Preface

Huntington's Disease (HD) is a neurodegenerative disease caused by the autosomal dominant mutation, in particular, CAG trinucleotide expansion, in the huntingtin gene on chromosome 4. It is characterized by complex phenomenology, in particular, cognitive, motor, and psychiatric symptoms. When it comes to pathophysiology, it has been demonstrated that mutant huntingtin leads to neuronal death via a number of mechanisms such as mitochondrial abnormalities, disruption of protein regulation, and direct toxicity of the mutant protein. Early changes are mainly detected in the striatum, but also the cortex as the disease progresses. In recent years there have been significant advances in research dedicated to HD biomarkers, diagnosis, and, finally, therapy. To date, only symptomatic treatment has been available. With the consequent urgent need for studies to identify new targets for therapeutic interventions, not only is research focused on the development of new treatments of utmost importance, but basic science, neuroimaging and biomarkers are also relevant. With this book, we would like to raise awareness of the most up-to-date HD science. We hope that it will be of use to both experts and the general public.

**Natalia Szejko**

Department of Neurology, Department of Bioethics, Medical University of Warsaw, Warsaw, Poland

**1**

Section 1

Biomarkers of Huntington'

Disease

s

Section 1

### Biomarkers of Huntington' s Disease

#### **Chapter 1**

## Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration and Aggregates

*Elisabeth Petrasch-Parwez, Hans-Werner Habbes, Marlen Löbbecke-Schumacher, Constanze Rana Parwez, Carsten Saft and Sarah Maria von Hein*

#### **Abstract**

Neuropathology of Huntington's disease (HD) presents with progredient neuronal cell loss mainly in the striatum, but also in multiple other brain areas suggesting HD as a multisystem neurodegenerative disorder. Mutant huntingtin aggregates are the characteristic hallmark of HD. The aggregates are misfolded proteins varying in location, form, size and structural composition indicating a complex involvement in neurotoxicity. The question if and how the aggregates and many interacting protein partners may lead to cell death is continuously a matter of debate. The role of mutant huntingtin is more than ever of paramount importance as present genetic therapeutic approaches try to target downregulation of the Huntingtin gene expression and/or lowering the corresponding protein. In this context—and these aspects are focussed—it is of crucial interest to elucidate the regional distribution as well as the cellular and subcellular localization of aggregates in established animal models of HD and in affected HD brains.

**Keywords:** Huntington's disease, mutant huntingtin, misfolded proteins, aggregates, inclusion bodies, neurodegeneration, human HD brain, R6/2 mouse, tgHD rat, EM48-immunohistochemistry, transmission electron microscopy

#### **1. Introduction**

The autosomal dominantly transmitted Huntington's disease (HD) is caused by an expanded cytosine-adenine-guanine (CAG) trinucleotide repeat in exon 1 of the Huntingtin gene (*HTT*) resulting in an abnormally long polyglutamine tract in the protein huntingtin (Htt; [1]). Patients with 36–39 CAG repeats have an increasing risk to develop HD characteristic symptoms and repeats of 40 and more will result in onset of the disease within a normal lifespan [2]. In about 90% of adult-onset HD patients, the mean age of onset is between 35 and 50 years with marked individual variations; duration of the illness is usually 15–20 years. There is also a correlation between the CAG repeat length and the age of onset in HD [3]. Manifest patients

≤20 years were classified as juvenile-onset HD patients with an estimated prevalence of up to 15%, associated with CAG repeats >60 leading to early death [4, 5]. Core clinical symptoms are cognitive decline, progredient motor impairments and psychiatric alterations—the latter often preceding the onset of the other symptoms.

Neuropathologically, HD shows progredient neuronal cell loss most pronounced in the neostriatum, but also in many other cortical and non-cortical brain areas with considerable regional differences between the HD individuals reflecting the high variability of clinical symptoms. Currently, there is no cure for HD, and only symptoms can be treated.

HD-affected brains show misfolded proteins in form of mutant Huntingtin (mHtt) aggregates, which may be toxic or protective, and their pathomechanism is far from being understood. Aggregates are detected in the nucleus of neurons, the cytoplasm, cell processes and the neuropil. Notably, new therapies address lowering the *mHTT* gene production and/or mHtt protein expression to slow down or even stop disease progression [6, 7]. Therefore, localization of mHtt in HD-affected brains is of major interest in the interplay between the pathogenesis and therapeutic approaches.

In this chapter, we start with some general aspects on neurodegeneration in the human HD brain, then review the distribution and composition of mHtt aggregates and inclusions in two selected rodent models and in human HD brains and conclude with an outlook to future studies to further elucidate the controversial discussion about aggregates and their toxicity.

#### **2. Neurodegeneration in human HD brains**

Degeneration of human HD brains has been reported long before the causative gene was detected [8, 9]. The diagnosis was initially performed according to family history, characteristic choreiform movements, cognitive decline and the progredient course of the disease. First post-mortem studies focussed on bilateral striatal atrophy that has always been the most pronounced and consistent macroscopic alteration of the HD brain. The striatal atrophy, which occurs in 95% of all examined HD postmortem brains, has led to the grading system of Vonsattel, still the most used tool when neurodegeneration of the human HD brain is classified [10, 11]. Based on a high number of post-mortem brains, Vonsattel evaluated the degeneration of striatal areas including the caudate nucleus and putamen as dorsal (motor) and the accumbens as ventral (limbic) neostriatum and the globus pallidus with its external and internal segment as paleostriatum, the latter belonging to the diencephalon. The caudate nucleus and putamen are first affected with a progredient caudo-rostral, mediolateral and dorso-ventral shift, followed by the accumbens ventrally to the head of the caudate nucleus. Vonsattel determined the temporospatial striatal atrophy into five grades (0–4), also involving the pathohistology of the affected areas. An example of the striatal atrophy at grade 3–4 is documented in **Figure 1A**. At this advanced stage of degeneration, the medial striatal outline bordering the ventricle is straight, the caudate nucleus can hardly be identified and the putamen and globus pallidus are enormously shrunken. The neostriatal atrophy is due to the progredient loss of medium-sized projection neurons that are with about 90–95% the most abundant neuronal cell type in all neostriatal areas (**Figure 1C**). With progredient degeneration, most neurons are dysmorph or lost in the caudate nucleus and putamen, especially in the dorsal parts, accompanied by a marked increase in glial cells (**Figure 1B**). In HD, astrocytosis was reported to be inversely proportional to neuronal cell loss with a later *Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration… DOI: http://dx.doi.org/10.5772/intechopen.102828*

#### **Figure 1.**

*(A) Frontal HD brain section at advanced degeneration stage (Vonsattel grade 3–4) and a control brain at comparable striatal level with the anterior commissure (ac). Atrophy of the caudate nucleus (Cd) adjacent to the internal capsule (ic) leads to straight outline (white arrowheads) bordering the enlarged lateral ventricle (V) not observed in striatal areas of the control brain. Putamen (Pu) and globus pallidus (GP) also display severe atrophy. Note the shrinkage of white matter (wm) and the corpus callosum (cc) in the HD brain. (B) Cresyl violet-stained paraffin section (15 μm) of the HD putamen displays loss of most medium-sized striatal neurons with some neuronal cell bodies left (black arrowheads) and a pronounced gliosis as detected by abundant small cell nuclei. (C) Section of the control brain shows normal distribution of medium-sized striatal neurons (black arrowheads). Bar in A = 5 cm; bar in C for B and C = 150 μm.*

beginning, whereas the density of oligodendrocytes was already increased at the early Vonsattel grades 0, 1 and 2 [12]. An increase in oligodendrocytes was also observed in the tail of caudate nucleus in HD mutation carriers already prior to the onset of symptoms [13]. A grade-dependant increase of activated microglia was also found in the dorsal neostriatum and globus pallidus [14]. In contrast, ventral striatal areas only show mild neuronal cell loss and moderate gliosis, even at advanced stage of degeneration [11]. The pronounced vulnerability of medium-sized projection neurons is still unclear [15, 16] and the fate of local aspiny striatal interneurons which make up about 5–10% of all striatal neurons is still controversial. They appear to be less and later affected in HD [17]. The striatal interneurons consist of different subpopulations all of which may undergo a different grade-dependant degeneration pattern [18]. Notably, striatal atrophy is also correlated with HD repeat size, younger age of onset and age of death [19].

The globus pallidus as the main neostriatal projection area also undergoes severe atrophy in HD (**Figure 1A**). Pallidal degeneration starts later at Vonsattel grade 2 up to a volume loss of around 50% at grade 4 with the external (the major output target area of the dorsal neostriatum) more affected than the medial segment [20]. Interestingly, the pallidal volume shrinkage is mainly due to loss of neuropil [11, 21], which could represent the reduction of projection axons emerging from neostriatal neurons und their synaptic terminals densely contacting the large pallidal neurons and their proximal dendritic shafts.

Beyond the striatum cortical, other diencephalic and brainstem areas are also affected though highly variable in expression. Morphometric studies of the telencephalon detected that all four lobes showed cortical atrophy more expressed in parietal and occipital than in frontal and temporal areas [22]. These observations were confirmed by MRI-based studies [23]. Imaging studies can be applied in large cohorts of patients in pre-symptomatic, early, middle and late stages and are therefore valuable tools for investigating the development of atrophy in HD brains. A recent MRI study

in HD patients carried out annually over a time period of 10 years also confirmed the greatest atrophy in parietal and occipital cortical areas [24]. Remarkably, neuronal cell loss is considerably variable between HD subjects as detected in selected cortical areas [25]. Furthermore, loss of neurons in the primary motor cortex is related to motor symptoms, whereas loss of neurons in the anterior cingulate cortex is related to mood disturbances [26]. To date, many cortical areas have not yet been examined.

In addition to pallidal studies, diencephalic investigations focussed on thalamic and hypothalamic affection. A voxel-based morphometry-based study detected a co-variation between atrophy and cognitive performance suggesting impairment in executive functions [27]. Atrophy is described in the centromedian/parafascicular thalamic complex [28]. The centromedian nucleus is involved into the sensorimotorassociated basal-ganglia-thalamo-cortical feedback loop. The mediodorsal nucleus, which is involved in the corresponding limbic loop, also shows significant neuronal cell loss [29]. Thus, thalamic nuclei involved in HD-associated functionally important feedback loops appear to be severely affected in HD.

Interestingly, the hypothalamus shows a significant loss of grey matter signals already in prodromal HD individuals [30]. Some non-motor dysfunctions are discussed to be associated with changes in neuropeptidergic cell populations disturbing hypothalamic circuitry [31]. Dysfunctions include daily hormone excretion pattern and circadian rhythm disorders, which could also be a target for therapeutic treatment in the disease.

As HD patients show cognitive decline such as planning deficits and short-term memory impairments often already in prodromal phases of the disease, hippocampal involvement should also be considered. A mild but significant atrophy of the hippocampus formation was observed by Lange and Aulich [22] and later confirmed by MRI-based morphometric studies [23]. Accordingly, Vonsattel et al. [11] detected loss of neurons and gliosis in numerous HD cases. However, to really evaluate the hippocampal impact on cognitive impairments, more specific studies on the different subdivisions and cell populations in correlation with clinical symptoms are necessary.

Consistent neuronal loss was also detected in brain stem areas such as substantia nigra, superior and inferior olive, pontine and vestibular nuclei [32]. Regional brain stem affection may contribute to better understanding of vestibular and oculomotor dysfunctions in HD, the latter being one of the main clinical features of HD.

Vonsattel et al. [11] reported on the basis of more than 1000 post-mortem brains that the cerebellum is only slightly smaller in grade 3 and 4 HD brains than in controls. He also detected that the mainly segmental loss of Purkinje cells is inconsistent across the HD brains examined. In contrast, Rüb et al. [33] found Purkinje cell loss in the cerebellum and loss of neurons in the four cerebellar nuclei. In a recent study, significant Purkinje cell loss was correlated with motor impairments, whereas no loss was associated with a major mood-phenotype in HD [34]. Notably, cerebellar atrophy is particularly pronounced in juvenile-onset HD individuals accompanied with neuronal loss and gliosis [35].

White matter degeneration is most obvious in telencephalic areas including the corpus callosum and internal capsula (**Figure 1A**) indicating a severe affection of interhemispheric commissural connections and projection fiber tracts between cortical and noncortical brain regions. White matter alterations occur early in HD as supported by post-mortem [36] and magnetic resonance studies [37, 38]. Fiber tracts that are less in HD focus are also early affected. The fornix connecting the hippocampus with mammillary bodies displays a reduction of 34% already in prodromal cases and 41% in manifest HD [39]. This study also shows that white matter pathology is partly due to myelin breakdown and reduction of oligodendrocyte genes.

*Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration… DOI: http://dx.doi.org/10.5772/intechopen.102828*

All in all, for many cortical and noncortical areas including white matter and fiber tracts, detailed information is still limited and needs more specific interdisciplinary investigations to provide a better understanding of the regional pathology and respective functional impairments. Of note, HD may also be related to other neurodegenerative diseases, for example Alzheimer (AD) and Parkinson disease that could influence regional degeneration [19]. However, the frequency of the coexistence of AD in HD is similar to AD in general population [11]. These aspects have to be considered when evaluating the neuropathology of HD, especially at older ages. Finally, the variation in neuronal degeneration among different HD patients also reflects the heterogeneity in functional impairments and pathogenesis.

#### **3. Mutant Huntingtin aggregates**

Misfolded proteins are common in many neurodegenerative diseases such as amyloid plaques or neurofibrillary tangles in Alzheimer and Lewy bodies in Parkinson disease. However, Alzheimer and Parkinson diseases comprise a group of disorders with similar symptoms, but may be caused by various reasons. HD is caused by a single gene; therefore, studies on HD-specific aggregates are particularly useful, as they are well comparable among different HD-affected brains. The CAG trinucleotide repeat in the *HTT* gene leads on translation to a polyglutamine stretch at the N-terminus of the protein Htt. Misfolded fragments of the protein are detected as aggregated forms differing in size, shape and composition within the cell nucleus, soma, cell processes or optionally also in the intercellular space. The pathogenesis of aggregates is still unclear and the interplay between neurodegeneration and aggregation far from being understood.

In HD brains, misfolded proteins were first described as so-called inclusion bodies by conventional electron microscopy [40]. They were detected in the nucleus of neurons as membrane-less round structures that could be distinguished due to lighter homogenous appearance from the surrounding caryoplasm. This observation must have been more of an incidental finding, as intranuclear inclusions are relatively rare in adult-onset human HD brains and extremely difficult to detect without immunohistochemical staining. Similar intranuclear structures with fibrillar and granular composition were also detected in the first transgenic HD animal model, the R6/2 mouse, as documented in **Figure 2**, by conventional transmission electron microscopy. R6/2 mice express exon 1 of the human HD gene with 115–150 CAG repeats, develop symptoms very early with some features reflecting juvenile-onset HD and exhibit a widespread distribution of intranuclear inclusions in all brain areas [41, 42]. Next, in human HD brains, the presence of mHtt aggregates was confirmed in nuclei and axons by light- and electronmicroscopic mHtt- and Ubiquitinimmunohistochemistry [43]. Generation of the EM48 antibody, which is specific to N-terminal fragments of mHtt [44], confirmed and extended the localization of mHtt aggregates/inclusions in neuronal cytoplasm, dendrites, axons and synapses [45]. Since then, the presence of aggregates and/or inclusions became the characteristic hallmark for histopathology in human HD brains and the increasing number of small and large animal models. From the morphological viewpoint, the terms for aggregates and inclusions are heterogenously used and therefore confusing. Common descriptions are aggregates in the neuropil and inclusions localized intranuclear. Considering the heterogenous size and form in human HD brains [43], it is difficult to distinguish one from another. In this chapter, we try to use both simultaneously, if possible.

#### **Figure 2.**

*Transmission electron microscopy of a cortical pyramidal neuron in R6/2 mouse. (A) The intranuclear inclusion (INI) is a membrane-less structure clearly distinguished from the surrounding chromatin in the caryoplasm and the nucleolus (N). (B) Enlargement of the INI reveals loosely arranged fibrillar (white arrowheads) and granular structures. Postembedding Ubiquitin immunogold staining exhibits particles (15 nm) localized in the INI (black arrowheads), not in caryoplasm. Bar in A = 1 μm; bar in B = 0.5 μm.*

Studying mHtt aggregates/inclusions in the broad spectrum of HD animal models, it becomes apparent that the regional, cellular and subcellular localization is as diverse as the large number of HD models themselves. The use of different antibodies makes the assessment of comparable results still more difficult, furthermore, if strong retrievals are used prior to antibody incubation.

Nevertheless, it is undisputed that animal models are valuable to elucidate crucial aspects of underlying pathomechanisms, help to understand the neurological dysfunction and psychiatric alterations and are undispensable for the development of preclinical therapeutic approaches. The choice of an HD animal model will always depend on the underlying question. However, it has to be considered carefully to which extent the respective animal model could answer the respective question in human HD. To elucidate differences and similarities of mHtt aggregates, two established rodent models were presented here in more detail.

#### **3.1 Aggregates in R6/2 mouse**

The R6/2 mouse presents with behavioural and motor dysfunctions very early and shows severe other symptoms as progredient weight loss with affection of many peripheral organs leading to early death at 12–15 weeks of age [41]. According to their rapid and reproducible phenotype, they were early transferred to commercial breeding from where they are accessible by all interested scientists. The easy availability has also contributed to the fact that the R6/2 mouse has become one of the most extensively studied HD animal model.

Neuropathologically, the R6/2 mouse displays the greatest density of aggregates/ inclusions, which makes this model extremely valuable when aggregates/inclusions are used for follow-up studies and/or *in vitro* and *in vivo* investigations of the misfolded proteins themselves. Therefore, the R6/2 mouse with abundant aggregates, the early and severe symptoms and a short life span has become a standard model for testing preclinical therapeutic approaches.

#### *Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration… DOI: http://dx.doi.org/10.5772/intechopen.102828*

When investigating by conventional electron microscopy (**Figure 2**), intranuclear inclusions are easily detected in adult R6/2 mice in all brain areas inspected. As in human HD brains, the membrane-less intranuclear inclusion is clearly distinguished from the surrounding caryoplasm and comprises homogenously distributed fibrillar and granular structures (**Figure 2A** and **B**). Postembedding immunogold staining with Ubiquitin confirms intranuclear inclusion.

Immunostaining with EM48 antibody reveals an overall distribution of mHtt aggregates in all R6/2 brain areas. At cellular level, many neurons exhibit reactivity throughout the caryoplasm and a dense inclusion (**Figure 3A**). Immunoelectron microscopy confirmed nuclear distribution of EM48 reactivity loosely distributed in the caryoplasm and the dense intranuclear inclusion (**Figure 3B**). This observation extends the general assumption, that intranuclear mHtt is mainly localized as inclusion body. In R6/2 mice, the whole nucleus may harbour aggregates with varying expression. Nucleoli are always spared (**Figure 3B**). Of note, neurons with immunopositive caryoplasm show signs of degeneration as the irregular invaginated nuclear envelope starts to collapse indicating that mHtt may cause the cellular dysfunction finally leading to cell death (**Figure 3B**). The cytoplasm lacks mHtt reactivity. Single immunopositive spots were also detected in the surrounding neuropil. Taken together, the aggregates/inclusions in R6/2 mice are distributed across all brain areas, focussed on the caryoplasm and sparsely localized in the neuropil.

#### **Figure 3.**

*EM48-immunohistochemistry in the striatum of R6/2 mouse. (A) Vibratome section (50 μm) displays brown-stained nuclei (black arrowheads) many of which with a black inclusion body. Single positive spots are distributed in the neuropil (white arrowheads). (B) Transmission immunoelectron microscopy confirms EM48 reactivity in the caryoplasm (C) densely arranged in the intranuclear inclusion (INI). Nucleoli (N) are spared. Immunopositive nuclei show irregular nuclear envelope. Adjacent glial cell nucleus (G) lacks EM48 reactivity. Bar in A = 100 μm; bar in B = 1 μm.*

#### **3.2 Aggregates in the tgHD rat**

The transgenic rat model of HD (tgHD rat) carries a truncated htt cDNA fragment with 51 CAG repeats under control of the native rat promotor [46]. In contrast to the R6/2 mouse, the tgHD rat presents with slowly progressive motor and behavioural impairments reflecting the adult-onset phenotype of human HD individuals. Interestingly, the tgHD rat shares neuropathological similarities in regional

distribution and subcellular composition of aggregates with human HD brains. In the tgHD rat und in human HD brain, aggregates are focussed on the ventral striatum and the extended amygdala [47–49] areas that are crucial for elucidating psychiatric aspects of the disease. In the tgHD rat, detailed transmission immunoelectron microscopy detected that aggregates are localized in medium-sized striatal neurons as small patches in neuronal cytoplasm, mitochondria, myelinated and unmylinated axons, synaptic terminals and, most frequently, loosely distributed or as large compact inclusions in dendrites and dendritic spines [48].

Aggregates are also localized in the nucleus (**Figure 4**). In contrast to the R6/2 mouse, the tgHD rat caryoplasm only exhibits very few small EM48-positive spots and occasionally a single inclusion (**Figure 4A** and **B**). Signs of degeneration are rarely observed in the striatal neurons. In sum, the tgHD rat shows a more regional mHtt distribution focussed on basal forebrain systems. On subcellular level, aggregates/inclusions may be detected in many parts of medium-sized striatal neurons.

#### **Figure 4.**

*EM48 immunohistochemistry in the neostriatum of tgHD rat. (A) Transmission electron microscopy shows an intranuclear inclusion (INI) in a normal appearing medium-sized neuron of a 23 months old tgHD rat. Some positive spots (black arrowheads) are also detected in the neuronal caryoplasm, cytoplasm and dendrites (D). (B) At higher enlargement the INI exhibits fibrillar (white arrowheads) and granular structures. Ly, lysosomes; bar in A = 5 μm; bar in B = 1 μm.*

#### **3.3 Aggregates in human HD brains**

There are multiple studies on aggregates in HD animal models, but the localization of mHtt in human HD brains is less extensively investigated. In the studies of DiFiglia et al. and Gutekunst et al. [43, 45], important aspects of regional, cellular and subcellular aggregate localization were worked out in detail. Brains investigated included various Vonsattel grades as well as juvenile- and adult-onset HD brains. MHtt immunopositive intranuclear aggregates consistently called inclusion bodies by DiFiglia et al. [43] were more frequently detected in cortical layers of juvenile- than in adult-onset HD brains, in which they were predominantly detected in the neuropil in neuronal cell processes (called dystrophic neurites). The pronounced cortical localization in layers V and VI—especially in adult-onset HD individuals—was confirmed by Gutekunst et al. [45] for various cortical areas. Our investigations of

#### *Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration… DOI: http://dx.doi.org/10.5772/intechopen.102828*

selected frontal, parietal, temporal and occipital cortical areas by peroxidase EM48 immunohistochemistry also show that layers V and VI display the highest amount of aggregates/inclusions in varying degrees (unpublished results). The striatum, which is the first focus of studies in HD animal models, only displays a limited amount of aggregates in human HD brains, more expressed in the ventral than in the dorsal neostriatum [43, 45]. This observation was extended by our investigation, as we found aggregates/inclusions focussed to the accumbens and the extended amygdala [49], both functional-anatomical entities acting as interface between motor, limbic and olfactory-associated basal forebrain areas.

All human brains investigated in our cohort showed a heterogenous spectrum of aggregates differing in size, form and composition (**Figure 5A**). Confocal EM48 immunofluorescence counterstained with DAPI detected that most aggregates are localized in the neuropil, and only a few nuclei are associated with small positive spots (**Figure 5B**). Of note, it is relatively easy to localize aggregates in the nucleus and cytoplasm; however, the exact assignment to neuronal cell processes in the neuropil is difficult and awaits further detailed investigations using various techniques. One of the techniques to elucidate the fine structural composition of aggregates is transmission electron microscopy. Large inclusions often display an immunopositive rim with granular and vesicular structures and a mainly immunonegative core with densely arranged fibrillar structures (**Figure 5C**). In sum, in human HD brains aggregates/inclusions are predominantly localized in cortical areas, and—less expressed in selected basal limbic-associated forebrain systems. Localization of aggregates/ inclusions in many subcortical areas is less investigated and awaits further and more detailed investigation. Particularly, correlation studies between aggregate distribution and neurological dysfunctions are almost completely lacking in human HD.

A breakthrough to understand the mHtt structure at close to native cellular level was performed by the recently developed high-resolution cryo-electron tomography [50, 51]. This methods allows a three-dimensional imaging of cytosolic inclusions and aggregates [51]. Hela cells and mouse neurons transfected with GFP-tagged Htt exon 1 comprising 97 Q displayed inclusions which were identified by live cell imaging and further treated for cryo-electron tomography. Large mHtt inclusions are composed of organized centrally located fibrils, which interact with the membranes of the endoplasmic reticulum and deform their normal organization. This observation elucidates the subcellular machinery of mHtt aggregates and suggests a destructive effect of the inclusions. Comparing Bäuerlein's results with the large inclusions detected in the human HD brains investigated here by transmission electron microscopy (**Figure 5C**), it may look similar with the granular and vesicular structures in a more loosely arranged rim area and tightly packed fibrillar structures within the core. The question remains how far Bäuerlein's results reflect the broad spectrum of mHtt inclusions/ aggregates in the human HD brain. Nevertheless provide these results important insights into cellular impairments by mHtt inclusions/aggregates and are encouraging findings, which show that the interdisciplinary research on subcellular level is currently on the way to complement one another.

For therapeutic approaches that target lowering of mHtt levels in the brain, it is of major interest to develop non-invasive tools as biomarkers to visualize mHtt during disease progression. Imaging agents visible by positron emission tomography would be extremely helpful to identify and track mHtt distribution prior and during the therapy. Recently, a high-affinity Fluorine-18 radioligand was developed for imaging mHtt aggregates in HD animal models and also human post-mortem HD brain tissue [52]. This PET-imaging agent showed sufficient brain uptake in rodents and

#### **Figure 5.**

*EM48-immunohistochemistry in human HD brain (CAG 54/20). (A) Vibratome section (80 μm) displays distribution of mHtt aggregates varying in form and size localized in layer V and VI of the anterior cingulate cortex. (B) Confocal immunofluorescence of a cryosection (15 μm) with EM48 (green) counterstained with DAPI (blue) shows mainly neuropil aggregates. Some nuclei are associated with tiny positive dots (white arrowheads). (C) Transmission electron microscopy exhibits a large aggregate with an immunopositive rim (Ri) and a mainly immunonegative core (Co). Arrowheads mark the border of the neuropil structure harbouring the aggregate. M, mitochondrium; T, synaptic terminal; bar in B for A and B = 50 μm; bar in C = 1 μm.*

non-human primates to be monitored *in vivo*. Furthermore, autogradiography with the ligand displayed specific binding on human post-mortem HD brain sections, which may correspond to aggregate accumulation as further indicated by mHttimmunohistochemistry on adjacent sections. Even if such non-invasive studies are still at the very beginning, the application of radioligands as PET-imaging tracer in

*Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration… DOI: http://dx.doi.org/10.5772/intechopen.102828*

human HD brains to monitor alterations of mHtt localization would be of enormous benefit for controlling the course of therapy. It is also of crucial importance to carefully evaluate the validity of ligands as imaging biomarkers especially in mHtt-rich human brain areas, as localization of mHtt may be completely different from the before investigated animal model.

#### **4. Conclusions**

In HD, neurodegeneration is most expressed in striatal areas, but cortical and other noncortical areas are also severely affected including the grey and—especially in early HD—also the white matter. Notably, neurodegeneration is varying across the different HD brains, which may reflect the diversity in functional impairments of HD patients. For many brain areas, detailed information about macroscopic and microscopic affection is still limited and needs more specific interdisciplinary investigations to provide a better understanding of the regional neuropathology and related dysfunctions.

Aggregates/inclusions are the characteristic histopathological hallmark of HD. In HD animal models, the regional, cellular and subcellular localization is as diverse as the large number of models themselves with differently pronounced similarities to the human HD aggregation pattern. To date, the exact role of the Htt protein has not yet been clarified. The mechanism of formation and maturation of aggregates is currently intensively studied in living cell cultures providing first insights into the dynamic of mHtt and the toxic influence of aggregates and inclusion bodies. Furthermore, high-resolution techniques and improved tissue preservation are necessary to transfer the results on living cells to the aggregation process in human HD brains. So far, the controversial discussion about gain of function and toxicity in the interplay between aggregates and neurodegeneration is going on.

#### **Acknowledgements**

We are grateful to Anne Schlichting, Luzie Augostinowski, Katja Rumpf, Sabine Peuckert, Robert Nadgrabski, Claudia Schneider (Institute of Anatomy, Medical Faculty, Ruhr-University Bochum, Germany) for their excellent technical assistance.

The authorship criteria are listed in our Authorship Policy: https://www.intechopen.com/page/authorship-policy.

#### **Conflict of interest**

There are no conflicts of interest.

### **Author details**

Elisabeth Petrasch-Parwez1 \*, Hans-Werner Habbes1 , Marlen Löbbecke-Schumacher1 , Constanze Rana Parwez<sup>2</sup> , Carsten Saft<sup>3</sup> and Sarah Maria von Hein3

1 Department of Neuroanatomy and Molecular Brain Research, Ruhr-University Bochum, Bochum, Germany

2 Department of Neurology, St. Josef's Hospital, Ruhr-University Bochum, Bochum, Germany

3 Department of Neurology, Huntington-Center NRW, St. Josef's Hospital, Ruhr-University Bochum, Bochum, Germany

\*Address all correspondence to: elisabeth.petrasch-parwez@rub.de

© 2022 The Author(s). Licensee IntechOpen. 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.

*Neuropathology in Huntington's Disease: A Balancing Act between Neurodegeneration… DOI: http://dx.doi.org/10.5772/intechopen.102828*

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#### **Chapter 2**

## Neuropathology of Huntington's Disease

*Taylor G. Brown and Liam Chen*

#### **Abstract**

Huntington's disease (HD) is a devastating neurodegenerative disease that results in motor, cognitive, and psychiatric impairments. HD results from an autosomal dominant polyglutamine expansion in the *huntingtin* (*HTT*) gene that results in a misfolded and aggregated protein. The disease is uniformly fatal and demonstrates characteristic neuropathological changes. While the striatum is preferentially affected, the cortex and many other brain regions are involved in pathogenesis and show progressive changes throughout the disease.

**Keywords:** Huntington, neuropathology, degeneration, striatum, aggregate

#### **1. Introduction**

This chapter summarizes the current knowledge of the neuropathological changes that occur in Huntington's disease (HD). HD is an autosomal dominant neurodegenerative disease caused by a polyglutamine expansion in the *huntingtin* gene [1]. The mutation was discovered in 1993 [1], however cases were first documented in 1872 by George Huntington [2]. The disease is characterized by progressive motor, cognitive, and psychiatric impairments and is uniformly fatal [3–6]. Analysis of postmortem brains reveals global atrophy of approximately 19–30% with 29–64% and 23–29% reductions in basal ganglia and cortical volume, respectively [7–9]. HD is thought to preferentially affect medium spiny neurons (MSNs) of the striatum and lead to their degeneration, however the exact reasons why MSNs are so vulnerable is still unknown [7, 10–12]. In addition to the striatum, HD affects other brain areas and peripheral tissues as well, though many of these areas are comparatively less studied.

#### **2. Biological basis and symptomatology of HD**

#### **2.1 Biological basis of HD**

HD is caused by a polyglutamine (CAG) expansion in exon 1 of the *huntingtin* gene (*HTT*). Normal individuals have stable repeat lengths up to 26, whereas repeat lengths from 27 to 35 are potentially unstable. HD is associated with CAG repeats of 40 or more, with repeat lengths of 36–39 demonstrating incomplete penetrance [13]. The expanded CAG repeat produces a dysfunctional, unfolded, and aggregated huntingtin (HTT) protein, called mutant HTT (mHTT) [1]. The normal function of HTT is poorly understood, although some broad functions, such as roles in development, cell adhesion, and brain-derived neurotrophic factor transport and production, have been reported [14–16]. Not only does the expansion disrupt normal HTT functions, but it also exhibits toxic gain-of-function [16, 17]. While HTT is expressed in many cell types, medium spiny neurons (MSNs) of the striatum are particularly vulnerable to mHTT. While the exact mechanisms are still unclear, mHTT causes MSN death and leads to degeneration of the striatum [7, 11, 12].

#### **2.2 HD symptomatology**

HD causes motor, cognitive, and psychiatric deficits and the disease is uniformly fatal within a median time from motor symptom onset to death of 18 years [18]. The symptoms are severe and patients often lose independence rapidly, requiring constant care approximately 10 years after motor symptom onset [4]. The motor symptoms come in two broad categories: involuntary movements and impaired voluntary movement [3]. Involuntary movements such as chorea are common in the early stages of HD whereas the impaired voluntary movements, including coordination difficulties and bradykinesia, are often seen in later stages of the disease [4]. In addition, patients also have oculomotor abnormalities and dysdiadochokinesis among other motor symptoms [4]. Patients also experience cognitive symptoms, including personality changes, problems with attention and emotion recognition, cognitive slowing, initiation difficulties, and lack of awareness of deficits [4, 6]. Psychiatric symptoms include depressed mood, anxiety, apathy, irritability, social disengagement, and impulsivity [4, 5].

#### **3. Basal ganglia**

#### **3.1 Normal basal ganglia**

The basal ganglia is a set of subcortical nuclei located at the base of the forebrain [19]. This region is highly affected in HD [8] and thus, a review on its components, architecture, and circuitry is provided for context.

The basal ganglia is comprised of the striatum, globus pallidus (GP), subthalamic nucleus (STN), and substantia nigra (SN) [20]. The globus pallidus has two components, the internal and external segments (GPi and GPe respectively). The substantia nigra has two components, the pars reticulata (SNr) and the pars compacta (SNc) [19, 20].

The striatum gets input from many cortical areas, integrates the information, and sends it in multiple pathways throughout the basal ganglia [21]. The two major pathways are the direct and indirect pathways. The direct pathway is a monosynaptic pathway from the striatum to the GPi, whereas the indirect pathway is multisynaptic. The indirect pathway has projections from the striatum to the GPe, the GPe to the STN, and the STN to the GPi [19, 22–24]. The two pathways converge at GPi and send inhibitory projections to the ventral anterior and ventral lateral nuclei of the thalamus. Disruption or imbalance of these pathways can lead to motor dysfunction [25].

*Neuropathology of Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.106664*

#### *3.1.1 Striatum*

The dorsal striatum is comprised of the caudate and the putamen, while the ventral striatum is nucleus accumbens. Running between the caudate and the putamen is the internal capsule [26].

The striatum itself is a heterogenous region with two compartments—the matrix and the striosomes. The compartments differ based on their efferent and afferent connections as well as their neurochemical makeup. The striosomes receive inputs from the SNc, prefrontal cortex, and limbic system and they send outputs to the SNc [27]. The matrix compartment receives inputs from motor, somatosensory, frontal, parietal, and occipital cortices and the matrix sends outputs to GPe, GPi, and SNr [27]. The matrix strongly expresses acetylcholinesterase (AChE) whereas the striosomes only weakly stains for AChE [28]. Additionally, there are many other markers that can differentiate between striosome or matrix compartments, such as tyrosine hydroxylase or enkephalin [29].

The striatum contains multiple types of neurons and glial cells [3]. MSNs are GABAergic projection neurons that make up 90–95% of the striatal neuronal population [3, 30]. They receive glutamatergic input from many brain areas including the cortex and some thalamic nuclei [31–33] as well as dopaminergic input from the SN [27, 30]. MSNs that are in the direct pathway and project to the GPi express dopamine D1 receptors while MSNs in the indirect pathway express D2 receptors [22, 30]. Besides MSNs, the striatum also contains several classes of interneurons, the most abundant of which are large cholinergic interneurons [30, 34].

#### **3.2 HD striatum**

The dorsal striatum shows significant bilateral atrophy with striking caudate and putamen volume loss (**Figure 1A**) [7]. The degeneration typically occurs from the tail of the caudate to the head and body (caudal to rostral, dorsal to ventral, and medial to lateral) [36]. The particular sequence of degeneration is described further in Section 8.

Within the striatum, MSNs experience profound degeneration (**Figure 1B** and **C**) [3]. In general striatal interneurons are not very affected in HD [3, 37, 38], with the exception of parvalbumin-containing interneurons which degenerate significantly and in a grade-dependent manner [39]. Some cases of HD show rare but distinct, round striatal areas of preservation called islets. They measure 0.5–1.0 mm and show normal neuronal density but increased astrocyte density [40, 41].

Nearly all evidence suggests that cells displaying the classic apoptotic morphology are extremely rare in HD [42]. Remaining MSNs in HD brains appear to be smaller but maintain their normal somatic morphology [36]. Degenerating neurons, called "neostriatal dark neurons" appear darker than healthy neurons and have scalloped cellular membranes, granular dark cytoplasm, and condensed chromatin [29]. These dark neurons are typically present between atrophic and normal areas of the striatum [36]. Besides these atrophic neurons, ballooned neurons are extremely common in affected regions of HD brains. These neurons have enlarged, basophilic cytoplasm with flattened nuclei and Nissl substance and lipofuscin granules at the nuclear periphery [26, 43].

#### *3.2.1 Matrix and striosome*

The heterogenous, patchy nature of mHTT reactivity in the striatum of HD patients was observed and determined to reflect the distribution of striatal

#### **Figure 1.**

*Neuropathology of HD. (A) Coronal section of fixed brain at the level of the nucleus accumbens. Note HD grade 4 severe atrophy of the striatum and marked enlargement of the lateral ventricles. Normal (B) and grade 3 HD (C) putamen. Note severe neuron loss and astrocytosis. Immunohistochemical staining with 1C2 antibody against expanded polyglutamine of the TATA binding protein [35] demonstrates intranuclear inclusions, cytoplasmic granules (D and E) and neuritic aggregates (F) in cerebral cortex.*

compartments. In early studies of HD, HTT was predominantly found in the matrix [44]. Many reports focusing on neurodegeneration found either matrix [45, 46] or striosome [47, 48] predominance, and in particular, discrepancies between compartments were evident in early disease stages. Further investigation into this phenomenon revealed that matrix or striosome predominance correlated with symptom type, where HD patients who showed preferential neuronal loss in striosomes tended to experience more mood dysfunction and HD patients who showed preferential matrix neuronal loss showed mainly motor symptoms [49]. It should be noted that many brains do not show any matrix or striosome predominance, so this phenomenon seems to be ungeneralizable, and it is possible for either compartment to experience predominant neuronal loss.

#### *3.2.2 Indirect and direct pathway*

While some studies have shown no differences between indirect and direct pathway MSN degeneration [50], other studies have shown that indirect, D2 receptor-positive MSNs degenerate and show dysfunction prior to direct, D1 receptor-positive MSNs [10, 51–53]. This predominant neuronal loss of indirect pathway MSNs occurs early and by late disease stages, no difference between pathways is noted [51, 52].

#### **3.3 HD globus pallidus, STN, SN**

The GP shows atrophy in grades 3 and 4, with more significant atrophy of the GPe than the GPi [36, 40, 43]. GP volume is decreased significantly [54, 55], but the density of neurons is maintained, suggesting minimal neuronal degeneration [36, 40]. Reactive gliosis is seen in the GPe in late stages of the disease [36].

The STN shows marked atrophy in grades 3 and 4, but no reactive astrocytes are seen in this region [36, 54]. An approximately 20% reduction in STN neuron number are seen in HD brains compared to control brains [56].

The SN pathology differs by component [36]. There is a loss of neurons in the SNr, whereas the SNc is more controversial, appearing thin but with an unchanged number of neurons in some studies [57, 58]. Other studies, however, claim to see a reduction of neuron number in the SNc, although to a lesser degree than in the SNr [59, 60].

#### **3.4 Physiological and neurochemical changes**

The loss of MSNs likely causes reductions in NMDA, GABA, and cannabinoid receptor binding in the striatum of HD patients [49, 61–63]. In the GPe, enkephalin staining is diminished as enkephalin-containing MSNs projecting to the GPe are lost and in the GPi and SNr, substance P staining is diminished as substance P-containing MSNs are lost [10, 51, 52, 64]. In addition, urea levels in the brains of HD patients are elevated, even at early disease stages, which suggests disrupted urea metabolism may play a role in neurodegeneration or neurological impairment in HD [65]. Vitamin B5, the precursor for coenzyme A, is reduced in HD patient brains and may contribute to neurodegeneration, as genetic defects in the coenzyme A biosynthetic pathway lead to neurodegeneration [66, 67].

It is thought that excitoxicity and synaptic changes may play a role in cell death. Many studies using animal models of HD have shown a variety of factors, such as astrocyte dysfunction, defects in energy metabolism, or altered cortical input, that could contribute to the MSN vulnerability to excitotoxity [68–73]. HD animal models also show early loss of corticostriatal and thalamostriatal synapses [74]. In postmortem brain tissue from HD patients, it was found that bassoon, an anchoring protein in the active zone, and HTT interact and this process draws bassoon into aggregates, causing a reduction of active zone proteins which may impair synaptic function [75].

#### **4. Pathology in other brain regions**

In addition to the basal ganglia, HD affects other brain regions such as the cortex, thalamus, cerebellum, brainstem nuclei, subventricular zone, hypothalamus, hippocampus, and white matter. In general, striatal pathology severity correlates well with pathology in other brain regions, though striatal pathology often precedes pathology elsewhere [36].

#### **4.1 Cortex**

Cortical pathology is a significant yet disputed component of HD [36]. Early reports on HD pathology either saw significant cortical atrophy [76] or minimal to absent cortical pathology [77]. Currently, the consensus suggests that there is indeed cortical atrophy, cortical thinning, and cortical volume loss [8, 9, 78]. Exactly which layer of cortical neurons is most affected is still under investigation, as some studies have reported involvement of layers V and VI [79, 80] and others have reported layers III and V [81]. Layers III and V project to the striatum so it has been hypothesized that pathology here could be retrograde from the striatum, however layer VI involvement questions this. It is fairly clear, however, that pyramidal neurons degenerate more than cortical interneurons [81, 82], though there may be heterogenous involvement of cortical interneurons and it may differ by HD patient [29].

Particular cortical regions, such as the motor cortex [82, 83], prefrontal cortex [80, 84], entorhinal cortex [79], cingulate cortex [83], and primary sensory areas [85], have been studied and each shows HD pathology, though it appears to vary between HD patients [86]. It is possible that degeneration and thinning of these particular areas may correlate with specific HD symptoms. One study showed that cases with significant motor cortex pathology showed profound motor dysfunction whereas cases with anterior cingulate cortex pathology showed predominance of mood symptoms [83, 87].

#### **4.2 Cerebellum**

Cerebellar involvement in HD is rather controversial. The cerebellum plays roles in motor coordination and control, attention, and many other processes [88–90]. Earliest reports of HD neuropathology did not note any cerebellar pathology [77], however recent studies have varied in their assessment of cerebellar atrophy, volume loss, and degeneration [3, 36, 91]. Vonsattel and others reported that the cerebellum displayed normal neuronal density but was atrophied in late stage HD brains [36, 92]. However, other studies were reporting the density of Purkinje cells were reduced by half [3]. A systematic study using serial sections of the cerebellum in HD brains showed widespread loss of Purkinje cells and degeneration of neurons in the deep cerebellar nuclei present at early stages of HD [91]. Interestingly, when HD patients are separated by symptom predominance, those with predominant motor symptoms had significant loss of Purkinje cells whereas those with predominant mood symptoms did not show any loss of Purkinje cells in the neocerebellum [93]. This cerebellar pathology is reminiscent of that seen in spinocerebellar ataxias (1, 2, and 3), and it is possible that HD has more similarities with these diseases than previously thought [94–96]. Classic cerebellar dysfunction signs such as gait abnormalities, dysarthria, oculomotor abnormalities, and fine motor skill impairment have indeed been reported in HD [3, 97–99].

#### **4.3 Other brain areas**

HD cases appear to show varied levels of thalamic pathology [100]. The thalamus often appears grossly normal but does show pathology in later stages, though this varies by thalamic nucleus [40]. The thalamus appears normal at early disease stages but at late disease stages, astrocytosis and neuronal loss are seen in the centromedial nucleus [36]. Atrophy has been reported in the centromedial/parafascicular nucleus, the dorsomedial nucleus, and the centromedial/ventrolateral nucleus group [100–102].

The hypothalamus is another area that shows HD pathology [55, 103, 104]. HD patients present with sleep disturbances, altered circadian rhythm, and weight loss *Neuropathology of Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.106664*

[105–107]. Atrophy, gliosis, and 90% cell loss of the lateral tuberal nucleus has been reported [3]. Other studies have shown a loss of orexin-positive and somatostatinpositive neurons in the lateral hypothalamus [108–110].

Early HD reports did not observe changes in hippocampal density [77], however more recent studies have found a 20% reduction of area [9], 9% reduction of volume [111], and neuronal loss and astrocytosis [36] in the hippocampus. It seems that changes in neuronal density may be restricted to the CA1 region [112].

The subventricular zone (SVZ), which is a region that contains adult stem cells and is located at the edge of the caudate nucleus, shows thickening that progresses with increasing grades of HD [113]. There was increased cell proliferation [113] and altered lipid architecture [114] in the SVZ of HD patients as well.

White matter changes occur in HD and the loss of white matter correlates with amount of gray matter lost [9]. Diffusion tensor imaging and MRI show presymptomatic white matter changes in the microstructure of the corpus callosum and internal capsule [115, 116].

Lastly, brainstem nuclei show pathology in HD. The brainstem shows widespread neuronal loss with particular involvement of the substantia nigra, precerebellar pontine nuclei, inferior olive, oculomotor reticulotegmental nucleus, premotor oculomotor area, raphe interpositus nucleus, auditory superior olive, and the vestibular nuclei [26, 117, 118]. HD patients present with autonomic disturbances and oculomotor dysfunction [4, 119], some of which could be explained by brainstem pathology.

#### **5. Aggregates**

Expanded HTT accumulates and forms aggregates and inclusions (**Figure 1D**–**F**) [120–122]. Normally, HTT is found mostly in the cytoplasm, dendrites, and axon terminals, but in HD, inclusions are also seen in the nucleus [123]. There are both intranuclear and extranuclear inclusions in HD [124] and they appear round or oblong, ranging from approximately 0.5–20 μm in diameter [123]. mHTT inclusions are found in neurons most commonly but also astrocytes, oligodendrocytes, and microglia [125, 126]. They are found more commonly in gray matter than white matter [123]. The aggregates can be detected prior to symptom onset [127].

A high density of aggregates are homogenously distributed throughout the striatum of HD brains [123]. No distinction between striosome and matrix compartments were noted in terms of aggregate load [123]. There is a high aggregate load in layers V and VI of the cortex as well [123]. Specifically, the insular and cingulate cortex had a high density of aggregates while other cortical areas and the SN, thalamus, hypothalamus, brainstem nuclei, GP, hippocampus, and cerebellum showed a much lower density [123]. Interestingly, mHTT aggregates are also found in the olfactory bulb of HD patients, though aggregate load here does not correlate with Vonsattel grading score [128].

Polyglutamine proteins can be detected using antibodies that recognize polyglutamine stretches, such as IC2. IC2 is a monoclonal antibody that predominantly binds to pathologic repeat lengths and can therefore detect mHTT [35]. It has consistently been used to detect mHTT aggregates in postmortem HD brains [129, 130]. Other studies have used EM48, an antibody that detects the N-terminal region of HTT, to detect HTT aggregates [123].

#### **6. Pathology in peripheral tissues**

HTT is expressed in many tissues and organs outside of the brain [105]. Besides the classic neurological symptoms, HD patients exhibit weight loss, atrophy of skeletal muscle, cardiac dysfunction, testicular atrophy, impaired glucose tolerance, and osteoporosis [105]. While the symptoms have been documented in patients [105, 131, 132], most of the research in peripheral tissue pathology is in HD animal models.

Postmortem samples from HD patients showed testicular atrophy and spermatogenesis deficits, with fewer spermatocytes and spermatids. Additionally, thicker walls and cross-sectional area of the seminiferous tubules were noted. In their study, the patient with the longest repeat length had the most severe testicular pathology [133].

Skeletal muscle atrophy is another hallmark of HD [134]. Muscle cells express mHTT and show inclusion bodies in animal models of HD [135, 136] and in muscle cell cultures from HD patients [137]. In addition to aggregate formation, cultured cells from HD patients also show mitochondrial abnormalities [137–139], and the two may work together to cause muscle wasting in HD.

Cardiac failure is relatively common among HD patients, as it occurs in about 30% of cases [132]. Cardiac tissue expresses HTT and while mHTT inclusions are seen in HD mice [140], no aggregates have been reported in cardiac tissue from HD patients [136]. Altered autonomic input to the heart [141], calcium dysregulation [142], and conduction abnormalities [111] are all seen in HD patients and could contribute to heart conditions.

The exact mechanisms that cause peripheral pathology are not fully understood. It is likely that cells that express aggregates are affected cell-autonomously to at least some extent, but the contribution of brain-derived hormones, signals from affected brain areas, or the degeneration of autonomic nerves is still being uncovered.

#### **7. Gliosis**

Gliosis is a significant part of HD pathology [143]. Microglia, astrocytes, and oligodendrocytes all show changes in response to HD [7, 36, 144–146]. The density of oligodendrocytes increases in the striatum of HD brains [36] and is particularly evident in early disease stages [127, 144].

Reactive microglia are seen in the striatum, cortex, and globus pallidus of HD patients in all grades of pathology and their number correlates with the degree of neuronal loss in the striatum [145, 146]. Positron emission tomography scans indicate that progressive microglia activation is also seen in the anterior cingulate cortex and prefrontal cortex [147]. In fact, this microglia activation was even seen in presymptomatic HD patients [148].

In addition to microglia, astrocytes play an important role as well. Glial fibrillary acidic protein (GFAP)-positive astrocytes are a component of the Vonsattel grading system (below) [7]. GFAP-positive astrocytes have traditionally been viewed as the reactive type, although the complexity of astrocyte heterogeneity and reactivity is still being uncovered [143, 149–152]. GFAP-positive astrocyte number in the striatum increases progressively with disease severity and striatal neurodegeneration [7]. Despite cortical atrophy and pathology, no astrocytosis was noted in prefrontal cortex samples from HD patients [84, 143].

#### **8. Grading**

The Vonsattel grading system was developed in 1985 and utilizes both macroscopic and microscopic pathology in the striatum to categorize the severity of HD degeneration [7, 26, 36].

Grade 0—These brains show no gross abnormalities despite clinical evidence of HD. There may be up to 30–40% reduction in neuron number in the head of the caudate, though no reactive astrocytes are present at this stage.

Grade 1—Macroscopically, there is mild atrophy of the caudate tail and body. The head of the caudate and the putamen may still appear normal. Microscopically, the neuronal loss and astrocytosis is predominantly in the tail of the caudate nucleus with lesser involvement of the caudate body and head and the nearby putamen.

Grade 2—Macroscopically, some atrophy of the caudate is seen with resulting enlargement of the lateral ventricles, although the ventricular surface maintains its convex shape. Microscopically, significant neuronal loss and reactive astrocytosis is seen in the dorsal parts of the caudate and putamen. The globus pallidus begins to show degeneration, with the GPe degenerating before the GPi.

Grade 3—Macroscopically, there is significant atrophy of the caudate, causing the ventricular surface to appear straight as it now parallels the internal capsule boundary. Microscopically, the neuronal loss and reactive astrocytosis is visible throughout the caudate and putamen and becomes severe. This pathology progresses dorsal to ventral, rostral to caudal, and medial to lateral in the striatum. Mild pathology is present in the nucleus accumbens.

Grade 4—Macroscopically, there is severe atrophy of the striatum causing the ventricular surface to become concave. Microscopically, severe striatal neuron loss reaches approximately 95% and there is severe astrocytosis. The globus pallidus volume is reduced by half and the nucleus accumbens may begin to show more significant pathology.

#### **9. Juvenile HD**

Juvenile HD occurs when disease onset manifests before 20 years of age [4, 153, 154], 75% of juvenile HD patients have inherited the mutation from their father. Paternal inheritance is associated with increased likelihood of repeat-length expansion, leading to earlier onset in the next generation, referred to as "anticipation" [155]. It is difficult to diagnose because it often presents with minimal chorea. Instead, behavioral symptoms are more prominent than motor symptoms at such early ages [4] and the motor symptoms that do prevail are typically rigidity and bradykinesia [3]. Contrary to adult-onset HD, juvenile cases are prone to seizures as well [3, 153].

Juvenile HD patient brains typically show more severe striatal pathology than adult HD brains [3]. Magnetic resonance imaging (MRI), magnetic resonance spectroscopy, and postmortem brains of juvenile HD patients show severe and early striatal volume loss accompanied by reduced neuronal density but no significant cortical or white matter involvement [156]. Interestingly, juvenile cases of HD show more islets than adult cases [40]. In general, neuronal intranuclear inclusions are more common in juvenile than adult HD [123]. Aggregate load in the striatum is also more significant in juvenile HD brains [157].

Compared to adult onset HD, juvenile HD brains show severe cerebellar atrophy [36, 158]. In one case study involving a father with adult onset HD and a son with

juvenile HD, significantly more cerebellar pathology, including mHTT inclusions in cerebellar neurons, was seen in the juvenile case than the adult case [155]. In addition to the cerebellum, the GPi, thalamus, and nucleus accumbens are also more severely affected in juvenile HD patients [157, 159]. The frontal and parietal regions show gross atrophy and MRI analysis showed more widespread and faster cortical volume loss in juvenile cases compared to adult onset cases [157, 160].

#### **10. Developmental changes**

While HD is being increasingly recognized as a developmental disease, few neuropathological studies of developing brains with adult onset HD exist. Using tissue from HD carrier fetuses, it was found that HTT is mislocalized in ventricular zone progenitor cells, which disrupts the neuroepithelial junctional complexes and interkinetic nuclear migration [161]. This causes progenitor cells to prematurely enter into lineage specification [161]. Using imaging approaches, it has been shown that there is an absence of Sylvian fissure asymmetry, which occurs early in development, in the brains of HD patients [162]. Early genetic testing has allowed for imaging studies in children well prior to HD onset. In children with expanded repeats as young as 6 years old, increased connectivity between the striatum and other brain regions is evident [163]. Additionally, imaging has shown that these children have larger striatal volumes early in life, but more rapid decline in volume through aging [164].

Developmental malformations are not uncommon in HD patients. A recent study using a large cohort of autopsy brains found that developmental malformations were found approximately 6 times more frequently in HD-brains than in non HD-brains, with heterotopias being the most common malformation, though other asymmetric and solitary malformations were also seen [165].

#### **11. Conclusion**

HD is a complex neurodegenerative disease that involves multiple brain areas. In fact, it has taken decades to firmly establish that HD is not only a basal ganglia disorder, but rather affects many regions in a symptomatically relevant manner. Therefore, the neuropathology of HD is constantly being re-evaluated and studied. It is clear, however, that this disease causes significant striatal atrophy and neuronal loss with concomitant cortical changes that result in devastating motor, cognitive, and psychiatric consequences for HD patients.

#### **Conflict of interest**

The authors declare no conflict of interest.

*Neuropathology of Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.106664*

#### **Author details**

Taylor G. Brown and Liam Chen\* University of Minnesota, Minneapolis, USA

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

© 2022 The Author(s). Licensee IntechOpen. 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.

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#### **Chapter 3**

## Exploring Biomarkers for Huntington's Disease

*Omar Deeb, Afnan Atallah and Sawsan Salameh*

#### **Abstract**

Huntington's disease (HD) is a progressive, non-curative, autosomal dominant neurodegenerative disease characterized by prominent psychiatric problems, as well as progressive deterioration in both cognitive function and motor control. The success of therapeutic interventions in HD patients cannot be easily examined without reliable and practical measurements by using effective biomarkers. Many clinical trials have been held to evaluate biomarkers efficacies in disease-modifying treatment before the manifestation of the disease or its severity. Biofluid (wet) biomarkers have potential advantages of direct quantification of biological processes at the molecular level, imaging biomarkers, on the other hand, can quantify related changes at a structural level in the brain. The most robust biofluid and imaging biomarkers are being investigated for their clinical use and development of future treatment and can offer complementary information, providing a more comprehensive evaluation of disease stage and progression.

**Keywords:** Huntington's disease (HD), biomarkers, clinical biomarkers, wet biomarkers, imaging biomarkers, premanifest, manifest

#### **1. Introduction**

Huntington's disease (HD) is an inherited disease that causes breakdown of nerve cells in the brain. HD, resulting from gene mutation, affects different parts of the brain impacting movement, behavior, emotion regulation, and psychiatric disturbance. Eventually, the person will need full-time care, and death of the disease is inescapable. HD is caused by an expanded trinucleotide cytosine-adenine-guanine (CAG) repeat in the huntingtin gene. HD is one of the rare neurodegenerative conditions for which predictive genetic testing is available for individuals with a known family history [1]. The identification of HD gene mutation carriers, while they are still healthy before manifestation (premanifest) of clinical signs of the disease has several benefits as this may help prevent the development or slowdown of the progression of the disease, hence, improved quality of life of the patient.

HD symptoms can develop at any time, but they often start at 30–50 years of age. If the condition develops earlier, before the age of 20, the symptoms start with behavioral disturbances and learning difficulties. Because of this, there is an urgent need to diagnose the disease as early as possible using biomarkers and assess the development of the disease. This can be achieved by identifying a number of biomarkers that are altered either premanifest or during the disease progression.

The unified Huntington's Rating Scale (UHDRS) has been used as a clinical rating scale to assess four domains (motor function, cognitive function, behavioral abnormalities, and functional capacity) of clinical performance and capacity in HD patients. However, one of the main challenges in using this rating scale is the slow progression of HD, rendering the scale imperfect as a standalone tool [2] leading, in some cases, to limitations of clinical trials that aiming to assess the benefits of therapeutic agents in HD. In addition, there are several factors that could influence the clinical measures including the placebo effect and the clinical rater variability. This results in reduced ability indistinguishing between symptom relief and amelioration of the underlying disease process [3].

Finding biomarkers that change with clinical progression quickly and predictably with the use of a therapeutic agent could greatly facilitate future HD clinical trials by reducing the duration and number of patient volunteer required for such studies. This is especially important in premanifest HD mutation carriers, who may remain free from all clinical manifestations for decades. In addition, pharmacodynamic biomarkers can be utilized in preclinical trials and early phase clinical trials to predict if the therapeutic agent will have its intended effect and to assist in the decision-making process on whether to continue such trials or not.

Up to date biomarker research has included both focused small-scale and large studies. For instance, TRACK-HD (a prospective observational study of HD that examines disease progression in premanifest individuals carrying the mutant HTT gene and those with early-stage disease and those who have had it for 12 months or less) [4], and PREDICT-HD (a multicenter observational research study aimed to examine measures that may be associated with disease in the largest cohort ever recruited of pre-diagnosed individuals carrying the gene expansion for HD) [5], have afforded scientists in the field the opportunity to study many potential biomarkers for HD.

#### **2. Biomarkers**

The term biomarker is defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" [6]. Therefore, a biomarker can be related to the disease itself or to the response to treatment. Hence, biomarkers can serve many different functions, such as diagnostic, prognostic, monitoring, response/pharmacodynamic, susceptibility/Risk Biomarker [7, 8]. In addition, most therapeutic clinical trials that aim to evaluate the efficacy of potential disease-modifying treatments during pre-manifest HD require biomarkers to serve as outcome measures. Some efficacy biomarkers may also function as 'state biomarkers' or 'biomarkers of progression', which are used as indicators of disease severity. These state biomarkers could very well reflect the underlying disease pathobiology and linearly track clinical progression of the disease (including during the pre-manifest stage) [1].

The biomarkers that have been used, hitherto, in HD are of types including, clinical biomarkers, wet biomarkers, and imaging biomarkers. Detailed discussion of these biomarkers and their subtypes is presented below.

#### **2.1 Clinical biomarkers**

Rather than relying on United Huntington's Disease Rating Scale (UHDRS's) dichotomous notion of 'disease onset', some researchers have proposed the use of *Exploring Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.103840*

continuous measures, such as clinical symptoms of the disease. Some UHDRS motor abnormalities can be objectively quantified, thereby improving accuracy and reducing inter-rater and intra-rater variability [1].

Tabrizi et al. have supported the hypothesis that neuronal dysfunction occurs many years before the development of motor signs that are diagnostic of HD. The motor alterations that have been described are most likely secondary to progressive neuronal loss or dysfunction. This could help define quantifiable endpoints for future therapeutic interventions [9]. Motor signs are amenable to quantitative assessment and may provide objective measures for disease onset and progression. Several quantitative motor tasks, including force-transducer-based assessments, detect deficits in premanifest gene carriers [10], for example, finger tapping precision and a problem task is evident even in pre-manifest HD and worsen with time. Another longitudinal study identified numerous cognitive task impairments in more than one variable, one of these variables is the Symbol Digit Modalities test which assesses visual attention and psychomotor speed [1, 10, 11] suggesting the limited use of clinical markers in preventive clinical trials.

#### **2.2 Wet biomarkers**

Wet biomarkers also called biofluids, (those obtained from bodily fluids, such as blood, urine, saliva, and cerebrospinal fluids (CSF)) are another potential source of useful outcome measures if they reflect the pathophysiology of a disease and show the response for a therapeutic agent.

In HD, various pathologic mechanisms have been implicated and numerous potential molecular markers have been detected. Progression of the disease have been reported to be associated with detectable changes in inflammatory signals in peripheral blood which matched changes in peripheral and central processes such as immune activation, neuroinflammation, and metabolic markers [12, 13]. In some cases, substances that are released from dying neurons that can penetrate the bloodbrain barrier can be detected in peripheral blood and could be used as a biomarker. Unfortunately, however, if these substances have peripheral sources, conflict in interpretation may occur [14]. Cerebrospinal fluid, which is enriched with brain-derived substances is of particular interest, however, other biofluids have the potential to yield relevant biomarkers if their composition reflects that of the CNS. Consequently, all biofluids, including CSF, may reflect peripheral as well as central disease-related changes [3].

#### *2.2.1 Mutant huntingtin protein*

Huntington's disease (HD) is caused by a cytosine-adenine-guanine (CAG) trinucleotide repeat expansion in the huntingtin gene (HTT), which leads to the production of the mutant huntingtin (mHTT) protein. The degree of symptom severity, disease stage, and markers of neuronal damage have been shown to correlate with levels of mHTT protein in the CSF in patients with HD. This toxic mHTT protein production is believed to result in neurotoxicity, as normal cellular processes important for cellular survival are disrupted. Furthermore, decreased level of mHTT is an important measure of the response to the therapeutic agents. mHTT quantification has been achieved for the first time in 2015 using a `femtomolar` single molecule counting (SMC) immunoassay, and a combination of mHTT N-terminal-detecting 2B7 antibody and polyglutamine-binding MW1 antibody. mHTT was significantly

higher in manifest HD and premanifest HD compared to controls with a roughly threefold difference seen between premanifest HD and manifest [3, 14].

mHTT detection is associated with disease onset and cognitive and motor function disability. mHTT quantification in CSF could potentially serve as a biomarker for the development and testing of experimental mHTT-lowering therapies for HD [15]. mHTT levels also correlate with clinical manifestations as well as with two indicators of neuronal damage (CSF tau and neurofilament light chain) [14] suggesting that mHTT is released from damaged or dying neurons.

#### *2.2.2 Neurofilament light and tau protein*

Neurofilament light protein (NfL, also known as NFL) is the smallest of three subunits that make up neurofilaments, which are major components of the neuronal cytoskeleton. NfL is released from damaged neurons. Its concentrations in CSF are elevated in people with neurodegenerative diseases.

Detection of NfL in the CSF using enzyme-linked immunosorbent assay (ELISA) reflects that NfL concentration is elevated in both premanifest and manifest HD. This elevation is associated with mHTT elevation in CSF, disease stage, motor and cognitive impairment, functional impairment and brain atrophy, as well as reduction in all brain volume measures [14, 16–18].

Also, NfL is detectable in blood plasma or serum using a single-molecule 'Simoa' assay. It has been shown to increase in blood of people with neurodegenerative diseases including HD [14, 16]. Quantification of NfL in plasma provides an accessible biomarker that has close links to diagnosis, progression of HD and the response to disease-modifying treatments. Also, NfL in both plasma and CSF is considered a better biomarker to differentiate between premanifest and manifest HD than CSF mHTT [3, 17–19].

Tau protein (a microtubule-associated protein, which aggregates abnormally under certain pathologic conditions) is another protein that is hypothesized to be associated withHD. It has been found that CSF tau concentration in HD gene mutation carriers is increased compared with that of healthy controls. It has also been reported that CSF tau concentrations are associated with phenotypic variability in HD. This report strengthens the case for CSF tau as a biomarker in HD [20].

#### *2.2.3 Inflammatory markers*

Activation of glial cells has been reported in several neurodegenerative diseases including HD. Biomarkers reflecting these peripheral and/or central neuroinflammation could be useful to identify the disease onset, progression, and the therapeutic response. Proteomics screen of HD plasma identified immune proteins that are elevated in HD compared to healthy controls, including pro-inflammatory cytokine IL-6, acute-phase protein alpha-2-macroglobulin, complement factors, and a complement inhibitor clusterin. Additionally, it has been found that IL-6 levels were significantly increased in premanifest subjects with an estimated mean of 16 years before motor signs onset [8, 12, 16, 21].

Another marker that has also been studied as a CSF inflammatory marker in HD is YKL-40 (chitinase 3-like protein 1 (CHI3L1)), a member of the glycosyl hydrolase family 18 and a marker of microglial activation. The results about this marker are mixed [3, 14, 16].

#### *2.2.4 Metabolic markers*

Weight loss and muscle wasting are examples of some disorders that appear in patients with HD reflecting metabolic alterations in those patients. Several metabolites were tested as potential biomarkers for HD. In addition, several amino acids were tested as potential biomarkers. It has been reported that plasma levels of asparagine (Asn) and Serine (Ser) were significantly decreased suggesting a potential biomarker role for these two amino acids [22].

Studies conducted on the association of total cholesterol, HDL-cholesterol and LDL-cholesterol with HD revealed mixed, and in some cases, contradictory results. Whereas most studies showed that changes in cholesterol levels were insignificant, one study showed that reductions in cholesterol levels were significant in premanifest and manifest patients [23]. In another study, 24(S) hydroxycholesterol (24OHC), the brain-specific elimination product of cholesterol long considered a marker of brain cholesterol turnover, was significantly reduced in HD patients at all disease stages. This reduction was paralleled with a reduction of the caudate volume suggesting that the reduction of 24OHC may reflect progressive neuronal loss in HD patients. In addition, a decrease in the plasma concentration of cholesterol precursors` lanosterol and lathosterol was observed [8, 24, 25]. These results suggest the potential usefulness of these two cholesterol precursors as metabolic biomarkers in HD diagnosis and progression.

#### *2.2.5 Neuroendocrine markers*

Patients with manifest HD display circadian rhythm abnormalities with disturbances in rest-activity profiles and abnormal day-night ratios associated with alterations in sleep-wake timing and melatonin and cortisol profiles [26].

Melatonin is a light-sensitive hormone secreted predominantly by the pineal gland and displays a circadian rhythm with maximum levels peaking at night. It has a key role in the sleep-wake cycle which is disrupted in the early stages of HD. A significant decrease in mean melatonin levels has been reported in manifest HD, with trends towards decreased melatonin levels in premanifest HD and temporal shift in melatonin release in mHTT carriers. Altered melatonin patterns may provide an explanation for disrupted sleep and circadian behavior of HD patients acting as a biomarker for this disease state [3, 26–29]. While there were no differences in melatonin release when it was measured at a single time point in advanced HD, differences in melatonin release were detected when measured at multiple time points. This suggests the need to measure melatonin levels at points representing the whole circadian rhythmicity [8].

Cortisol is another substance that plays a role in circadian rhythm as it has been observed that increased cortisol levels lead to sleep disturbances, which are likely to potentiate neurodegeneration and associated changes in cognitive, motor deficits and mood disturbances in HD [27, 30].

With markers that have specific circadian rhythms, 24-hour sample collections could be the means to using these markers as pharmacodynamic markers to assess the response to the treatment rather than the progression of the disease [3].

#### *2.2.6 Oxidative stress markers*

Both human and animal studies have suggested the involvement of energy metabolism dysfunction and oxidative stress in HD pathogenesis as it has been shown that

levels of oxidative damage products, free radical production are elevated in areas of degeneration in HD brain [31]. It is thought that impairment in the electron transport chain and mitochondrial dysfunction are behind the increased production of reactive oxygen species in HD [32–34]. Markers of oxidative stress have been investigated in HD blood plasma and brain tissue in the animal model, but few have been quantified in CSF.

Several studies have reported enhanced lipid peroxidation in individuals with HD with a correlation between lipid peroxidation products in plasma and the degree of severity in patients with HD. It has been reported that F2-isoprostanes are a marker for lipid peroxidation found to be elevated in HD [3, 8, 14].

#### *2.2.7 Endogenous opioid peptides*

The endogenous opioid peptides have been found to be implicated in the regulation of motor function as well as in the pathophysiology of abnormal movement disorders. Degeneration of opioid peptide-containing neurons in the basal ganglia has been demonstrated in some neurodegenerative diseases such as HD [35]. Recently, it has been found that CSF proenkephalin (PENK) levels were significantly decreased in manifest HD patients compared to premanifest. The decrease in PENK CSF levels in premanifest patients was insignificant when compared to controls. Moreover, levels of PENK in the CSF is inversely proportional to the progression of HD symptoms. This decrease in PENK levels reflects the degeneration or dysfunction of neurons that produce PENK, consequently, PENK levels may serve as marker for the state of medium spiny neurons (MSNs) in HD patients [36].

Prodynorphin (PDYN) is another endogenous opioid that has been studied in HD. It has been found that PDYN-derived peptide levels were significantly decreased in CSF of patients with HD. This decrease is unique to HD as a comparable decrease was not observed in the other neurodegenerative disorders studied. These results suggest that PDYN-derived peptides in CSF could be considered as strong biomarker candidates for HD [37].

#### *2.2.8 MicroRNAs*

The microRNAs (miRNAs) are involved in different biological processes including development, proliferation, inflammation and apoptosis. miRNA is an intracellular component but also can be detected in the peripheral circulation. The level, structure, type and sequence of miRNAs detected in blood will reflect the physiological status, the type and stage of the disease [38]. The detection of abnormal expression of different miRNAs in the HD mouse model provides further support regarding the importance of miRNA in HD pathogenesis and therapeutics [39], and the potential usefulness of miRNAs as biomarkers for diagnosis, prognosis, and therapeutic response [38].

#### *2.2.9 Exosomes*

In the central nervous system (CNS), exosomes play essential physiological roles in the cell-to-cell communication and homeostasis maintenance required for normal brain function [40]. Exosomes contain a variety of key bioactive substances reflecting the status of the intracellular environment. As exosomes can penetrate the blood-brain barrier they can be found in peripheral body fluids, and their contents

#### *Exploring Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.103840*

will change with diseases [41]. Most cell types in the brain release extracellular vesicles (EVs) and these have been shown to contain neurodegenerative proteins. In HD, by using a model culture system with overexpression of HTT-exon 1 polyQ-GFP constructs in human 293 T cells, it has been found that the EVs did incorporate both the polyQ-GFP protein as well as the expanded repeat RNA. These findings support the role of EVs as delivery vehicles of toxic expanded trinucleotide repeat RNAs from one cell to another [42]. Exosomes have a huge potential as non-invasive diagnostic biomarkers of HD for their content of mHTT, its fragments, or other proteins reflect the conditions of exosomes producing CNS cells [40].

#### **2.3 Imaging biomarkers**

In HD, neuroimaging techniques have been extensively investigated and have aided in our understanding of the disease's natural history. Imaging is attractive as a source of biomarkers because it is generally non-invasive; data collecting, processing, and quality control can be standardized, and data can be easily sent over great distances, which is advantageous for multi-site investigations. The ideal imaging biomarker would be widely available, reasonably priced, and repeatable across multiple sites using different scanner manufacturers and field strengths and have a reasonable acquisition time - especially since HD patients may not tolerate longer scanning protocols and movement that degrades image quality.

Structural MRI, diffusion imaging, functional MRI, and PET are just a few of the imaging modalities available. There are a variety of image processing algorithms for each modality, and the approach chosen can have a big impact on the output metrics that are used as biomarkers. Some automated procedures, for example, can generate mistake and systemic bias, especially in atrophic brains [43]. To avoid difficulties, extensive validation of the acquisition and processing technique is essential before such measures may be used as a biomarker, which has been absent in many imaging investigations to date.

#### *2.3.1 Structural volumetric MRI*

Structural MRI (sMRI) is a non-invasive technique that provides information to describe the shape, size, and integrity of gray and white matter structures in the brain. MRI results emphasized that there are strong correlations between many gray and white matter regions and clinical tests, including recognition of negative emotions, metronome tapping precision, and measures of tongue force. The latest findings point of sMRI data enables to collect information from across the brain during the premanifest to manifest period in HD. The data show that no uniform atrophy occurs throughout the brain (**Figure 1**), where the largest changes (~18–22%) occurring in the striatum (caudate, pallidum, and putamen) and gradual changes (~7–16%) occurring across the four main brain regions (parietal, temporal, frontal, and occipital) over a period of ~11 years [44]. This timeframe is similar to prior studies of the timing of sMRI alterations in HD [45], according to which the rate of putamen and caudate atrophy becomes substantial roughly 9 and 11 years after estimated onset, respectively [44].

When used as a clinical trial endpoint, the rate of change of a proposed biomarker can influence the length of the study and the number of participants required to identify a meaningful change. There is no agreement on whether the pace of striatal atrophy progression differs with disease stage. TRACK-HD found stepwise

#### **Figure 1.**

*RACK-HD cohort. Average magnitude of change of ten regional volumes from genotype-positive trajectories in TRACK-HD [44].*

accelerated rates of change from the earliest premanifest stage to early-stage disease, with limited evidence that the acceleration diminishes after symptoms appear [4, 46]. After controlling for age, TRACK-HD found highly significant relationships between the rate of change and disease burden ratings in both the caudate and putamen. The PREDICT-HD study, on the other hand, did not discover that rates accelerated across its premanifest group, but this could be due to differences in longitudinal change assessment methodology [47].

Studies for regional Cortical found in HD patients reported a heterogeneous volume loss [4, 9, 46, 48–50], where the cortical thinning occurs early during the clinical stage of disease and seems to increase with disease progression. The reported thinning of the cortical gray was clear in posterior cortical regions, with increasing duration of symptoms, more anterior cortical regions were affected. The reported data suggest that the cortex undergoes degeneration, much of which occurs in the striatum particularly in the early premanifest stage of the disease [46, 48, 51]. Cortical thinning was distributed in many areas, even within gray regions. In some areas the thinning was as much as 0.4 mm which corresponds to approximately a 20% loss of thickness whereas in other areas, thinning was around 1 mm, corresponding to 30% loss of thickness (**Figure 2**) [49, 53–57].

The Cross-sectional studies have reported volume reductions in the corpus callosum [5] and frontal white matter (**Figure 3**) [9, 52, 59]. In premanifest HD, both TRACK-HD and PREDICT-HD showed progressive white matter atrophy, even in the groups farthest from anticipated onset [9, 46, 60]. In manifest disease, a similar picture has been observed, with cross-sectional reductions in white matter volume compared to controls [4, 9, 61–63], and elevated atrophy rates in longitudinal studies [52, 64]. White matter atrophy has been shown to correlate with motor function [47, 59, 65, 66], cognitive function [59, 65, 67] and total functional capacity (TFC) [47, 68]. White matter volume

*Exploring Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.103840*

#### **Figure 2.**

*(A) Mean thickness maps. The surface reconstruction demonstrates mean thickness differences of three different subjects with Huntington's disease (HD) in differing stages of the disease. Darker gray areas correspond to sulci; lighter gray areas correspond to gyri [52].*

loss' prognostic value for manifest HD conversion is less evident, with inconsistent findings in two large observational investigations [48, 69]. White matter atrophy, on the other hand, does track disease progression and is present from the earliest premanifest stage through established disease.

#### *2.3.2 Functional MRI*

There is mounting evidence that the severity of clinical manifestations in HD is influenced not just by neuronal loss but also by neuronal dysfunction and circuitry rearrangement, and that these processes can occur early in the disease, possibly even before neurodegeneration. By monitoring the hemodynamic response (blood flow) of neural activation, functional neuroimaging methods such as functional MRI (fMRI) produce dynamic images of the brain that aid in elucidating neural activity. Data from manifest HD patients revealed decreased task-activation in multiple subcortical and cortical regions, as well as increased task-activation in various cortical areas, which was interpreted as a compensatory mechanism for task performance [70–75]. Interestingly, premanifest HD gene carriers who were further away from illness onset showed increased activation in multiple brain regions, whereas premanifest HD gene carriers who were closer to disease onset showed lower activation in the striatum [76–79].

Both premanifest and manifest HD gene carriers have exhibited intrinsic deficits in functional connectivity in resting-state fMRI data [80–82]. Reduced bloodoxygen-level-dependent (BOLD) synchronization between the caudate and premotor cortex was reported in premanifest HD gene carriers [80]. A study found several abnormal networks in both premanifest and manifest HD subjects using a method that measures changes in synchrony in BOLD signal amplitude and across space.

**Figure 3.**

*Tracts showing lower fractional anisotropy in Huntington's disease gene carriers when compared with controls. Results (red-yellow [lower to higher statistical values]) are projected on a white matter skeleton (green), overlaid on a customized mean fractional anisotropy image [58].*

For example, it has been found that premanifest HD gene carriers had lower restingstate synchronization in the sensory-motor network and that this level of synchrony was related to motor performance as determined by speeded self-paced tapping [83]. Overall, these data demonstrate aberrant functional network connection in both

#### *Exploring Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.103840*

premanifest and manifest HD, implying that resting state fMRI could be valuable for detecting early neural dysfunction and tracking disease progression.

Premanifest HD gene carriers have also been discovered to have neurovascular changes. Cortical arteriolar cerebral blood volume (CBVa) was significantly elevated in premanifest HD gene carriers compared to normal controls, which was connected with genetic parameters including the CAG-age product score and estimated years to onset [84].

#### *2.3.3 Diffusion MRI*

Diffusion MRI assesses the microstructural integrity of white matter filaments, providing additional information to volumetric MRI. The diffusion of water in different directions within the brain is measured using this technique. Water diffusion in healthy white matter fibers is usually only in one direction, making them anisotropic. When white matter breaks down, for example, due to axonal injury or demyelination, diffusion increases in directions other than the axons. Diffusion MRI might, in theory, reveal neuronal injury or dysfunction that occurs before volumetric loss.

The most widely-studied diffusion technique in HD is diffusion tensor imaging (DTI). Across various neurodegenerative diseases, reductions in fractional anisotropy (FA) and increases in mean diffusivity (MD) are commonly observed [85–87] indicating their sensitivity but lack of specificity to the underlying neurodegenerative process. Axonal loss, demyelination, and less cohesive white matter tracts are thought to be the cause of these abnormalities, which would be expected to occur before volume loss.

A Diffusion metric change has been observed in premanifest HD in cross-sectional investigations, particularly in the corpus callosum, internal capsule, and thalamic radiations [88–91]. These alterations in the white matter, particularly the frontal, parietal, and occipital white matter, become increasingly pronounced and extensive in manifest HD [92–94]. The results of longitudinal studies using diffusion metrics have proved inconclusive. Two studies in premanifest HD failed to find 12–30 month changes [89, 95], whereas two larger studies found progressive changes over 1–5 years in premanifest HD cohorts including those up to 10 years away from onset [90, 96]. In manifest-HD, longitudinal alterations in DTI measures have also been demonstrated [51].

Changes in regional DTI measurements have been linked to total motor score (TMS), timed finger tapping, executive function [80], apathy [97], and depression [98]. However, no research has looked into the effectiveness of DTI measurements in predicting clinical development. Furthermore, DTI measurements had smaller impact sizes than volumetric measures in a comparative investigation across periods of 6–15 months [99] limiting the use of DTI as a biomarker of HD progression.

Recent advances in diffusion acquisition and modeling techniques, such as the use of neurite orientation dispersion and density imaging (NODDI) methods (**Figure 4**), have the potential to improve the sensitivity of diffusion MRI measures in HD [100–102]. However, there is currently a lack of agreement on diffusion imaging acquisition parameters, processing, and analysis procedures, which accounts for some of the variance in findings to date.

#### *2.3.4 Positron emission tomography (PET)*

The use of PET in the diagnosis and understanding of neurological pathologies is crucial. It is a non-invasive molecular imaging technology that uses

**Figure 4.** *White matter abnormalities: Neurite orientation dispersion and density imaging (NODDI) analysis [100].*

radiopharmaceuticals to attach to a specific molecular target, such as a transporter or receptor, after crossing the blood–brain barrier, allowing accurate tracking of changes in their function. PET now has a wide range of radiolabeled biomarkers for neuroimaging in psychiatry and neurodegenerative diseases like Parkinson's disease (PD), Alzheimer's disease (AD), and Huntington's disease (HD).

PET has been used in HD to investigate metabolic markers of hypo-metabolism, dopaminergic function, microglial activation, and the expression of the PDE10A enzyme [103]. However, similar research have been conducted in small numbers, with mixed results. PET scanning is also more expensive than volumetric or diffusion MRI, generally is less available for large multicenter studies, and is more invasive because it uses ionizing radiation. PET, on the other hand, has the advantage of being able to provide more detailed information about pathological processes, and a future use of PET as a biomarker for target engagement in smaller proof-of-concept or phase 1 trials is in the horizon. PET was recently used to demonstrate effective target engagement of a new PDE10A inhibitor after a single dosage, paving the way for continued clinical development into a phase 2 trial [104]. Amyloid PET has shown promise in both experimental and clinical studies of Alzheimer's disease [105] and a ligand capable of binding a pathogenic form of mutant huntingtin protein could be a useful PET biomarker for relevant pathology and regional brain tissue target engagement in huntingtin lowering studies [106].

#### **3. Artificial intelligence and machine-learning techniques**

Computational methods such as machine learning techniques are very useful tools in helping and improving the diagnosis as well as the disease monitoring process. A recent review study [107] concentrated on artificial intelligence in neurodegenerative diseases such as Huntington's disease and others in which the authors reviewed the available tools with focus on machine learning techniques. Many authors have concentrated on Huntington's disease alone using artificial intelligence and machine learning techniques [108–110]. More details on using artificial intelligence and machine learning techniques in the diagnosis and monitoring of Huntington's disease will be reviewed alone later in a future publication.

#### **4. Conclusion**

As Huntington's disease is not a preventable or curative disease, the availability of a diagnostic, prognostic, or response biomarker will have significant importance *Exploring Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.103840*

either in premanifest or manifest stage. Reliable biomarkers are needed either to delay/prevent the appearance of symptoms, slow the progression of the disease, and/ or to monitor response to the therapy.

The identification of imaging and other measurements that have the ability to monitor and predict disease progression and therapy response has recently progressed in HD biomarker research. The most promising of them appear to be suitable for providing target engagement and efficacy readouts in premanifest HD or at short intervals. Such biomarkers may be verified as surrogate endpoints or even in the clinical context to guide prognostic discussions and treatment decisions in HD in the future as viable medicines become available. This promise will be realized through ongoing efforts to standardize methodology and reproduce findings in large-scale cohorts.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Omar Deeb1 \*, Afnan Atallah<sup>2</sup> and Sawsan Salameh1

1 Faculty of Pharmacy, Al-Quds University, Jerusalem, Palestine

2 Faculty of Medicine, Al-Quds University, Jerusalem, Palestine

\*Address all correspondence to: deeb.omar@gmail.com

© 2022 The Author(s). Licensee IntechOpen. 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.

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#### **Chapter 4**

### Neuroimaging Biomarkers for Huntington's Disease

*Nadine van de Zande, Eidrees Ghariq, Jeroen de Bresser and Susanne de Bot*

#### **Abstract**

Biomarkers are of great importance in the prediction of onset and follow-up of patients with Huntington's disease (HD). Neuroimaging is a convenient biomarker, because of its non-invasive character. Since technology is continuously evolving, we are increasingly able to visualize detailed neural structures and functions. Furthermore, it could also identify new targets for therapeutic interventions. In this chapter, we review findings in neuroimaging research applied to HD. First, we will describe the neuroanatomical structures and cellular processes, which are important in the pathophysiology of HD and are therefore particularly interesting to focus on. We will then discuss the different imaging modalities; from structural to functional, from commonly used to novel imaging strategies. Striatal- and cortical-volume loss on conventional MRI and decrease in uptake of radiotracers on PET are currently the most robust markers of disease progression. The use of other MRI-metabolites, specific PET radioligands, DTI, and fMRI may have the potential to detect HD pathology earlier and more accurately but needs further investigation. These neuroimaging markers, possibly combined, can be useful clinical outcome measures in clinical trials and could improve the management and treatment of future patients.

**Keywords:** neuroimaging, biomarkers, Huntington's disease, MRI, fMRI, PET

#### **1. Introduction**

Huntington's disease (HD) is an autosomal dominant inherited neurodegenerative disorder caused by an expansion of a CAG repeat in the huntingtin gene. Therefore, confirmation of carrier ship of an expanded CAG repeat is achieved by a genetic test, which can be performed years before disease onset [1]. The clinical diagnosis and follow-up of the disease are obtained by standardized clinical scales like the Unified Huntington's Disease Rating Scale (UHDRS) [2]. This assessment consists of a specific neurological exam (e.g. 'Total Motor Score', TMS), Total Functional Capacity (TFC), and neuropsychological assessments. During the neuropsychological assessments cognitive, psychological, and psychiatric information is obtained. Clinical diagnosis in research and in clinic is grounded on a clinician's rating of the diagnostic confidence level. The clinical diagnosis requires a level of 4, on a 0–4 scale, completely based on the motor features of HD. Although these assessments are standardized

and well-known, they have their limitations. In particular, the subjective nature of these assessments contributes to a high inter-rater variability [3]. They also show low sensitivity to longitudinal change and monitoring of treatment effects [4]. There are often many observable clinical and functional changes before motor onset. Since the clinical diagnosis is not made until motor symptoms appear, there is a large gray area where symptoms already exist without being officially classified as 'manifest'.

At this moment, there is no successful disease-modifying therapy for HD. Nevertheless, research trials are constantly evolving, hoping to find solutions in the near future. Sensitive biomarkers are of great importance in these clinical trials. These are valuable not only for accurate group selection, or deciding when to start therapy, but are also important for evaluating treatment response, which is currently measured by the earlier mentioned clinical scales. The Biomarkers Definitions Working Group defines a biomarker as 'a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention' [5]. A biomarker must be widely available, reliable, reproducible and it should show low variability in the normal population. Besides that, it has to change in relation to disease progression and disease-modifying treatment. An ideal biomarker should be able to predict disease onset and should be obtained in a minimally invasive manner.

Neuroimaging biomarkers are convenient, as they are relatively non-invasive and can provide an accurate picture of the pathophysiology. Currently, there are different types of neuroimaging methods, sequences, and tools available and many of them have been used in observational studies of patients with HD. In this chapter, we summarize the results of different neuroimaging modalities that have been used in HD research. Furthermore, we will discuss the qualities and the limitations of these modalities and appraise which type of methods we should favor for future clinical trials. Before moving to a discussion of the neuroimaging modalities, we first elaborate on the pathophysiology of HD to determine the relevant neuroanatomical structures and cellular processes that neuroimaging modalities should focus on.

#### **2. From neuropathology to scan**

HD is a neurodegenerative disease caused by CAG repeat expansion in the huntingtin gene (HTT) on chromosome 4. The number of CAG repeats is associated with an increased accumulation of abnormal huntingtin protein in the neurons. When the CAG-repeat rises above a threshold of 36 repeats, this leads to cumulative toxicity [1]. The CAP score, a statistical prediction tool developed by Penney and colleagues, is an index to *estimate* the degree of cumulative exposure to the effects of the CAG repeat expansion. The score considers the length of repeats and the lifetime exposure to the disease burden. It can be used to predict the clinical status of HD expanded gene carriers (HDEGC), as it has proven to be a good predictor of HD pathology in post-mortem brains [6]. In general, it is believed that the longer the CAG repeat, the earlier the age of disease onset [7]. However, CAG repeat length is certainly not the only predictor of clinical outcome, as similar CAG repeat lengths can lead to many different manifestations of the disease (e.g. variable age of onset) [8].

An important part of the pathology of HD is the accumulation of mutant huntingtin protein to form intranuclear inclusions, which subsequently leads to loss of GABAergic medium spiny neurons (MSNs) in the neostriatum [9, 10]. This includes the caudate nucleus and the putamen, but also other regions of the basal ganglia, such as the globus pallidus. Multiple studies have shown this striatal atrophy, making it the hallmark of HD's pathology [11, 12]. Degeneration of inhibitory MSNs leads to

#### *Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

hyperactivity of the dopaminergic pathway, contributing to chorea [13]. As the pathology progresses, neuronal degeneration spreads to the cortex and other extrastriatal regions [12, 14, 15]. All four cerebral lobes undergo cortical thinning with a layerspecific neuronal loss [16]. Cerebellar symptoms are quite common in HD, including dysarthria and ataxic movements of extremities. Although less studied, recent studies also show a reduction of the cerebellar volume [17]. Considering all these findings, HD is now being viewed as a multisystemic disease [16]. Further research in this area is needed. There is no current explanation why these cortical and subcortical brain regions are selectively affected, ultimately leading to HD-specific neurodegeneration.

Disease-specific cellular processes can be investigated using neuroimaging techniques like magnetic resonance spectroscopy (MRS), iron sensitive MRI, and PET-scans. The loss of post-synaptic striatal MSNs results in a decline of postsynaptic dopaminergic neurons. Phosphodiesterase (PDE) is an enzyme that seems to play a role in the pathophysiology of HD. This is an intracellular enzyme that plays an important role in cell signal transduction and in promoting neuronal survival. PDE10A is predominantly expressed in the striatum and has an essential role in regulating dopaminergic signaling. In mutated HD models, it has been shown that mutant huntingtin decreases striatal PDE10A expression [18] and a decreased PDE10A level has been identified before onset of symptoms [19].

Besides the dopaminergic receptors, there are several other receptors suggested to be involved in HD pathophysiology. One of them is the GABA receptor. Studies have shown a decreased GABA receptor density in the caudate, putamen, and the frontal cortex in post-mortem HD brains [20]. In the globus pallidus an increased level of GABA receptors was found [21]. Another common receptor that seems to play a role in HD pathophysiology is the adenosine type 1 receptor, which plays a role in neuroprotection and autoregulation of cerebral blood flow [22]. Cannabinoid type 1 receptors (CB1R) are expressed in the basal ganglia, predominantly in the GABAergic striatal MSNs. They seem to be important for motor and cognitive function and play a protective role against excitotoxicity and promote neuronal survival [23, 24]. Transgenic HD mouse models showed decreased levels of CB1R [25] in both premanifest and manifest stages, with a further decline during the manifest stages [26].

Iron accumulation [27], synaptic dysfunction [28, 29] as well as mitochondrial dysfunction [30] are other mechanisms of HD pathophysiology. Iron accumulation has been correlated with aging and increased accumulation has been found in several neurodegenerative diseases like HD [27]. It is currently unknown whether iron accumulation is a cause or a consequence of neurodegeneration. Iron accumulates in microglia, as has been shown in HD [31]. Microglia cells are also involved in neuroinflammation, another probable pathological process in HD. Activated microglia cells have a neuroprotective effect, but overactivation can result in neuronal damage due to toxic levels of free radicals, nitric oxide and interleukins. Activated microglia have been found in the neostriatum, globus pallidus, cortex, and subcortical white matter in post-mortem human brain tissue in HDEGC [32]. It remains unclear whether this activation is a compensatory mechanism in response to the loss of neurons, or whether microglia activation itself is the cause of pathophysiology.

#### **3. Magnetic resonance imaging**

Magnetic Resonance Imaging (MRI) is an imaging method where a magnetic field, magnetic gradients and radiofrequency pulses are used to change the state of hydrogen atoms in the brain. These changes create energy which is subsequently measured and exported in the form of an MR image [33].

There are different approaches in methodology when analyzing a scan. There is a region-of-interest (ROI) approach and a whole-brain analysis approach. The first one is driven by known pathologically affected structures, the latter is more exploratory, unbiased and does not require a priori assumptions. ROI studies can be useful to study specific hypotheses since they often show high sensitivity for detecting differences between groups and demonstrating longitudinal change. The delineation of ROIs can be performed manually or by using automated software. Although manual delineation of ROIs is the gold standard, it is very time consuming and is susceptible to inter- and intra-rater variability. The automated software can define ROIs on a more consistent level across studies and is therefore likely to be used more frequently in future studies. However, there is an increased level of error to include some of the surrounding tissue in the analysis while working with these automated software methods. ROI approaches are used in volumetric imaging as well as diffusionweighted imaging (DWI) [34].

Whole-brain-analysis enables exploratory analysis across all brain regions. It has been widely used in HD studies, for example, to measure brain volume. Voxel-based morphometry (VBM) is the most commonly used approach. With this approach volumetric differences in the gray matter, white matter, and cerebrospinal fluid (CSF) can be measured between different groups. It can also be used to make associations between volume and other biomarkers [35]. Cortical thickness is another wholebrain approach, widely used in HD studies. In DWI a whole-brain analysis can be performed by using Tract-Based Spatial Statistics (TBSS). This compares diffusion metrics across the brain. Another type of analysis that is used in DWI is tractography. This is used to measure diffusivity between two or more regions of interest [36].

#### **3.1 Volumetric MRI**

Structural or volumetric MRI scans can be used to measure anatomical features of the brain, such as volume and cortical thickness. They are usually 3D T1 weighted sequences, after which volume measurements are made using software packages [37]. Each body tissue has a different relaxation time that is dependent on how tightly bound the protons are in their environment. Volumetric MRI studies were the first and are still the most common in vivo imaging studies in HD research.

MRI-based brain volume measurements have been introduced in HD at the beginning of the 1990s. The most common finding is striatal atrophy. Harris et al. were the first to discover a volume loss in the putamen and caudate, comparing 15 symptomatic HDEGC with 19 healthy controls [38]. In 1996, they were able to discriminate manifest HDEGC from healthy controls using volume measurements [39]. In the early years of structural MRI research, Aylward et al. did multiple studies showing that striatal atrophy was already present years before clinical motor diagnosis [40–42]. Contemporary scientists have confirmed these findings, showing volume loss up to 24 years before clinical motor onset [43–45].

Longitudinal studies show reducing striatal volumes with decreasing time to estimated diagnosis, with striatal volumes markedly reduced compared to age-matched controls at the time of clinical motor diagnosis [46–48]. In the past years there have been four large multicentre studies, aimed at identifying sensitive and reliable biomarkers. TRACK-HD is one of these studies, following 120 premanifest and 123 early manifest HDEGC longitudinally [43, 49, 50]. After a 12-month follow-up, they

measured a mean volume loss of 1.4% and 2.9% in the caudate, and 2.3% and 4.5% in the putamen, compared with baseline for the premanifest and manifest HDGC group, respectively [49]. After a 24-month follow-up, this atrophy progressed in both groups [4]. IMAGE-HD, another multicentre longitudinal study, showed that longitudinal volume change in the caudate was the only measure among a range of multi-modal imaging features that discriminated between groups across different disease stages (e.g. >15 years from clinical motor onset, <15 years from clinical motor onset, and after clinical motor onset). Caudate volume showed statistically bigger longitudinal change than putamen volume, over 30 months [51]. This larger difference in caudate volume loss compared to putamen atrophy was confirmed by another multicentre study called PADDINGTON [48].

While striatal atrophy can already be detected years before onset, cortical atrophy becomes more apparent after clinical motor diagnosis [52–54]. Atrophy in the frontal lobe has been identified in the moderate and late stages of HD. Volume reductions have been identified in almost all brain structures, including the total cerebrum, cerebral cortex, basal ganglia, amygdala, hippocampus, brainstem, and cerebellum [52, 53]. Another study showed an association between increased losses of gray matter volume in the occipital, parietal, frontal and insular cortices, and disease progression [55]. However, in the TRACK-HD study whole brain and gray matter atrophy were already found in premanifest HDEGC with <10.8 years from predicted symptomatic onset. In the premanifest HD group >10.8 years from predicted symptomatic onset atrophy was limited to the striatum, the white matter surrounding the striatum, the corpus callosum, and the posterior white matter tract [50]. Volume loss in total brain matter and white matter progressed after a 12-month follow-up [49]. The fourth large longitudinal multicentre study, PREDICT-HD, showed volume loss in total brain, white matter, cortical gray matter, thalamus, caudate, and putamen volume in premanifest HDEGC when compared to controls. Striatal volume, especially the putamen, showed the largest loss of volume [46, 47, 56]. White matter atrophy has been identified in more studies, showing volume loss early in the disease, continuing to decline with disease progression [43, 44, 48, 49, 54]. One recent imaging study using both PET and MRI, found significant volume loss in caudate, putamen, and pallidum in premanifest HDEGC. In the early manifest HDEGC, they also found significant atrophy in the thalamus, occipital and frontal cortex, and whole gray matter [57].

Cortical thinning has also been found in early manifest HD, affecting the sensorimotor areas, the occipital, and prefrontal cortices [58–60]. Thinning of the cortical gray matter can be detected before clinical diagnosis, becoming more pronounced and proceeding from posterior to anterior regions as the disease progresses [43, 53, 59, 60].

There is overwhelming evidence showing associations between brain volume loss and clinical outcome measures, showing a decline in performance with reducing volumes. Striatal atrophy has been associated with predicted time to clinical disease onset, age of onset, disease duration, and an increasing CAP score [40, 47, 61]. After a 36-month follow-up, the TRACK-HD study showed progressive whole brain, caudate, putamen, and gray matter atrophy in early manifest HDEGC which correlated to a decreasing TFC score. In the premanifest group with >10.8 years from predicted motor onset, striatal atrophy was not associated with a decline in motor and cognitive performances [50]. This was confirmed by a recent multimodal imaging study, where they also found significant striatal atrophy in premanifest HDEGC which did not correlate to clinical measures [62]. In the TRACK-HD study, the premanifest group within 10.8 years from predicted motor onset did show a significant decline of motor and

cognitive performances. Besides this, TRACK-HD showed that striatal and gray volume measures were sensitive predictors of conversion from the premanifest to manifest HD stage [50]. Furthermore, at the most advanced disease stage (7 ≤ TFC ≤ 10) the caudate volume showed a constant rate of decline over the 12-, 24-, and 36-month follow-up periods. Whole-brain and caudate volumetric MRI measurements have a substantially better power analysis than standard clinical outcome measures used in current clinical trials (TMS and TFC). They only need one-sixth of the sample size to detect the same degree of slowing [63]. The PADDINGTON study, which looked at longitudinal changes in early manifest HDEGC compared to healthy controls, found more MRI changes than changes in clinical outcome measures [64].

In premanifest HDEGC and early HD manifest patients subtle impairments were correlated with regional brain volume, especially in the caudate, putamen, and globus pallidus [65–67]. Regional cortical atrophy has also been correlated with MMSE, TFC, and motor scores [52, 60]. Regional cortical thinning was found to be correlated with cognitive decline [68, 69], depression [69], and TMS [70]. Atrophy in the precentral and parieto-occipital regions correlated with TFC, clinical motor, and cognitive scores [71–73]. Associations have been found between atrophy in the caudate and worse outcome scores on the mini-mental state examination (MMSE) [39], TMS, SDMT, and TFC [74]. A voxel-based morphometry showed an inverse correlation between the TMS and the concentrations of caudate nuclei tissue, internal capsule, occipital lobes, cerebellum, lower brainstem (corrected for age and CAG repeat length) [75]. Increased atrophy in the putamen also correlates with motor impairment [38, 39]. Thalamic atrophy was found to be associated with apathy [76]. Atrophy in the thalamus, insula and white matter volume has been associated with cognitive performance scores in both pre-manifest and manifest HD groups [77, 78].

It can be concluded that volume loss occurs many years before the development of motor signs that mark the clinical diagnosis of HD, starting with striatal atrophy and concomitantly spread over the gray and white matter. To study the microstructural changes that could explain neuronal loss and to find approaches for disease-modifying treatments, other imaging modalities are necessary.

#### **3.2 Diffusion tensor imaging**

DWI is a newer MRI technique that has been extensively used in HD research for the past two decades. It is based upon the diffusion properties of protons in the intraand extracellular space. In an unrestricted space, water molecules can move in any direction, which is called isotropic movement. When the path of the water molecules is restricted, such as along a white matter tract, water diffuses in an anisotropic way. Diffusion tensor imaging (DTI) is a specific type of DWI that enables a more precise assessment of the direction of diffusion. DTI allows measurement of the orientation, strength, and directionality of the diffusion of water molecules. The measures that can be derived from DTI are mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). MD represents the speed of diffusion, where a low MD value represents a restricted diffusion and a high MD value an unrestricted diffusion (e.g., CSF). FA represents the strength of the main direction of diffusion. FA values range from 0 to 1. When the FA value is close to 0 there is an equal diffusion in all directions, as is the case in CSF. AD is the diffusion rate along the main axis of diffusion and an increase in AD reflects axonal degeneration and loss. RD is the rate of diffusion in the transverse direction, where an increase in RD reflects the demyelination of white matter tracts [79].

*Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

The advantage of DTI is that it can detect microstructural changes, which precedes the larger changes that appear on a volumetric MRI image [80]. This applies especially to the white matter, due to the white matter tracts.

As with structural MRI, diffusion imaging has been used in large multicentre imaging trials like TRACK-HD, TRACK-ON HD, PADDINGTON, IMAGE-HD, and PREDICT-HD. These large MRI datasets are important to improve the technical standardization and statistical power of clinical DTI studies in HD.

Widespread diffusion abnormalities have been demonstrated in both manifest and premanifest HDEGC [81]. Multiple studies have shown a decreased FA and increased AD, RD, and MD in a wide range of white matter regions for HDEGC compared to healthy controls [82–91]. Among these studies three of them included symptomatic HDEGC and healthy controls [87, 89, 91] and three studies compared premanifest HDEGC and controls [83, 84, 90]. There have been many more studies showing a decrease in FA, of which Reading and colleagues were the very first. They showed lower FA values in the precentral cortex of premanifest HDEGC [92]. The TRACK-ON HD study, a longitudinal study of 72 premanifest HDEGC over 24 months, identified that cortico-striatal connections were most affected, followed by interhemispheric and intrahemispheric connections [93]. The corpus callosum has been studied frequently in DTI-studies, probably due to its easy access and well-organized pattern of the pathway. In 2010, Rosas et al. found an increase in AD and RD in fibers of the corpus callosum for the first time, suggesting a cortical disconnection between the prefrontal cortical regions [94]. Also the basal ganglia, the surrounding white matter tracts between these regions and the cortex have been analyzed thoroughly using DTI. There have been studies looking at both white matter and gray matter, showing an increase of FA in the gray matter and a decrease of FA in the white matter for HD patients compared to controls [95–103]. Other studies showed that diffusion imaging could be used as a method to differentiate the subgroup close-to-onset premanifest HDEGC from far-from-onset HDEGC. They showed an increased MD in close-toonset-HD, compared to far-from-onset and healthy controls [104, 105].

There are also studies with contradictory results. Syka and colleagues did not find any significant difference between FA and MD in the pallidum of symptomatic HDEGC compared to healthy control [106]. Poudel et al. and Matsui et al. have found increased RD without a difference in AD in HDEGC, suggestive of myeline pathology [107, 108].

The longitudinal IMAGE-HD study showed a progressive increase in MD located in the caudate, putamen and corpus callosum of premanifest HDEGC after 18 and 30 months follow-up [51, 109]. Sampedro and colleagues studied 39 HDEGC in different stages and showed correlations between MD and disease progression [110]. Other longitudinal diffusion studies did find cross-sectional differences, but did not find significant longitudinal effects in the included groups [70, 88, 111–113].

Diffusion studies have also tried to correlate diffusion to different phenotypes, clinical signs and genetic variables. Changes in diffusion have been linked to increased impairment in neuropsychological performance [69, 70, 98] and motor assessments [69, 70, 114]. For example, Bohanna et al. showed a positive correlation between motor symptoms and an increase in diffusion in the corpus callosum [91]. Philips and colleagues showed correlations between diffusion and clinical and genetic variables, such as CAG and cognitive assessments [115]. Microstructural dissociation in WM tracts has been associated with depression [69], apathy [116], and irritability [117]. Combining both volumetric and diffusion data sets, Georgiou-Karistianis and his group could accurately detect individuals up to 15 years before onset of symptoms, making it a valuable biomarker [118].

In summary, it seems that MD is increased in widespread regions of the brain and FA is reduced in white matter regions when comparing HDEGC to controls. This difference already begins in the premanifest phase of the disease and increases with disease progression. These results indicate an important role in white matter disorganization in HD.

#### **3.3 Magnetic resonance spectroscopy**

MRS is an MRI technique that can look on a microscopic level at the pathophysiology. It uses protons, like hydrogen protons, to measure metabolite concentrations. This modality does not give structural information about the brain tissue, but enables an interpretation of the chemical composition of the tissue. The most common metabolites are N-acetylaspertate (NAA), which is a marker for neuronal and axonal integrity; creatine, reflecting brain energy metabolism; choline, a glial marker; glutamate, a neurotransmitter; lactate, a product of anaerobic glycolysis; and myo-inositol, an astrocyte marker [119]. To date, no previous studies have shown a pathognomic alteration of any metabolite for HD [120]. In manifest HDEGC lower NAA and creatine have been measured in the putamen and/or caudate nucleus [121–125] and thalamus [126]. Adanyeguh et al. found a significantly higher total creatine in the visual cortex and a significantly lower total creatine in the striatum, in manifest HDEGC compared to controls [127]. This decreased level of total creatine has also been found in premanifest HDEGC in some studies [121, 125], of which one was part of the multicentred TRACK-HD study [121]. Other studies did not show a decreased level in premanifest HDEGC [128, 129]. MRS studies looking at glutamate levels in HDEGC did not demonstrate consistent results either. Some of these studies found increased levels of glutamate [130–132], others found no difference with healthy controls [121, 122] or found a lower level compared to controls [121, 127]. Choline was found to be decreased in the frontal cortex of premanifest HDEGC [128] and the NAA/choline ratio was found to be decreased in the frontal cortex of manifest HDEGC [133]. Furthermore, increased myo-inositol was measured in the putamen of manifest HDEGC [121] and increased lactate has been assessed in the basal ganglia, cerebellum and occipital, parietal, and frontal cortex of manifest HDEGC [123, 124, 133, 134]. Correlations have been made between alternations in metabolites and motor performance [121, 125], neuropsychological performance [128], disease severity [133], and disease burden [121]. So far there have been two longitudinal MRS studies, both showing no longitudinal change of metabolites in their follow-up [135, 136].

#### **3.4 Iron sensitive MRI**

Various MRI techniques have been used, trying to assess iron accumulation. One of these techniques is the use of relaxation rates R2 and R2\* when using the T2 and T2\* sequences, respectively. This susceptibility measurement cannot distinguish between paramagnetic or diamagnetic entities and therefore cannot conclude whether there is iron or myeline pathology or calcifications. Susceptibility weighted imaging (SWI) is another widely applied method to visualize iron deposition, although it is not able to provide quantitative measures. One of the more novel techniques to measure magnetic susceptibility is quantified susceptibility mapping (QSM). This technique can differentiate between diamagnetic and paramagnetic and can quantify the iron measures.

*Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

Multiple studies have shown increased iron levels in the basal ganglia in early manifest HDEGC compared to controls [137–143]. Some studies also found a decreased level of iron deposition in the frontal white matter [137, 140], parieto-occipital cortex [144], and in the substantia nigra and hippocampus. More novel studies have also included premanifest HDEGC, often showing an increased iron level in the caudate [145], putamen, and globus pallidus [146] as well [142–144, 147]. One study from Sanchez-Castaneda et al. showed an increased iron level in the caudate, staying relatively stable throughout disease progression, whereas iron levels in the putamen and globus pallidus increased progressively [144]. Iron-sensitive MRI studies have found correlations between iron levels and CAG repeat [139, 140, 144, 145, 147], increasing disease severity [138, 139, 141, 142, 144] and were found to be independent of volume [138].

#### **4. Functional MRI**

Functional MRI (fMRI) is an imaging technique that measures brain activity by using changes in blood oxygenation due to hemodynamic (blood flow) response to neuronal activity (Blood Oxygen Level-Dependent [BOLD]). This BOLD signal represents the ratio between oxygenated and deoxygenated blood and can be used as a measure of local neural activity. fMRI can be obtained in a resting state (rs-fMRI), where you can analyze the function between interacting regions, focusing on network connectivity or connectomics. Task-Based fMRI is another way to measure neural function connectivity, by doing a particular task or function while being in the MRIscanner. It investigates the neurovascular response to these tasks [148].

Like in structural MRI there are different ways to investigate the neural activity. Task-based fMRI is usually done on a voxel-by-voxel basis. With rs-fMRI you can use seed-based analyses or independent component analysis. Seed-based analysis is partly hypothesis-driven and looks at a predefined region, compared to the rest of the brain. Independent component analysis is not based on predefined knowledge. Functional connectomics can be made by using both these rs-fMRI methods [149]. Current studies often correct functional connectivity for loss of volume.

Functional activity is highly dependent on the type of task and the region of the brain that is analyzed. Furthermore, when applying task-based fMRI in a multicentre imaging trial, this can entail a higher degree of variability in the performed tasks. Therefore, comparing fMRI studies and interpreting them can be very difficult [81].

#### **4.1 Resting state-fMRI**

rs-fMRI studies have been used to examine functional networks in HD populations. They overcome the variability in task performances that come along with task-based fMRI. In recent years, there is an increase in studies looking at the entire connectome. Studies using a seed-based analysis often look at functional networks like known motor and cognitive networks. They also regularly include the default mode network (DMN), a network that becomes active when the brain is at rest. These studies show reduced connectivity in the DMN of HDEGC compared to controls, suggesting a disrupted connection when the brain is at rest [150, 151]. Reduced connectivity in manifest HDEGC has been found within the basal ganglia, between the basal ganglia and the insula and between the primary motor cortex and the insula [97]. Also, premanifest HDEGC has shown reduced connectivity within the primary

motor cortex [152], between the premotor cortex and the caudate nucleus [153], and in the somatosensory cortex [154]. This reduced connectivity in both premanifest and manifest HDEGC correlated with motor performance [97, 152, 154]. Another study showed reduced connectivity between the cerebellum and the paracentral gyrus, which correlated with disease burden and motor signs [155]. Reduced connectivity in the lower fusiform gyrus, which is important in the visual network, correlated with disease burden and symbol digit modality test (SDMT) scores [156]. Functional connectivity was also found to be reduced in the dorsal attention network in both premanifest and manifest HD, correlating with cognitive decline [154].

Studies investigating functional connectivity in the executive network found both reduced [151, 156] and increased connectivity [154, 156, 157] in manifest HD. This difference in connectivity seems to follow different spatial trajectories: parietal cortex and subcortical structures get a decreased connectivity in manifest stages, [151, 154, 156], while increased connectivity is measured in the frontal cortex [156–158]. McColgan et al. confirmed this difference in connection per region using connectomics [159]. Increased connectivity was also found in the supplementary motor area (SMA) and motor cortex, correlating with worsening of motor performance, in manifest HD patients [156, 157, 160]. Other regions with increased connectivity in manifest HDEGC were bilateral caudate, inferior and middle frontal cortices [156], striatum, thalamus, and frontal regions [157]. Increased CAG repeat length correlated with increasing fronto-occipital connectivity and decreasing connectivity within the visual cortex [161]. A recent rs-fMRI study, using a 7 T MRI-scanner, found functional connectivity to be decreased between the premotor cortex and the striatum as well as between the SMA and the premotor cortex in both premanifest and manifest HDEGC compared to controls. The connectivity was increased between the striatum and both the frontal inferior and frontal middle region. They also found a significant correlation between the TMS and the connectivity in the premotor regions and between the UDHRS behavioral score and the connectivity in the frontal middle regions. The CAP score and estimated years to onset correlated with functional dysconnectivity between the striatum and frontal region, and between the premotor cortex and the SMA, suggesting a potentially valuable biomarker [162]. Some rs-fMRI studies show an association between increased frontal connectivity and more preserved cognitive function [163, 164], suggesting a compensatory response.

Some studies have also shown a difference in disconnection between premanifest and manifest expanded gene carriers. While reduced connectivity was found between frontal and motor cortex and within the medial visual network for both premanifest and manifest HDEGC, reduced connectivity in the deep gray matter and occipital cortex was only detected in manifest HDEGC [151]. Other studies have found no difference in the functional connectivity of the visual network between premanifest HDEGC and healthy controls [60, 165]. Coppen and colleagues did find a significant decrease in functional connectivity in this area in the manifest patients [60].

So far, studies have not found a longitudinal change in connectivity [165–167]. One study did a follow-up of a premanifest HDEGC cohort and found no change in connectivity after 3 years [165].

#### **4.2 Task-based fMRI**

Multiple task-based fMRI studies have shown changes in activation in manifest HD patients compared to controls, correcting for brain atrophy [168–171]. Something that has been identified more than once is a hyperactivation in certain regions in

#### *Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

premanifest HDEGC, while other regions show a decreased activation. In one study they found a decreased activation in the posterior cingulate and hyperactivation in the left anterior prefrontal cortex in premanifest HDEGC [160]. Klöppel et al. found increased activation in the supplementary motor area (SMA) after a finger tapping task in premanifest HDEGC, especially in the HDEGC subgroup closest to onset [172]. They also found increased activation in the right parietal cortex in response to a working memory task, with a correlation to atrophy [164]. This increased activation suggests compensation in the premanifest phase of the disease. Other studies in premanifest HDEGCs confirmed this hyperactivation. In premanifest HDEGC *far-from-onset* hyperactivation was found in the left sensorimotor cortex [173], anterior cingulate and preSMA [174], and subcortical structures [175]. In only one study, hypoactivity was found in the anterior cingulate in premanifest HDEGC *far-from-onset* [173]. In the premanifest subgroup *close-to-onset* hypoactivation was found in the subcortical structures [174, 175], SMA, left insula, right inferior frontal gyrus [173], and dorsolateral prefrontal cortex [176]. Besides Klöppel et al. [172], one other study found hyperactivation in premanifest HDEGC *close-to-onset*, in the left inferior parietal and right superior frontal regions [176]. The multimodal study of Pini et al., where they used volumetric MRI, DTI, and fMRI, confirmed these differences in premanifest HDEGC subgroups. In *far-from-onset* premanifest HDEGC, there was increased connectivity in the left caudate-cortical functional pathway compared to the healthy controls. No significant differences were found between the *close-toonset* subgroup and healthy controls. There was also no difference between the total premanifest group and controls with regard to functional connectivity of the right caudate nucleus, bilateral putamen, and bilateral nucleus accumbens [105]. These differences in activity and locations sometimes differ from each other, partly due to differences in task designs. Therefore, no real conclusion can be made about the exact time point in the premanifest phase when and the brain region where hyperactivation takes place. However, it's quite clear that there is regional increased activation somewhere during the premanifest phase of the disease, possibly far-from-onset. This is often interpreted as compensation for dysfunctional circuits elsewhere [177].

Several longitudinal fMRI studies have shown none or little evidence of longitudinal changes in activity. Dominguez et al. followed 29 controls, 35 premanifest HDEGC, and 18 manifest HDEGC and showed no changes in activity in either the controls or the premanifest HDEGC after a period of 30 months. The symptomatic HDEGC did show a reduction over time [168]. Poudel and colleagues showed no longitudinal change in activation in early manifest HDEGC and controls after a follow-up of 30 months [178]. However, in the premanifest cohort, there was a progressive increase in activation in the dorsolateral prefrontal cortex and frontal regions over 18 months [179] and over 30 months [178]. Also, Wolf et al. found no evidence of longitudinal changes in activity after a 2-year follow-up of 13 premanifest HDEGC and 13 controls [180].

There have been fMRI studies that relate neural activity to specific symptoms, especially neuropsychiatric symptoms [154]. One study found a positive correlation between depressive symptoms and activation of the ventromedial prefrontal cortex during the Stroop interference task, in premanifest HDEGC [153]. This correlation was more significant with longer CAG repeats. Gray et al. found an association between reduced prefrontal activation in symptomatic HDEGC and severe neuropsychiatric problems such as disinhibition and depression [181]. In the longitudinal study of Dominguez and colleagues, the progressive hypoactivation in the right dorsolateral prefrontal cortex and putamen, in symptomatic HDEGC, was found to be associated with disturbances in executive functioning [168].

fMRI studies have shown us that HD is much more than a basal ganglia disorder. It serves as a tool for a better understanding of the underlying pathophysiological mechanisms, especially concerning different symptoms. Task-based fMRI studies have demonstrated compensatory mechanisms, which may serve as an important marker in the premanifest stage before clinical signs develop.

#### **5. PET-scan**

PET is a non-invasive molecular imaging technique for the quantitative imaging of biological functions. It involves the injection of a metabolically active compound labeled with a radioactive isotope, also known as radioligand. This radioligand binds to specific targets and emits gamma rays which are detectable by the gamma camera in the PET scanner. Based on the molecular pathophysiology several radioligands have been identified and used in HD imaging studies [182]. These include tracers for cerebral glucose metabolism, postsynaptic dopaminergic receptors, phosphodiesterase (PDE)10A, cannabinoid receptors, GABA receptors, adenosine A1 receptors, presynaptic terminal marker SV2A, and activated microglia as markers of neuroinflammation.

First of all, the most common radioligand is [18F]FDG, which traces the uptake of glucose. Multiple studies using [18F]FDG-PET scans showed glucose hypometabolism in the caudate [73, 183] and putamen [54, 73, 184–186] in manifest HDEGC compared to healthy controls. A decreased glucose metabolism has also been measured in premanifest HDEGC [54, 185, 187]. Some studies have identified hypometabolism in the cortex as well [54, 73, 183, 187]. Researchers have found a progressive decline in glucose metabolism over the years [185, 188] and could correlate decreased metabolism in the caudate [189] and the putamen [190] with predicted time to symptomatic onset. Some of these studies corrected the measured glucose uptake for volumetric loss and found hypometabolism to be independent of atrophy [54, 73, 184, 191]. This makes it plausible that altered glucose metabolism precedes volumetric loss. If metabolism is not corrected for partial volume, metabolic deficits could simply be a consequence of neuronal atrophy. Furthermore, there have been studies correlating hypometabolism with clinical assessments, such as cognitive decline [183, 192] and severity of motor symptoms [186]. A recent study from Sampedro et al., which corrected for partial volume, found frontotemporal hypometabolism which correlated to the severity of apathy, and striatal hypometabolism which correlated with motor and cognitive UHDRS scores [73]. Limitations in FDG-PET studies are the several influencing factors like hyperglycaemia [193] and psychotropic drugs including benzodiazepine which can decrease the (global) brain activity and thus the glucose metabolism [194].

Other radiotracers which are useful in HD research are the ones that trace postsynaptic dopaminergic receptors. D1 receptors can be investigated using the radioligand [11C]SCH23390 and D2 receptors are measured by using [11C]raclopride. In PET studies, loss of post-synaptic D1 and D2 receptors has been reported in premanifest [195–197] and manifest HDEGC [198, 199]. Longitudinal studies showed an annual loss of striatal D1 en D2 receptors and this decline seems to be faster during earlier premanifest disease stages [195, 200]. It has been shown that [11C]raclopride is a more sensitive marker of disease progression than glucose metabolism, showing a higher annual loss of D2 receptors than a decline of striatal glucose uptake [185]. Furthermore, it has been shown to precede striatal atrophy [201]. Studies have also

#### *Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

found correlations between dopaminergic receptor loss and CAG repeat (after correcting for age) [201, 202], severity of cognitive function [203], TMS [204], and TFC [195]. Decreased D2 binding in the putamen correlated with higher chorea scores on the TMS and cognitive decline [204]. The specific areas *within* the striatum have also been linked to specific clinical manifestations. Motor signs have been linked to loss of dopamine receptors in the sensorimotor striatum [200], while the associative striatum, in addition to the temporal cortex, is more involved in cognitive decline [205]. Cortical reduction in D2 receptor binding has also been identified in premanifest HDEGC and manifest HDEGC, where the loss of receptors was correlated with worse attention and executive function [205]. Other regions with loss of dopaminergic receptors are the thalamus [203], hypothalamus [206], and frontal and temporal regions [200].

Radioligands that trace PDE10A have been identified in the last decades, such as [ 18F]MNI-659 and [11C]IMA107. Studies in premanifest [207] and manifest HDEGC show a decrease in PDE10A in the caudate and putamen [208], and also in the globus pallidus [209], compared to healthy controls. Two longitudinal studies showed a progressive decrease in these three regions with disease progression, of which decline in the caudate was the most obvious [210, 211]. Fazio et al. showed a slightly more rapid decline from late premanifest to HD stage I, than from early premanifest to late premanifest. Loss of PDE10A has been correlated with more severe motor scores, disease burden, and striatal atrophy [209]. Several studies have shown that annual changes in PDE10A expression were greater than the annual changes in dopamine D2 receptors [185, 195, 200, 211]. This makes PDE10A an even more sensitive marker of disease progression than dopaminergic receptors.

Researchers have identified radiotracers that can detect specific receptors that are related to the HD pathophysiology. Van Laere et al. looked at cannabinoid type receptor (CB1R) levels with the radioligand [18F]MK9470 and found a decreased level in the cortex, brainstem, and cerebellum of early manifest HDEGC. They also found an association between the loss of CB1R and increased disease burden scores [212]. Ceccarini and colleagues measured CB1R levels in premanifest HDEGC and found a decreased binding in the prefrontal cortex, which correlated with depression. They included a control group consisting of gene-negative subjects from HD families to control for potential effects of distress caused by growing up in an HD family and undergoing genetic testing [62].

Two PET studies that have looked at GABA receptor expression, using [11C]flumazenil, and glucose uptake, using [18F]FDG, found a lower level of GABA receptors in the caudate of early manifest HDEGC, compared to healthy controls. Glucose uptake was decreased in the caudate, putamen, and thalamus of these HDEGC [213, 214].

One PET study used [18F]CPFPX to look at striatal adenosine A1 receptors and found a decreased level in the caudate and putamen, in manifest HDEGC compared to healthy controls. There was no significant difference in receptor levels in the caudate and putamen of premanifest HDEGC, compared to healthy controls. However, in the thalamus, they did find a significant increase in A1 receptor level in premanifest HDEGC far-from-onset, while there was no significant difference in the group closeto-onset [215].

There is one PET-study looking at synaptic damage using the tracer [11C]-UCB-J for the presynaptic terminal marker SV2A. In manifest HDEGC, they found a significant loss of SV2A binding in the putamen, caudate, pallidum, cerebellum and parietal, temporal and frontal cortex, whereas glucose metabolism observed with an 18F-FDG PET was only reduced in the caudate and putamen of these patients. Loss of SV2A in the putamen correlated with the TMS. In premanifest HDEGC there was only significant decrease in SVA2 in the caudate and putamen [57].

To study the role of neuroinflammation in cerebral and neurodegenerative diseases, radiotracers that bind to activated microglia can be used, e.g. [11C]PK11195. Studies in HD subjects using [11C]PK11195 have shown increased microglial activation in striatal and cortical regions in both premanifest and manifest HDEGC, with a correlation to loss of striatal D2 receptors [216, 217]. Increased glial activation has also been found in the putamen and pallidum in HDEGC [218]. Studies found microglial activation to be correlated to the severity of motor symptoms [216, 219], disease severity and higher probability of motor onset over the next 5 years [219], and increased levels of interleukins IL-1β, IL-6, IL-8, and TNF-α [220]. Regions of increased activation also seem to differ in disease progression. The dorsal striatum, which is involved in motor and cognitive function, is often affected in premanifest stages. The ventral striatum, involved in psychiatric symptoms, is affected later in the manifest phase [219].

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

Based on the cumulative evidence of the imaging studies included in this chapter, it can be concluded that clinical diagnosis of HD is not the starting point, but rather the endpoint of the neuropathophysiological changes. To summarize the current results of all these neuroimaging modalities along the disease course, we made a hypothetical graph (**Figure 1**). It must be emphasized that this is a hypothetical graph

**Figure 1.** *Course in neuroimaging changes during Huntington's disease progression.*

#### *Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

and may change as new studies are published in the future. Certain disease stages, such as the more advanced stages, have not been sufficiently studied with neuroimaging to make any conclusions. Nevertheless, it places current findings in perspective, by comparing the changes for each modality set against disease progression.

MRI-based brain volume measurements have already been used since the beginning of the 1990s in HD research and have consistently identified volume loss in multiple regions of the brain. These volume changes seem to start in the striatum, years before clinical onset and spread over the cortex ending up affecting widespread regions of the gray and white matter. Longitudinal studies have shown decreasing volumes of the striatum and specific cortical regions, which correlated with clinical outcome measures and genetic variables. All these factors make structural MRI a valuable technique that is helpful to predict clinical onset before diagnosis, although it is still not applicable on an individual level.

To analyze the microstructural differences, which develop before volume loss, DTI is a better imaging modality. This especially applies to the analysis of white matter changes. DTI studies have identified diffusion abnormalities in widespread regions of the brain, showing certain areas with decreasing diffusion while others have increasing diffusion measures in the same stage. These ratios seem to correlate with certain genetic and clinical variables. These results suggest a more important role for white matter disorganization in HD than we previously thought. A potential application of this knowledge might be in classifying and understanding HD phenotypes according to the differences observed in diffusion maps. Nevertheless, longitudinal diffusion effects have not been consistently proven to date.

Specific MRI sequences have improved our knowledge of the HD pathophysiology, can be used as a biomarker for stage-conversion, and give insights into alternative approaches for disease-modifying treatments. Iron accumulations and metabolic changes seem to precede clinical diagnosis and progress with advancing disease stages. However, longitudinal data and consistent cross-sectional results are still lacking.

fMRI studies have shown that HD is more than just a basal ganglia disorder, even in the premanifest stages of the disease. Using fMRI studies, we have been able to improve our understanding of the disease's pathophysiology. Increased connectivity after task performance precedes clinical diagnosis, which might be a compensatory mechanism that slows down the conversion into the manifest stage. One of the limitations is that this modality has not been able to consistently detect a significant change over time.

PET-scans can be used to detect early pathophysiological changes before structural changes, like glucose metabolism, neuroinflammation, and receptor level expression. They show a reliable longitudinal effect and correlate with clinical assessments. [ 18F]FDG PET is a less sensitive marker compared to dopaminergic D2-receptor ligand [11C]raclopride to monitor disease progression. PDE10A expression using different PET-tracers was found to be an even more sensitive marker than dopamine receptor levels. PET-scans improve our knowledge of disease pathophysiology at a molecular level and could help us in evaluating treatment response. At this moment iMagemHTT is in the recruitment phase to study novel mutant huntingtin PET radioligands [11C]CHDI-00485180-R and [11C]CHDI-00485626 and test their suitability for the quantification of aggregated mutant huntingtin in the brain of HDEGC compared to healthy controls [221]. This imaging method combines proteomic knowledge with neuroimaging to improve our knowledge of mHTT aggregation in the brain and it could improve evaluating treatment response.

Multimodal imaging studies, where multiple imaging modalities are used together in one study, are the future of neuroimaging research. To combine these data, researchers have started to use artificial intelligence such as machine learning. It has already been used to develop a multimodality neuroimaging polymarker of HD with the ability to identify HDEGC who are within 5 years of their clinical motor diagnosis. This polymarker consisted of subcortical region volume, cortical thickness, and resting-state functional connectivity [222]. One study used machine learning on neuroimaging datasets to successfully classify between premanifest HDEGC and controls [223]. Mohan et al. recently used machine learning to develop a new disease progression model with nine disease states of increasing severity, based on clinical data only [224]. Another focus in HD-research, requiring artificial intelligence, is the use of network models to explain the pathology of the disease progression and disease phenotypes [158, 225]. However, machine learning algorithms require large amounts of data, before they start to provide useful results, especially when it comes to neural networks. Application in larger imaging studies or the combination of datasets can therefore be expected in the future.

Furthermore, studies should be multicentred to overcome the most common limitation of all included research, which is a small study population. Nevertheless, standardization is not that easy, especially when it concerns advanced MRI-techniques. Despite the excellent performance of different PET-tracers as biomarkers, PET has a major limitation. Not all PET-studies can be performed at every site due to the need for an on-site cyclotron to produce 11C-labeled radioligands.

Using different imaging modalities new pathophysiological mechanisms have been discovered or hypothesized, such as neuroinflammation, iron deposition, cellular reactions, regional deposition of the huntingtin protein. This shines a new light on therapeutic approaches and could serve as a drug target image technique. The imaging of such biomarkers has also some limitations. A biomarker could be relevant only in specific disease stages, after specific physiological events, or present during a limited period of time [5]. An ideal biomarker should change longitudinally, as time and disease progresses. Therefore, longitudinal imaging studies are of great importance. Another characteristic of an ideal biomarker is that it should change in response to disease-modifying treatment, something quite valuable in future clinical trials.

*Neuroimaging Biomarkers for Huntington's Disease DOI: http://dx.doi.org/10.5772/intechopen.102528*

#### **Author details**

Nadine van de Zande1 \*, Eidrees Ghariq2,3, Jeroen de Bresser2 and Susanne de Bot1

1 Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands

2 Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands

3 Department of Radiology, Medical Spectrum Twente, Enschede, The Netherlands

\*Address all correspondence to: n.a.van\_de\_zande@lumc.nl

© 2022 The Author(s). Licensee IntechOpen. 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.

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Section 2
