Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington's disease

Transcellular transmission and molecular heterogeneity of aggregation-prone proteins in neurodegenerative diseases

The accumulation of aggregation-prone proteins in a specific neuronal population is a common feature of neurodegenerative diseases, which is correlated with the development of pathological lesions in diseased brains. The formation and progression of pathological protein aggregates in susceptible neurons induce cellular dysfunction, resulting in progressive degeneration. Moreover, recent evidence supports the notion that the cell-to-cell transmission of pathological protein aggregates may be involved in the onset and progression of many neurodegenerative diseases. Indeed, several studies have identified different pathological aggregate strains. Although how these different aggregate strains form remains unclear, a variety of biomolecular compositions or cross-seeding events promoted by the presence of other protein aggregates in the cellular environment may affect the formation of different strains of pathological aggregates, which in turn can influence complex pathologies in diseased brains. In this review, we summarize the recent results regarding cell-to-cell transmission and the molecular heterogeneity of pathological aggregate strains, raising key questions for future research directions.

Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches

Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure-function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure-function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders.

Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles

Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine-adenine-guanine repeat number. Moreover, the transcriptome-connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Ex vivo 100 μm isotropic diffusion MRI-based tractography of connectivity changes in the end-stage R6/2 mouse model of Huntington's disease

Background

Huntington's disease is a progressive neurodegenerative disorder. Brain atrophy, as measured by volumetric magnetic resonance imaging (MRI), is a downstream consequence of neurodegeneration, but microstructural changes within brain tissue are expected to precede this volumetric decline. The tissue microstructure can be assayed non-invasively using diffusion MRI, which also allows a tractographic analysis of brain connectivity.

Methods

We here used ex vivo diffusion MRI (11.7 T) to measure microstructural changes in different brain regions of end-stage (14 weeks of age) wild type and R6/2 mice (male and female) modeling Huntington's disease. To probe the microstructure of different brain regions, reduce partial volume effects and measure connectivity between different regions, a 100 μm isotropic voxel resolution was acquired.

Results

Although fractional anisotropy did not reveal any difference between wild-type controls and R6/2 mice, mean, axial, and radial diffusivity were increased in female R6/2 mice and decreased in male R6/2 mice. Whole brain streamlines were only reduced in male R6/2 mice, but streamline density was increased. Region-to-region tractography indicated reductions in connectivity between the cortex, hippocampus, and thalamus with the striatum, as well as within the basal ganglia (striatum-globus pallidus-subthalamic nucleus-substantia nigra-thalamus).

Conclusions

Biological sex and left/right hemisphere affected tractographic results, potentially reflecting different stages of disease progression. This proof-of-principle study indicates that diffusion MRI and tractography potentially provide novel biomarkers that connect volumetric changes across different brain regions. In a translation setting, these measurements constitute a novel tool to assess the therapeutic impact of interventions such as neuroprotective agents in transgenic models, as well as patients with Huntington's disease.

Advances in the neuroimaging of motor disorders

Neuroimaging is a valuable adjunct to the history and examination in the evaluation of motor system disorders. Conventional imaging with computed tomography or magnetic resonance imaging depicts important anatomic information and helps to identify imaging patterns which may support diagnosis of a specific motor disorder. Advanced imaging techniques can provide further detail regarding volume, functional, or metabolic changes occurring in nervous system pathology. This chapter is an overview of the advances in neuroimaging with particular emphasis on both standard and less well-known advanced imaging techniques and findings, such as diffusion tensor imaging or volumetric studies, and their application to specific motor disorders. In addition, it provides reference to emerging imaging biomarkers in motor system disorders such as Parkinson disease, amyotrophic lateral sclerosis, and Huntington disease, and briefly reviews the neuroimaging findings in different causes of myelopathy and peripheral nerve disorders.

Insights into White Matter Defect in Huntington's Disease

Huntington's disease (HD) is an autosomal-dominant inherited progressive neurodegenerative disorder. It is caused by a CAG repeat expansion in the Huntingtin gene that is translated to an expanded polyglutamine (PolyQ) repeat in huntingtin protein. HD is characterized by mood swings, involuntary movement, and cognitive decline in the late disease stage. HD patients often die 15-20 years after disease onset. Currently, there is no cure for HD. Due to the striking neuronal loss in HD, most studies focused on the investigation of the predominantly neuronal degeneration in specific brain regions. However, the pathology of the white matter area in the brains of HD patients was also reported by clinical imaging studies, which showed white matter abnormalities even before the clinical onset of HD. Since oligodendrocytes form myelin sheaths around the axons in the brain, white matter lesions are likely attributed to alterations in myelin and oligodendrocyte-associated changes in HD. In this review, we summarized the evidence for white matter, myelin, and oligodendrocytes alterations that were previously observed in HD patients and animal models. We also discussed potential mechanisms for white matter changes and possible treatment to prevent glial dysfunction in HD.

Hunting for the cause: Evidence for prion-like mechanisms in Huntington’s disease

The hypothesis that pathogenic protein aggregates associated with neurodegenerative diseases spread from cell-to-cell in the brain in a manner akin to infectious prions has gained substantial momentum due to an explosion of research in the past 10–15 years. Here, we review current evidence supporting the existence of prion-like mechanisms in Huntington’s disease (HD), an autosomal dominant neurodegenerative disease caused by expansion of a CAG repeat tract in exon 1 of the huntingtin (HTT) gene. We summarize information gained from human studies and in vivo and in vitro models of HD that strongly support prion-like features of the mutant HTT (mHTT) protein, including potential involvement of molecular features of mHTT seeds, synaptic structures and connectivity, endocytic and exocytic mechanisms, tunneling nanotubes, and nonneuronal cells in mHTT propagation in the brain. We discuss mechanisms by which mHTT aggregate spreading and neurotoxicity could be causally linked and the potential benefits of targeting prion-like mechanisms in the search for new disease-modifying therapies for HD and other fatal neurodegenerative diseases.

Ramping activity in the striatum

Control of the timing of behavior is thought to require the basal ganglia (BG) and BG pathologies impair performance in timing tasks. Temporal interval discrimination depends on the ramping activity of medium spiny neurons (MSN) in the main BG input structure, the striatum, but the underlying mechanisms driving this activity are unclear. Here, we combine an MSN dynamical network model with an action selection system applied to an interval discrimination task. We find that when network parameters are appropriate for the striatum so that slowly fluctuating marginally stable dynamics are intrinsically generated, up and down ramping populations naturally emerge which enable significantly above chance task performance. We show that emergent population activity is in very good agreement with empirical studies and discuss how MSN network dysfunction in disease may alter temporal perception.

Sleep Quality and Related Clinical Manifestations in Huntington Disease

(1) Background: Sleep patterns are frequently disrupted in neurodegenerative disorders such as Huntington disease (HD); however, they are still poorly understood, especially their association with clinic features. Our study aimed to explore potential correlations between sleep features and motor, cognitive, behavioural and functional changes in manifest HD subjects. (2) Methods: We enrolled 42 patients who were assessed by the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) questionnaires; clinical features were evaluated by the validated ENROLL-HD platform assay, including the Unified Huntington’s Disease Rating Scale (UHDRS) and the Problem Behaviours Assessment Short Form (PBA-s). (3) Results: We found a significant association between the patients’ perception of sleep abnormalities and scores of impaired independence, cognitive and motor performances. Specifically, sleep efficiency (PSQI—C4 subscores) and the use of sleep medications (PSQI—C6 subscores) seem to be more frequently associated with the severity of the disease progression. (4) Conclusion: sleep abnormalities represent an important part of the HD clinical profile and can impair patients’ quality of life by affecting their level of independence, cognition performance and mental well-being.

Timing of selective basal ganglia white matter loss in premanifest Huntington's disease

OBJECTIVES

To investigate the timeframe prior to symptom onset when cortico-basal ganglia white matter (white matter) loss begins in premanifest Huntington's disease (preHD), and which striatal and thalamic sub-region white matter tracts are most vulnerable.

METHODS

We performed fixel-based analysis, which allows resolution of crossing white matter fibres at the voxel level, on diffusion tractography derived white matter tracts of striatal and thalamic sub-regions in two independent cohorts; TrackON-HD, which included 72 preHD (approx. 11 years before disease onset) and 85 controls imaged at three time points over two years; and the HD young adult study (HD-YAS), which included 54 preHD (approx. 25 years before disease onset) and 53 controls, imaged at one time point. Group differences in fibre density and cross section (FDC) were investigated.

RESULTS

We found no significant group differences in cortico-basal ganglia sub-region FDC in preHD gene carriers 25 years before onset. In gene carriers 11 years before onset, there were reductions in striatal (limbic and caudal motor) and thalamic (premotor, motor and sensory) FDC at baseline, with no significant change over 2 years. Caudal motor-striatal, pre-motor-thalamic, and primary motor-thalamic FDC at baseline, showed significant correlations with the Unified Huntington's disease rating scale (UHDRS) total motor score (TMS). Limbic cortico-striatal FDC and apathy were also significantly correlated.

CONCLUSIONS

Our findings suggest that limbic and motor white matter tracts to the striatum and thalamus are most susceptible to early degeneration in HD but that approximately 25 years from onset, these tracts appear preserved. These findings may have importance in determining the optimum time to initiate future disease modifying therapies in HD.

Longitudinal mapping of cortical surface changes in Huntington's Disease

This paper investigated cortical folding in Huntington's disease to understand how disease progression impacts the surface of the cortex. Cortical morphometry changes in eight gyral based regions of interest (i.e. the left and right hemispheres of the lateral occipital, precentral, superior frontal and rostral middle gyri) were examined. We used existing neuroimaging data from IMAGE-HD, comprising 26 pre-symptomatic, 26 symptomatic and 24 healthy control individuals at three separate time points (baseline, 18-month, 30-month). Local gyrification index and cortical thickness were derived as the measures of cortical morphometry using FreeSurfer 6.0's longitudinal pipeline. The gyral based regions of interest were identified using the Desikan-Killiany Atlas. A Group by Time repeated measures ANCOVA was conducted for each region of interest. We found significantly lower LGI at a group level in the right hemisphere lateral occipital region and both hemispheres of the precentral region; as well as significantly reduced cortical thickness at a group level in both hemispheres of the lateral occipital and precentral regions and the right hemisphere of the superior frontal region. We also found a Group by Time interaction for Local gyrification index in the right hemisphere lateral occipital region. This change was largely driven by a significant decrease in the symptomatic group between baseline and 18-months. Additionally, lower local gyrification index and cortical thickness were associated with higher disease burden score. These findings demonstrate that significant longitudinal decline in right hemisphere local gyrification index is evident during manifest disease in lateral occipital cortex and that these changes are more profound in individuals with greater disease burden score.

The developmental roots of neurodegeneration

Neurodegenerative disorders can alter neural circuitry long before symptoms appear, but the path from early changes to later pathologies is obscure. In this issue of Neuron, Capizzi et al. (2021) show how early axonal growth defects in Huntington's disease create vulnerability to later degeneration.

Striatal Induction and Spread of the Huntington's Disease Protein: A Novel Rhes Route

The CAG/CAA expansion encoding polyQ huntingtin (mutant huntingtin [mHTT]) causes Huntington's disease (HD), which is characterized by atrophy and loss of striatal medium spiny neurons (MSNs), which are preceded by neuropathological alterations in the cortex. Previous studies have shown that mHTT can spread in the brain, but the mechanisms involved in the stereotyped degeneration and dysfunction of the neurons from the striatum to the cortex remain unclear. In this study, we found that the mHTT expression initially restricted in the striatum later spread to the cortical regions in mouse brains. Such transmission was diminished in mice that lacked the striatal-enriched protein Ras-homolog enriched in the striatum (Rhes). Rhes restricted to MSNs was also found in the cortical layers of the brain, indicating a new transmission route for the Rhes protein to the brain. Mechanistically, Rhes promotes such transmission via a direct cell-to-cell contact mediated by tunneling nanotubes (TNTs), the membranous protrusions that enable the transfer of mHTT, Rhes, and other vesicular cargoes. These transmission patterns suggest that Rhes and mHTT are likely co-transported in the brain using TNT-like cell-to-cell contacts. On the basis of these new results, a perspective is presented in this review: Rhes may ignite the mHTT transmission from the striatum that may coincide with HD onset and disease progression through an anatomically connected striato-cortical retrograde route.

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics

Ultra-high field MRI across the depth of the cortex has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer-selective vulnerability. Here we compare multi-parametric mapping (MPM) measures across cortical depths for a 7T 500 μm whole brain acquisition to (a) layer-specific cell measures from the von Economo histology atlas, (b) layer-specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high-fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer-specific cell number and layer-specific gene expression. R1- and R2*-weighted connectivity strength of cortico-striatal and intra-hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer-specific relationships demonstrated are at least in part related to the high cross-correlations of von Economo atlas cell counts and layer-specific gene expression across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.

INTEGRATIVE NETWORK LEARNING FOR MULTI-MODALITY BIOMARKER DATA

The biomarker networks measured by different modalities of data (e.g., structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI)) may share the same true underlying biological model. In this work, we propose a node-wise biomarker graphical model to leverage the shared mechanism between multi-modality data to provide a more reliable estimation of the target modality network and account for the heterogeneity in networks due to differences between subjects and networks of external modality. Latent variables are introduced to represent the shared unobserved biological network and the information from the external modality is incorporated to model the distribution of the underlying biological network. We propose an efficient approximation to the posterior expectation of the latent variables that reduces computational cost by at least 50%. The performance of the proposed method is demonstrated by extensive simulation studies and an application to construct gray matter brain atrophy network of Huntington's disease by using sMRI data and DTI data. The identified network connections are more consistent with clinical literature and better improve prediction in follow-up clinical outcomes and separate subjects into clinically meaningful subgroups with different prognosis than alternative methods.

Oligodendroglial Heterogeneity in Neuropsychiatric Disease

Oligodendroglia interact with neurons to support their health and maintain the normal functioning of the central nervous system (CNS). Human oligodendroglia are a highly heterogeneous population characterised by distinct developmental origins and regional differences, as well as variation in cellular states, as evidenced by recent analysis at single-nuclei resolution. Increasingly, there is evidence to suggest that the highly heterogeneous nature of oligodendroglia might underpin their role in a range of CNS disorders, including those with neuropsychiatric symptoms. Understanding the role of oligodendroglial heterogeneity in this group of disorders might pave the way for novel approaches to identify biomarkers and develop treatments.

Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes

Recently, it has been proposed that the harmonic patterns emerging from the brain's structural connectivity underlie the resting state networks of the human brain. These harmonic patterns, termed connectome harmonics, are estimated as the Laplace eigenfunctions of the combined gray and white matters connectivity matrices and yield a connectome-specific extension of the well-known Fourier basis. However, it remains unclear how topological properties of the combined connectomes constrain the precise shape of the connectome harmonics and their relationships to the resting state networks. Here, we systematically study how alterations of the local and long-range connectivity matrices affect the spatial patterns of connectome harmonics. Specifically, the proportion of local gray matter homogeneous connectivity versus long-range white-matter heterogeneous connectivity is varied by means of weight-based matrix thresholding, distance-based matrix trimming, and several types of matrix randomizations. We demonstrate that the proportion of local gray matter connections plays a crucial role for the emergence of wide-spread, functionally meaningful, and originally published connectome harmonic patterns. This finding is robust for several different cortical surface templates, mesh resolutions, or widths of the local diffusion kernel. Finally, using the connectome harmonic framework, we also provide a proof-of-concept for how targeted structural changes such as the atrophy of inter-hemispheric callosal fibers and gray matter alterations may predict functional deficits associated with neurodegenerative conditions.

The specific role of the striatum in interval timing: The Huntington’s disease model

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Patients with Huntington’s Disease (HD) report a temporal deficit in daily life.

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We tested HD gene carriers and controls in spatial (cm) and temporal (s) tasks.

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Early stage HD patients, but not presymptomatic carriers, were more impaired in time.

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Striatal volume was associated with the temporal deficit in gene carriers.

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Evaluation of interval timing processing should be used as a clinical tool.

Time processing over intervals of hundreds of milliseconds to minutes, also known as interval timing, is associated with the striatum. Huntington’s disease patients (HD) with striatal degeneration have impaired interval timing, but the extent and specificity of these deficits remain unclear. Are they specific to the temporal domain, or do they extend to the spatial domain too? Do they extend to both the perception and production of interval timing? Do they appear before motor symptoms in Huntington’s disease (Pre-HD)?

We addressed these issues by assessing both temporal abilities (in the seconds range) and spatial abilities (in the cm range) in 20 Pre-HD, 25 HD patients, and 25 healthy Controls, in discrimination, bisection and production paradigms. In addition, all participants completed a questionnaire assessing temporal and spatial disorientation in daily life, and the gene carriers (i.e., HD and Pre-HD participants) underwent structural brain MRI.

Overall, HD patients were more impaired in the temporal than in the spatial domain in the behavioral tasks, and expressed a greater disorientation in the temporal domain in the daily life questionnaire. In contrast, Pre-HD participants showed no sign of a specific temporal deficit. Furthermore, MRI analyses indicated that performances in the temporal discrimination task were associated with a larger striatal grey matter volume in the striatum in gene carriers.

Altogether, behavioral, brain imaging and questionnaire data support the hypothesis that the striatum is a specific component of interval timing processes. Evaluations of temporal disorientation and interval timing processing could be used as clinical tools for HD patients.

Cortical morphometry and neural dysfunction in Huntington's disease: a review

Numerous neuroimaging techniques have been used to identify biomarkers of disease progression in Huntington's disease (HD). To date, the earliest and most sensitive of these is caudate volume; however, it is becoming increasingly evident that numerous changes to cortical structures, and their interconnected networks, occur throughout the course of the disease. The mechanisms by which atrophy spreads from the caudate to these cortical regions remains unknown. In this review, the neuroimaging literature specific to T1-weighted and diffusion-weighted magnetic resonance imaging is summarized and new strategies for the investigation of cortical morphometry and the network spread of degeneration in HD are proposed. This new avenue of research may enable further characterization of disease pathology and could add to a suite of biomarker/s of disease progression for patient stratification that will help guide future clinical trials.

Dementia risk in Parkinson's disease is associated with interhemispheric connectivity loss and determined by regional gene expression

Parkinson's dementia is a common and devastating part of Parkinson's disease. Whilst timing and severity vary, dementia in Parkinson's is often preceded by visual dysfunction. White matter changes, representing axonal loss, occur early in the disease process. Clarifying which white matter connections are affected in Parkinson's with visual dysfunction and why specific connections are vulnerable will provide important mechanistic insights. Here, we use diffusion tractography in 100 Parkinson's patients (33 low visual performers) and 34 controls to identify patterns of connectivity loss in Parkinson's with visual dysfunction. We examine the relationship between regional transcription and connectivity loss, using the Allen Institute for Brain Science transcriptome atlas. We show that interhemispheric connections are preferentially affected in Parkinson's low visual performers. Interhemispheric connection loss was associated with downweighted genes related to the smoothened signalling pathway (enriched in glutamatergic neurons) and upweighted metabolic genes. Risk genes for Parkinson's but not Alzheimer's or Dementia with Lewy bodies were over-represented in upweighted genes associated with interhemispheric connection loss. Our findings suggest selective vulnerability in Parkinson's patients at highest risk of dementia (those with visual dysfunction), where differences in gene expression and metabolic dysfunction, affecting longer connections with higher metabolic burden, drive connectivity loss.

Diffusion imaging in Huntington's disease: comprehensive review

Huntington's disease (HD) is a monogenic disorder with 100% penetrance. With the advent of genetic testing in adults, disease-related, structural brain changes can be investigated from the earliest, premorbid stages of HD. While examining macrostructural change characterises global neuronal damage, investigating microstructural alterations provides information regarding brain organisation and its underlying biological properties. Diffusion MRI can be used to track the progression of microstructural anomalies in HD decades prior to clinical disease onset, providing a greater understanding of neurodegeneration. Multiple approaches, including voxelwise, region of interest and tractography, have been used in HD cohorts, showing a centrifugal pattern of white matter (WM) degeneration starting from deep brain areas, which is consistent with neuropathological studies. The corpus callosum, longer WM tracts and areas that are more densely connected, in particular the sensorimotor network, also tend to be affected early during premanifest stages. Recent evidence supports the routine inclusion of diffusion analyses within clinical trials principally as an additional measure to improve understanding of treatment effects, while the advent of novel techniques such as multitissue compartment models and connectomics can help characterise the underpinnings of progressive functional decline in HD.

White and Gray Matter Abnormalities in Manifest Huntington's Disease: Cross-Sectional and Longitudinal Analysis

BACKGROUND AND PURPOSE

Early white matter (WM) changes and cortical atrophy in Huntington's disease (HD) are often evident before disease onset and extend through the brain during manifest stages. The trajectory of these brain abnormalities in symptomatic stages remains relatively unexplored. The aim of this study is to investigate how the pattern of WM and gray matter (GM) alterations progress over time.

METHODS

We investigated alterations in brain WM, cortical thickness, and subcortical structures using diffusion and structural magnetic resonance imaging, in manifest HD patients (n = 13) compared to age-matched healthy controls (n = 11). Imaging and clinical data for the HD group were collected at follow-up (7 months) to explore possible longitudinal changes.

RESULTS

Cross-sectional analyses identified significant posterior cortical thinning (P < .05) and symmetric fractional anisotropy (FA) reduction (P < .01) in brain WM of HD group compared to HC. These changes were strongly correlated with impairment in motor symptoms and processing speed. Subcortical atrophy was significant in caudate, putamen, globus pallidus, and thalamus (P < .001). Regions of interest analysis revealed a significant reduction in FA of the corpus callosum (CC) (-2.19%, P < .05) upon follow-up, whereas no significant cortical thinning and subcortical atrophy was found.

CONCLUSIONS

This study showed broad GM and WM abnormalities in manifest HD patients. Reductions in FA and cortical thinning correlated significantly with the disturbances of motor and cognitive processing that describe HD. Follow-up assessment showed that the CC is compromised in the absence of detectable GM changes or motor decline, suggesting it plays an important role in disease progression.

Identifying disease-associated biomarker network features through conditional graphical model

Biomarkers are often organized into networks, in which the strengths of network connections vary across subjects depending on subject-specific covariates (eg, genetic variants). Variation of network connections, as subject-specific feature variables, has been found to predict disease clinical outcome. In this work, we develop a two-stage method to estimate biomarker networks that account for heterogeneity among subjects and evaluate network's association with disease clinical outcome. In the first stage, we propose a conditional Gaussian graphical model with mean and precision matrix depending on covariates to obtain covariate-dependent networks with connection strengths varying across subjects while assuming homogeneous network structure. In the second stage, we evaluate clinical utility of network measures (connection strengths) estimated from the first stage. The second-stage analysis provides the relative predictive power of between-region network measures on clinical impairment in the context of regional biomarkers and existing disease risk factors. We assess the performance of proposed method by extensive simulation studies and application to a Huntington's disease (HD) study to investigate the effect of HD causal gene on the rate of change in motor symptom through affecting brain subcortical and cortical gray matter atrophy connections. We show that cortical network connections and subcortical volumes, but not subcortical connections are identified to be predictive of clinical motor function deterioration. We validate these findings in an independent HD study. Lastly, highly similar patterns seen in the gray matter connections and a previous white matter connectivity study suggest a shared biological mechanism for HD and support the hypothesis that white matter loss is a direct result of neuronal loss as opposed to the loss of myelin or dysmyelination.

Selective Neuronal Death in Neurodegenerative Diseases: The Ongoing Mystery

A major unresolved problem in neurodegenerative disease is why and how a specific set of neurons in the brain are highly vulnerable to neuronal death. Multiple pathways and mechanisms have been proposed to play a role in Alzheimer disease (AD), Parkinson disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD), yet how they contribute to neuronal vulnerability remains far from clear. In this review, various mechanisms ascribed in AD, PD, ALS, and HD will be briefly summarized. Particular focus will be placed on Rhes-mediated intercellular transport of the HD protein and its role in mitophagy, in which I will discuss some intriguing observations that I apply to model striatal vulnerability in HD. I may have unintentionally missed referring some studies in this review, and I extend my apologies to the authors in those circumstances.

Network spread determines severity of degeneration and disconnection in Huntington's disease

Trans-neuronal propagation of mutant huntingtin protein contributes to the organised spread of cortico-striatal degeneration and disconnection in Huntington's disease (HD). We investigated whether the network diffusion model, which models transneuronal spread as diffusion of pathological proteins via the brain connectome, can determine the severity of neural degeneration and disconnection in HD. We used structural magnetic resonance imaging (MRI) and high-angular resolution diffusion weighted imaging (DWI) data from symptomatic Huntington's disease (HD) (N = 26) and age-matched healthy controls (N = 26) to measure neural degeneration and disconnection in HD. The network diffusion model was used to test whether disease spread, via the human brain connectome, is a viable mechanism to explain the distribution of pathology across the brain. We found that an eigenmode identified in the healthy human brain connectome Laplacian matrix, accurately predicts the cortico-striatal spatial pattern of degeneration in HD. Furthermore, the spread of neural degeneration from sub-cortical brain regions, including the accumbens and thalamus, generates a spatial pattern which represents the typical neurodegenerative characteristics in HD. The white matter connections connecting the nodes with the highest amount of disease factors, when diffusion based disease spread is initiated from the striatum, were found to be most vulnerable to disconnection in HD. These findings suggest that trans-neuronal diffusion of mutant huntingtin protein across the human brain connectome may explain the pattern of gray matter degeneration and white matter disconnection that are hallmarks of HD.

Huntington's disease: Brain imaging in Huntington's disease

Huntington's disease (HD) gene-carriers show prominent neuronal loss by end-stage disease, and the use of magnetic resonance imaging (MRI) has been increasingly used to quantify brain changes during earlier stages of the disease. MRI offers an in vivo method of measuring structural and functional brain change. The images collected via MRI are processed to measure different anatomical features, such as brain volume, macro- and microstructural changes within white matter and functional brain activity. Structural imaging has demonstrated significant volume loss across multiple white and gray matter regions in HD, particularly within subcortical structures. There also appears to be increasing disorganization of white matter tracts and between-region connectivity with increasing disease progression. Finally, functional changes are thought to represent changes in brain activity underlying compensatory mechanisms in HD. This chapter will provide an overview of the principles of MRI and practicalities associated with using MRI in HD studies, and summarize findings from MRI studies investigating brain structure and function in HD.

Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data

The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capture essential asymmetric structural information and heterogeneity between DAGs in order to allow for the identification of causal structure with observational data. In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. We propose a penalized likelihood-based approach to handle multi-dimensionality of the DAG model. We propose a fast, iterative computational algorithm, DAG-MM, to estimate parameters in mSEM and achieve desirable sparsity by hard-thresholding the edges. We theoretically prove the identifiability of mSEM. Using simulations and an application to protein signaling data, we show substantially improved performances when compared to existing methods and consistent results with a network estimated from interventional data. Lastly, we identify gray matter atrophy networks in regions of brain from patients with Huntington's disease and corroborate our findings using white matter connectivity data collected from an independent study.

In vivo characterization of white matter pathology in premanifest huntington's disease

OBJECTIVE

Huntington's disease (HD) is a monogenic, fully penetrant neurodegenerative disorder, providing an ideal model for understanding brain changes occurring in the years prior to disease onset. Diffusion tensor imaging (DTI) studies show widespread white matter disorganization in the early premanifest stages (pre-HD). However, although DTI has proved informative, it provides only limited information about underlying changes in tissue properties. Neurite orientation dispersion and density imaging (NODDI) is a novel magnetic resonance imaging (MRI) technique for characterizing axonal pathology more specifically, providing metrics that separately quantify axonal density and axonal organization. Here, we provide the first in vivo characterization of white matter pathology in pre-HD using NODDI.

METHODS

Diffusion-weighted MRI data that support DTI and NODDI were acquired from 38 pre-HD and 45 control participants. Using whole-brain and region-of-interest analyses, NODDI metrics were compared between groups and correlated with clinical scores of disease progression. Whole-brain changes in DTI metrics were also examined.

RESULTS

The pre-HD group displayed widespread reductions in axonal density compared with control participants; this correlated with measures of clinical disease progression in the body and genu of the corpus callosum. There was also evidence in the pre-HD group of increased coherence of axonal packing in the white matter surrounding the basal ganglia.

INTERPRETATION

Our findings suggest that reduced axonal density is one of the major factors underlying white matter pathology in pre-HD, coupled with altered local organization in areas surrounding the basal ganglia. NODDI metrics show promise in providing more specific information about the biological processes underlying HD and neurodegeneration per se. Ann Neurol 2018;84:497-504.

Can neuroimaging predict dementia in Parkinson's disease?

Dementia in Parkinson's disease affects 50% of patients within 10 years of diagnosis but there is wide variation in severity and timing. Thus, robust neuroimaging prediction of cognitive involvement in Parkinson's disease is important: (i) to identify at-risk individuals for clinical trials of potential new treatments; (ii) to provide reliable prognostic information for individuals and populations; and (iii) to shed light on the pathophysiological processes underpinning Parkinson's disease dementia. To date, neuroimaging has not made major contributions to predicting cognitive involvement in Parkinson's disease. This is perhaps unsurprising considering conventional methods rely on macroscopic measures of topographically distributed neurodegeneration, a relatively late event in Parkinson's dementia. However, new technologies are now emerging that could provide important insights through detection of other potentially relevant processes. For example, novel MRI approaches can quantify magnetic susceptibility as a surrogate for tissue iron content, and increasingly powerful mathematical approaches can characterize the topology of brain networks at the systems level. Here, we present an up-to-date overview of the growing role of neuroimaging in predicting dementia in Parkinson's disease. We discuss the most relevant findings to date, and consider the potential of emerging technologies to detect the earliest signs of cognitive involvement in Parkinson's disease.

Brain Regions Showing White Matter Loss in Huntington's Disease Are Enriched for Synaptic and Metabolic Genes

BACKGROUND

The earliest white matter changes in Huntington's disease are seen before disease onset in the premanifest stage around the striatum, within the corpus callosum, and in posterior white matter tracts. While experimental evidence suggests that these changes may be related to abnormal gene transcription, we lack an understanding of the biological processes driving this regional vulnerability.

METHODS

Here, we investigate the relationship between regional transcription in the healthy brain, using the Allen Institute for Brain Science transcriptome atlas, and regional white matter connectivity loss at three time points over 24 months in subjects with premanifest Huntington's disease relative to control participants. The baseline cohort included 72 premanifest Huntington's disease participants and 85 healthy control participants.

RESULTS

We show that loss of corticostriatal, interhemispheric, and intrahemispheric white matter connections at baseline and over 24 months in premanifest Huntington's disease is associated with gene expression profiles enriched for synaptic genes and metabolic genes. Corticostriatal gene expression profiles are predominately associated with motor, parietal, and occipital regions, while interhemispheric expression profiles are associated with frontotemporal regions. We also show that genes with known abnormal transcription in human Huntington's disease and animal models are overrepresented in synaptic gene expression profiles, but not in metabolic gene expression profiles.

CONCLUSIONS

These findings suggest a dual mechanism of white matter vulnerability in Huntington's disease, in which abnormal transcription of synaptic genes and metabolic disturbance not related to transcription may drive white matter loss.

Neurofilament light protein in blood predicts regional atrophy in Huntington disease

OBJECTIVE

Neurofilament light (NfL) protein in blood plasma has been proposed as a prognostic biomarker of neurodegeneration in a number of conditions, including Huntington disease (HD). This study investigates the regional distribution of NfL-associated neural pathology in HD gene expansion carriers.

METHODS

We examined associations between NfL measured in plasma and regionally specific atrophy in cross-sectional (n = 198) and longitudinal (n = 177) data in HD gene expansion carriers from the international multisite TRACK-HD study. Using voxel-based morphometry, we measured associations between baseline NfL levels and both baseline gray matter and white matter volume; and longitudinal change in gray matter and white matter over the subsequent 3 years in HD gene expansion carriers.

RESULTS

After controlling for demographics, associations between increased NfL levels and reduced brain volume were seen in cortical and subcortical gray matter and within the white matter. After also controlling for known predictors of disease progression (age and CAG repeat length), associations were limited to the caudate and putamen. Longitudinally, NfL predicted subsequent occipital gray matter atrophy and widespread white matter reduction, both before and after correction for other predictors of disease progression.

CONCLUSIONS

These findings highlight the value of NfL as a dynamic marker of brain atrophy and, more generally, provide further evidence of the strong association between plasma NfL level, a candidate blood biomarker, and pathologic neuronal change.