niedermeyer and Lopes da Silva, 2005). To fully exploit this data, methods for discovering subtle nonlinear patterns and deeper understanding of the relationship between emergent signal features, neurophysiology and behavior are needed. Near infrared spectroscopy (NIRS) has recently been introduced as a safe, portable alternative for measuring blood oxygen level dependent (BOLD) response in infants (Irani et al., 2007; Muehlemann et al., 2008). One of the primary advantages of NIRS, like EEG, is that it is safe for all ages, relatively inexpensive and portable. As a new brain-imaging tool, much remains to be discovered about the value and limitations of NIRS as a clinical instrument. In addition,

considered "normal" are quite complex.

behavioral patterns.

intended use of the technology.

coupling EEG and NIRS may have some advantages for clinical use and remains to be explored by researchers. Many of the advances in non-invasive functional brain measurement are being driven by the brain computer interface community, where mobility and cost requirements limit the technologies that can be adopted (Dornhege, 2007). Neuropsychology and cognitive neuropsychiatry can learn from this community, while adapting the methods to the particular needs of behavioral, affective and cognitive assessment.

In this chapter some relevant information concerning our current understanding of complex network organization and implications for finding neural correlates of behavior will be reviewed with goal of motivating engineers to consider contributing their skills to developing new neurotechnology. Considerable attention will be given to EEG measurements as one of the most promising technologies for clinical application to neuropsychiatry. Novel methods for extracting information from EEG signals are beginning to appear, taking advantage of advances in the physics of nonlinear systems and signals, complex network theory and machine learning algorithms. The need for innovative neurotechnology to meet the need for mental and neurological healthcare in developing regions of the world is great, but the promise is even greater. The primary goal of this chapter is to provide information to enable researchers interested in brain disorders and mental health to become involved in creating innovative neurotechnology for clinical use.
