Automated differentiation of pre-diagnosis Huntington's disease from healthy control individuals based on quadratic discriminant analysis of the basal ganglia: The IMAGE-HD study

Gray matter alterations in Huntington's disease: A meta-analysis of VBM neuroimaging studies

Increasing neuroimaging studies have attempted to identify biomarkers of Huntington's disease (HD) progression. Here, we conducted voxel-based meta-analyses of voxel-based morphometry (VBM) studies on HD to investigate the evolution of gray matter volume (GMV) alterations and explore the effects of genetic and clinical features on GMV changes. A systematic review was performed to identify the relevant studies. Meta-analyses of whole-brain VBM studies were performed to assess the regional GMV changes in all HD mutation carriers, in presymptomatic HD (pre-HD), and in symptomatic HD (sym-HD). A quantitative comparison was performed between pre-HD and sym-HD. Meta-regression analyses were used to explore the effects of genetic and clinical features on GMV changes. Twenty-eight studies were included, comparing a total of 1811 HD mutation carriers [including 1150 pre-HD and 560 sym-HD] and 969 healthy controls (HCs). Pre-HD showed decreased GMV in the bilateral caudate nuclei, putamen, insula, anterior cingulate/paracingulate gyri, middle temporal gyri, and left dorsolateral superior frontal gyrus compared with HCs. Compared with pre-HD, GMV decrease in sym-HD extended to the bilateral median cingulate/paracingulate gyri, Rolandic operculum and middle occipital gyri, left amygdala, and superior temporal gyrus. Meta-regression analyses found that age, mean lengths of CAG repeats, and disease burden were negatively associated with GMV atrophy of the bilateral caudate and right insula in all HD mutation carriers. This meta-analysis revealed the pattern of GMV changes from pre-HD to sym-HD, prompting the understanding of HD progression. The pattern of GMV changes may be biomarkers for disease progression in HD.

Selective vulnerability of layer 5a corticostriatal neurons in Huntington's disease

The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here, we employed serial fluorescence-activated nuclear sorting (sFANS), deep molecular profiling, and single-nucleus RNA sequencing (snRNA-seq) of motor-cortex samples from thirteen predominantly early stage, clinically diagnosed HD donors and selected samples from cingulate, visual, insular, and prefrontal cortices to demonstrate loss of layer 5a pyramidal neurons in HD. Extensive mHTT CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layers 6a and 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in HD cerebral cortex.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer

OBJECTIVES

To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC).

METHODS

A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA).

RESULTS

A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively.

CONCLUSION

The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC.

CLINICAL RELEVANCE STATEMENT

A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice.

KEY POINTS

• The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

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.

Selective Vulnerability of Layer 5a Corticostriatal Neurons in Huntington's Disease

The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here we employed serial fluorescence activated nuclear sorting (sFANS), deep molecular profiling, and single nucleus RNA sequencing (snRNAseq) to demonstrate that layer 5a pyramidal neurons are vulnerable in primary motor cortex and other cortical areas of HD donors. Extensive mHTT -CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layer 6a, layer 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify the vulnerable layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT -CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in the HD cerebral cortex.

The shape of things to come. Mapping spatiotemporal progression of striatal morphology in Huntington disease: The IMAGE-HD study

Mapping the spatiotemporal progression of neuroanatomical change in Huntington's Disease (HD) is fundamental to the development of bio-measures for prognostication. Statistical shape analysis to measure the striatum has been performed in HD, however there have been a limited number of longitudinal studies. To address these limitations, we utilised the Spherical Harmonic Point Distribution Method (SPHARM-PDM) to generate point distribution models of the striatum in individuals, and used linear mixed models to test for localised shape change over time in pre-manifest HD (pre-HD), symp-HD (symp-HD) and control individuals. Longitudinal MRI scans from the IMAGE-HD study were used (baseline, 18 and 30 months). We found significant differences in the shape of the striatum between groups. Significant group-by-time interaction was observed for the putamen bilaterally, but not for caudate. A differential rate of shape change between groups over time was observed, with more significant deflation in the symp-HD group in comparison with the pre-HD and control groups. CAG repeats were correlated with bilateral striatal shape in pre-HD and symp-HD. Robust statistical analysis of the correlates of striatal shape change in HD has confirmed the suitability of striatal morphology as a potential biomarker correlated with CAG-repeat length, and potentially, an endophenotype.

Selective perforant-pathway atrophy in Huntington disease: MRI analysis of hippocampal subfields

INTRODUCTION

While individuals with Huntington disease (HD) show memory impairment that indicates hippocampal dysfunction, the available literature does not consistently identify structural evidence for involvement of the whole hippocampus but rather suggests that hippocampal atrophy may be confined to certain hippocampal subregions.

METHODS

We processed T1-weighted MRI from IMAGE-HD study using FreeSurfer 7.0 and compared the volumes of the hippocampal subfields among 36 early motor symptomatic (symp-HD), 40 pre-symptomatic (pre-HD), and 36 healthy control individuals across three timepoints over 36 months.

RESULTS

Mixed-model analyses revealed significantly lower subfield volumes in symp-HD, compared with pre-HD and control groups, in the subicular regions of the perforant-pathway: presubiculum, subiculum, dentate gyrus, tail, and right molecular layer. These adjoining subfields aggregated into a single principal component, which demonstrated an accelerated rate of atrophy in the symp-HD. Volumes between pre-HD and controls did not show any significant difference. In the combined HD groups, CAG repeat length and disease burden score were associated with presubiculum, molecular layer, tail, and perforant-pathway subfield volumes. Hippocampal left tail and perforant-pathway subfields were associated with motor onset in the pre-HD group.

CONCLUSIONS

Hippocampal subfields atrophy in early symptomatic HD affects key regions of the perforant-pathway, which may implicate the distinctive memory impairment at this stage of illness. Their volumetric associations with genetic and clinical markers suggest the selective susceptibility of these subfields to mutant Huntingtin and disease progression.

Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington's disease

Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, can add important information to regional volumetric measurements. This study uses two large-scale longitudinal, multicenter datasets, PREDICT-HD and IMAGE-HD, to trace changes in DTI of HD participants with a broad range of CAP scores (a product of CAG repeat expansion and age), including those with pre-manifest disease (i.e., prior to clinical onset). Utilizing a fully automated data-driven approach to study the whole brain divided in regions of interest, we traced changes in DTI metrics (diffusivity and fractional anisotropy) versus CAP scores, using sigmoidal and linear regression models. We identified points of inflection in the sigmoidal regression using change-point analysis. The deep gray matter showed more evident and earlier changes in DTI metrics over CAP scores, compared to the deep white matter. In the deep white matter, these changes were more evident and occurred earlier in superior and posterior areas, compared to anterior and inferior areas. The curves of mean diffusivity vs. age of HD participants within a fixed CAP score were different from those of controls, indicating that the disease has an additional effect to age on the microscopic brain structure. These results show the regional and temporal vulnerability of the white matter and deep gray matter in HD, with potential implications for experimental therapeutics.

Alterations of fractional anisotropy throughout cortico-basal ganglia gray matter in a macaque model of Huntington's Disease

We recently generated a nonhuman primate (NHP) model of the neurodegenerative disorder Huntington's disease (HD) using adeno-associated viral vectors to express a fragment of mutant HTT protein (mHTT) throughout the cortico-basal ganglia circuit. Previous work by our group established that mHTT-treated NHPs exhibit progressive motor and cognitive phenotypes which are accompanied by mild volumetric reductions of cortical-basal ganglia structures and reduced fractional anisotropy (FA) in the white matter fiber pathways interconnecting these regions, mirroring findings observed in early-stage HD patients. Given the mild structural atrophy observed in cortical and sub-cortical gray matter regions characterized in this model using tensor-based morphometry, the current study sought to query potential microstructural alterations in the same gray matter regions using diffusion tensor imaging (DTI), to define early biomarkers of neurodegenerative processes in this model. Here, we report that mHTT-treated NHPs exhibit significant microstructural changes in several cortical and subcortical brain regions that comprise the cortico-basal ganglia circuit; with increased FA in the putamen and globus pallidus and decreased FA in the caudate nucleus and several cortical regions. DTI measures also correlated with motor and cognitive deficits such that animals with increased basal ganglia FA, and decreased cortical FA, had more severe motor and cognitive impairment. These data highlight the functional implications of microstructural changes in the cortico-basal ganglia circuit in early-stage HD.

Progressive alterations in white matter microstructure across the timecourse of Huntington's disease

BACKGROUND

Whole-brain longitudinal diffusion studies are crucial to examine changes in structural connectivity in neurodegeneration. Here, we investigated the longitudinal alterations in white matter (WM) microstructure across the timecourse of Huntington's disease (HD).

METHODS

We examined changes in WM microstructure from premanifest to early manifest disease, using data from two cohorts with different disease burden. The TrackOn-HD study included 67 controls, 67 premanifest, and 10 early manifest HD (baseline and 24-month data); the PADDINGTON study included 33 controls and 49 early manifest HD (baseline and 15-month data). Longitudinal changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, and radial diffusivity from baseline to last study visit were investigated for each cohort using tract-based spatial statistics. An optimized pipeline was employed to generate participant-specific templates to which diffusion tensor imaging maps were registered and change maps were calculated. We examined longitudinal differences between HD expansion-carriers and controls, and correlations with clinical scores, including the composite UHDRS (cUHDRS).

RESULTS

HD expansion-carriers from TrackOn-HD, with lower disease burden, showed a significant longitudinal decline in FA in the left superior longitudinal fasciculus and an increase in MD across subcortical WM tracts compared to controls, while in manifest HD participants from PADDINGTON, there were significant widespread longitudinal increases in diffusivity compared to controls. Baseline scores in clinical scales including the cUHDRS predicted WM microstructural change in HD expansion-carriers.

CONCLUSION

The present study showed significant longitudinal changes in WM microstructure across the HD timecourse. Changes were evident in larger WM areas and across more metrics as the disease advanced, suggesting a progressive alteration of WM microstructure with disease evolution.

Compensatory cognition in neurological diseases and aging: A review of animal and human studies

Specialized individual circuits in the brain are recruited for specific functions. Interestingly, multiple neural circuitries continuously compete with each other to acquire the specialized function. However, the dominant among them compete and become the central neural network for that particular function. For example, the hippocampal principal neural circuitries are the dominant networks among many which are involved in learning processes. But, in the event of damage to the principal circuitry, many times, less dominant networks compensate for the primary network. This review highlights the psychopathologies of functional loss and the aspects of functional recuperation in the absence of the hippocampus.

Investigating longitudinal changes to frontal cortico-striatal tracts in Huntington's disease: the IMAGE-HD study

The striatum is the principal site of disease pathology in Huntington's disease and contains neural connections to numerous cortical brain regions. Studies examining abnormalities to neural connections find that white matter integrity is compromised in HD; however, further regional, and longitudinal investigation is required. This paper is the first longitudinal investigation into region-based white-matter integrity changes in Huntington's Disease. The aim of this study was to better understand how disease progression impacts white matter tracts connecting the striatum to the prefrontal and motor cortical regions in HD. We used existing neuroimaging data from IMAGE-HD, comprised of 25 pre-symptomatic, 27 symptomatic, and 25 healthy controls at three separate time points (baseline, 18-months, 30-months). Fractional anisotropy, axial diffusivity and radial diffusivity were derived as measures of white matter microstructure. The anatomical regions of interest were identified using the Desikan-Killiany brain atlas. A Group by Time repeated measures ANCOVA was conducted for each tract of interest and for each measure. We found significantly lower fractional anisotropy and significantly higher radial diffusivity in the symptomatic group, compared to both the pre-symptomatic group and controls (the latter two groups did not differ from each other), in the rostral middle frontal and superior frontal tracts; as well as significantly higher axial diffusivity in the rostral middle tracts only. We did not find a Group by Time interaction for any of the white matter integrity measures. These findings demonstrate that whilst the microstructure of white matter tracts, extending from the striatum to these regions of interest, are compromised during the symptomatic stages of Huntington's disease, 36-month follow-up did not show progressive changes in these measures. Additionally, no correlations were found between clinical measures and tractography changes, indicating further investigations into the relationship between tractography changes and clinical symptoms in Huntington's disease are required.

Classification of Huntington's Disease Stage with Features Derived from Structural and Diffusion-Weighted Imaging

The purpose of this study was to classify Huntington’s disease (HD) stage using support vector machines and measures derived from T1- and diffusion-weighted imaging. The effects of feature selection approach and combination of imaging modalities are assessed. Fourteen premanifest-HD individuals (Pre-HD; on average > 20 years from estimated disease onset), eleven early-manifest HD (Early-HD) patients, and eighteen healthy controls (HC) participated in the study. We compared three feature selection approaches: (i) whole-brain segmented grey matter (GM; voxel-based measure) or fractional anisotropy (FA) values; (ii) GM or FA values from subcortical regions-of-interest (caudate, putamen, pallidum); and (iii) automated selection of GM or FA values with the algorithm Relief-F. We assessed single- and multi-kernel approaches to classify combined GM and FA measures. Significant classifications were achieved between Early-HD and Pre-HD or HC individuals (accuracy: generally, 85% to 95%), and between Pre-HD and controls for the feature FA of the caudate ROI (74% accuracy). The combination of GM and FA measures did not result in higher performances. We demonstrate evidence on the high sensitivity of FA for the classification of the earliest Pre-HD stages, and successful distinction between HD stages.

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.

Subcortical brain structure in children with developmental coordination disorder: A T1-weighted volumetric study

Developmental coordination disorder (DCD) is a neurodevelopmental disorder occurring in 5-6% of school-aged children. Converging evidence suggests that dysfunction within cortico-striatal and cortico-cerebellar networks may contribute to motor deficits in DCD, yet limited research has examined the brain morphology of these regions. Using T1-weighted magnetic resonance imaging the current study investigated cortical and subcortical volumes in 37 children with DCD, aged 8 to 12 years, and 48 controls of a similar age. Regional brain volumes of the thalamus, basal ganglia, cerebellum and primary motor and sensory cortices were extracted using the FreeSurfer recon-all pipeline and compared between groups. Reduced volumes within both the left and right pallidum (Left: F = 4.43, p = 0.039; Right: F = 5.24, p = 0.025) were observed in children with DCD; however, these results did not withstand correction for multiple comparisons. These findings provide preliminary evidence of altered subcortical brain structure in DCD. Future studies that examine the morphology of these subcortical regions are highly encouraged in order replicate these findings.

Tracking Huntington's Disease Progression Using Motor, Functional, Cognitive, and Imaging Markers

BACKGROUND

Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants.

OBJECTIVE

The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups.

METHODS

Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups.

RESULTS

Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression.

CONCLUSIONS

Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Validating Automated Segmentation Tools in the Assessment of Caudate Atrophy in Huntington's Disease

Background: Neuroimaging shows considerable promise in generating sensitive and objective outcome measures for therapeutic trials across a range of neurodegenerative conditions. For volumetric measures the current gold standard is manual delineation, which is unfeasible for samples sizes required for large clinical trials. Methods: Using a cohort of early Huntington's disease (HD) patients (n = 46) and controls (n = 35), we compared the performance of four automated segmentation tools (FIRST, FreeSurfer, STEPS, MALP-EM) with manual delineation for generating cross-sectional caudate volume, a region known to be vulnerable in HD. We then examined the effect of each of these baseline regions on the ability to detect change over 15 months using the established longitudinal Caudate Boundary Shift Integral (cBSI) method, an automated longitudinal pipeline requiring a baseline caudate region as an input. Results: All tools, except Freesurfer, generated significantly smaller caudate volumes than the manually derived regions. Jaccard indices showed poorer levels of overlap between each automated segmentation and manual delineation in the HD patients compared with controls. Nevertheless, each method was able to demonstrate significant group differences in volume (p < 0.001). STEPS performed best qualitatively as well as quantitively in the baseline analysis. Caudate atrophy measures generated by the cBSI using automated baseline regions were largely consistent with those derived from a manually segmented baseline, with STEPS providing the most robust cBSI values across both control and HD groups. Conclusions: Atrophy measures from the cBSI were relatively robust to differences in baseline segmentation technique, suggesting that fully automated pipelines could be used to generate outcome measures for clinical trials.

Multidimensional Apathy: The Utility of the Dimensional Apathy Scale in Huntington's Disease

Background

Apathy is a disorder of motivation common to Huntington's disease (HD). Recent conceptual frameworks suggest that apathy is not unitary but consists of discrete subtypes ("dimensions"). Which of the proposed dimensions are preferentially affected in HD, and how these dimensions evolve with disease progression is unknown.

Objectives

The Dimensional Apathy Scale (DAS) separates apathy into Executive, Initiation and Emotional subscales. Using the DAS, we aimed to: 1) Determine the apathy subtypes prevalent in HD; 2) Compare the DAS against a unitary measure of apathy (Apathy Evaluation Scale, AES); 3) Assess the reliability of self- and observer-ratings; and 4) Determine the relationship between the DAS, and disease burden, total functional capacity (TFC) and the AES.

Method

Fifty pre-manifest, 51 manifest-HD, 87 controls, and 50 HD-observers completed the DAS, AES, and TFC.

Results

Manifest-HD participants had the highest levels of apathy across all dimensions (30.4% on Executive subscale, 34.8% on Initiation subscale, and 15.2% on Emotional subscale), relative to pre-manifest and control participants. Self- and observer-ratings on the DAS did not differ. Hierarchical regressions across the entire gene-expanded sample showed that scores on the Initiation subscale correlated with AES scores; higher Executive subscale scores were related to higher disease burden; and Emotional subscale scores with lower total functional capacity.

Conclusions

In this first study of the DAS in HD, manifest-HD participants were more apathetic than pre-manifest and control participants across all apathy subtypes. The DAS may be a useful tool for measuring different aspects of apathy in people with HD.

Validation Study of a German Cognitive Battery for Huntington's Disease: Relationship Between Cognitive Performance, Functional Decline, and Disease Burden

Objective

Cognitive decline is a key characteristic of Huntington’s disease (HD). This study aimed to investigate the diagnostic accuracy of a cognitive battery with six tests used by most HD research centers to assess cognitive impairment in HD.

Method

In total, 106 HD patients in different disease stages with more (HD-CD, N = 30) and less cognitive impairments (HD-NC, N = 70) and 100 healthy controls (NC) were matched by age, sex, and education and were examined using a standardized protocol including cognitive, motor, and functional assessments.

Results

One-way between-groups analysis of variance showed that controls performed significantly better than HD patients and that HD-NC significantly outperformed HD-CD patients in all cognitive tests (NC > HD-NC > HD-CD), with all Games-Howell post-hoc tests p < .001. Analyses using area under the receiver-operating characteristic curve (AUC) disclosed the diagnostic accuracy of all tests included in the battery to discriminate between NC and HD patients with AUC ranging from 0.809 to 0.862 (all p < .001) and between HD-CD and HD-NC patients with AUC ranging from 0.833 to 0.899 (all p < .001). In both analysis, Stroop Color Naming Test showed the highest discriminative potential. Additional analyses showed that cognitive deficits in all domains progressed with disease duration. Moreover, cognitive performance correlated with the severity of motor and functional impairment (all p < .001) and with the Disease Burden Score regardless of disease duration and age.

Conclusion

Our results indicate that the cognitive battery is a suitable tool for assessing cognitive impairment in HD.

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.

Dissociable Motivational Deficits in Pre-manifest Huntington's Disease

Motivation is characterized by a willingness to overcome both cognitive and physical effort costs. Impairments in motivation are common in striatal disorders, such as Huntington’s disease (HD), but whether these impairments are isolated to particular domains of behavior is controversial. We ask whether HD differentially affects the willingness of individuals to overcome cognitive versus physical effort. We tested 20 individuals with pre-manifest HD and compared their behavior to 20 controls. Across separate trials, participants made choices about how much cognitive or physical effort they were willing to invest for reward. Our key results were that individuals with pre-manifest HD were less willing than controls to invest cognitive effort but were no different in their overall preference for physical effort. These results cannot be explained by group differences in neuropsychological or psychiatric profiles. This dissociation of cognitive- and physical-effort-based decisions provides important evidence for separable, domain-specific mechanisms of motivation.

We examine cognitive and physical effort discounting in pre-manifest HD

Individuals with pre-manifest HD are less cognitively motivated than controls

There are no differences in physical motivation between the two groups

This dissociation is not confounded by neuropsychological or psychiatric factors

Diffusion Tensor Imaging in Preclinical and Human Studies of Huntington's Disease: What Have we Learned so Far?

BACKGROUND

Huntington's Disease is an irreversible neurodegenerative disease characterized by the progressive deterioration of specific brain nerve cells. The current evaluation of cellular and physiological events in patients with HD relies on the development of transgenic animal models. To explore such events in vivo, diffusion tensor imaging has been developed to examine the early macro and microstructural changes in brain tissue. However, the gap in diffusion tensor imaging findings between animal models and clinical studies and the lack of microstructural confirmation by histological methods has questioned the validity of this method.

OBJECTIVE

This review explores white and grey matter ultrastructural changes associated to diffusion tensor imaging, as well as similarities and differences between preclinical and clinical Huntington's Disease studies.

METHODS

A comprehensive review of the literature using online-resources was performed (Pub- Med search).

RESULTS

Similar changes in fractional anisotropy as well as axial, radial and mean diffusivities were observed in white matter tracts across clinical and animal studies. However, comparative diffusion alterations in different grey matter structures were inconsistent between clinical and animal studies.

CONCLUSION

Diffusion tensor imaging can be related to specific structural anomalies in specific cellular populations. However, some differences between animal and clinical studies could derive from the contrasting neuroanatomy or connectivity across species. Such differences should be considered before generalizing preclinical results into the clinical practice. Moreover, current limitations of this technique to accurately represent complex multicellular events at the single micro scale are real. Future work applying complex diffusion models should be considered.

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.

Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE-HD cohort

Smaller manually-segmented amygdala volumes have been associated with poorer motor and cognitive function in Huntington's disease (HD). Manual segmentation is the gold standard in terms of accuracy; however, automated methods may be necessary in large samples. Automated segmentation accuracy has not been determined for the amygdala in HD. We aimed to determine which of three automated approaches would most accurately segment amygdalae in HD: FreeSurfer, FIRST, and ANTS nonlinear registration followed by FIRST segmentation. T1-weighted images for the IMAGE-HD cohort including 35 presymptomatic HD (pre-HD), 36 symptomatic HD (symp-HD), and 34 healthy controls were segmented using FreeSurfer and FIRST. For the third approach, images were nonlinearly registered to an MNI template using ANTS, then segmented using FIRST. All automated methods overestimated amygdala volumes compared with manual segmentation. Dice overlap scores, indicating segmentation accuracy, were not significantly different between automated approaches. Manually segmented volumes were most statistically differentiable between groups, followed by those segmented by FreeSurfer, then ANTS/FIRST. FIRST-segmented volumes did not differ between groups. All automated methods produced a bias where volume overestimation was more severe for smaller amygdalae. This bias was subtle for FreeSurfer, but marked for FIRST, and moderate for ANTS/FIRST. Further, FreeSurfer introduced a hemispheric bias not evident with manual segmentation, producing larger right amygdalae by 8%. To assist choice of segmentation approach, we provide sample size estimation graphs based on sample size and other factors. If automated segmentation is employed in samples of the current size, FreeSurfer may effectively distinguish amygdala volume between controls and HD.

Morphometric in vivo evidence of thalamic atrophy correlated with cognitive and motor dysfunction in Huntington's disease: The IMAGE-HD study

In Huntington's disease (HD), neurodegeneration causes progressive atrophy to the striatum, cortical areas, and white matter tracts - components of corticostriatal circuitry. Such processes may affect the thalamus, a key circuit node. We investigated whether differences in dorsal thalamic morphology were detectable in HD, and whether thalamic atrophy was associated with neurocognitive, neuropsychiatric and motor dysfunction. Magnetic resonance imaging scans and clinical outcome measures were obtained from 34 presymptomatic HD (pre-HD), 29 early symptomatic HD (symp-HD), and 26 healthy control individuals who participated in the IMAGE-HD study. Manual region of interest (ROI) segmentation was conducted to measure dorsal thalamic volume, and thalamic ROI underwent shape analysis using the spherical harmonic point distribution method. The symp-HD group had significant thalamic volumetric reduction and global shape deflation, indicative of atrophy, compared to pre-HD and control groups. Thalamic atrophy significantly predicted neurocognitive and motor dysfunction within the symp-HD group only. Thalamic morphology differentiates symp-HD from pre-HD and healthy individuals. Thalamic changes may be one of the structural bases (endomorphotypes), of the endophenotypic neurocognitive and motor manifestations of disease. Future research should continue to investigate the thalamus as a potential in vivo biomarker of disease progression in HD.

Striatal morphology and neurocognitive dysfunction in Huntington disease: The IMAGE-HD study

We aimed to investigate the relationship between striatal morphology in Huntington disease (HD) and measures of motor and cognitive dysfunction. MRI scans, from the IMAGE-HD study, were obtained from 36 individuals with pre-symptomatic HD (pre-HD), 37 with early symptomatic HD (symp-HD), and 36 healthy matched controls. The neostriatum was manually segmented and a surface-based parametric mapping protocol derived two pointwise shape measures: thickness and surface dilation ratio. Significant shape differences were detected between all groups. Negative associations were detected between lower thickness and surface area shape measure and CAG repeats, disease burden score, and UHDRS total motor score. In symp-HD, UPSIT scores were correlated with higher thickness in left caudate tail and surface dilation ratio in left posterior putamen; Stroop scores were positively correlated with the thickness of left putamen head and body. Self-paced tapping (slow) was correlated with higher thickness and surface dilation ratio in the right caudate in symp-HD and with bilateral putamen in pre-HD. Self-paced tapping (fast) was correlated with higher surface dilation ratio in the right anterior putamen in symp-HD. Shape changes correlated with functional measures subserved by corticostriatal circuits, suggesting that the neostriatum is a potentially useful structural basis for characterisation of endophenotypes of HD.

Altered Cortical Morphometry in Pre-manifest Huntington's Disease: Cross-sectional Data from the IMAGE-HD Study

Huntington's disease (HD) is an inherited progressive neurodegenerative disease mainly associated with subcortical striatal atrophy. There is also strong evidence showing cerebral atrophy and cortical thinning; however, limited research has investigated altered patterns of cortical folding in this disease. Here, we investigated cortical morphometry via both gyrification index (GI, a measure of cortical folding) and cortical thinning. The localized GI was examined using a novel GI, namely LB-GI. As part of a cross-sectional study, pre-manifest (pre-HD) individuals (n = 29) and matched controls (n = 29) underwent T1-MRI using data from the IMAGE-HD study. Compared to controls, pre-HD individuals demonstrated significantly lower GI in the left superior parietal and the right superior temporal regions and greater cortical thinning in the bilateral pre-central and the superior frontal gyri and left caudal middle frontal gyrus, as well as the superior parietal region. For the first time, we report evidence of abnormal localized cortical folding in pre-HD. We also provide evidence that cortical folding impacts different regions of the cortical surface more so than cortical thickness. As a result, we propose a potential new biological marker that may increase our understanding of the neuropathology of HD. Greater understanding of brain changes could inform new therapeutic approaches and target points for clinical trials.

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.

Reduced amygdala volumes are related to motor and cognitive signs in Huntington's disease: The IMAGE-HD study

In Huntington's disease (HD), the presence of neurodegeneration in brain regions other than the striatum has been recently gaining attention. The amygdala is one such area, which has been investigated in only eight structural magnetic resonance imaging studies to date, but with inconsistent findings. This is the largest MRI study to date examining manually traced amygdala volumes in HD participants and the relationship of amygdala volumes to clinical measures of HD. Our study included 35 healthy control participants, and groups of 35 pre-symptomatic, and 36 symptomatic HD participants. When comparing the pre-symptomatic and symptomatic HD groups together against the control group, amygdala volumes were significantly lower in HD than controls and in symptomatic HD than pre-symptomatic HD. When examining relationships between amygdala volumes and clinical measures of HD, significantly smaller amygdala volumes were associated with worse motor and cognitive signs. For pre-symptomatic HD participants who were close to disease onset, smaller amygdala volumes were also associated with higher levels of anxiety symptoms. These findings suggest that the amygdala is affected in pre-symptomatic and symptomatic HD, and that the amygdala is related to the clinical profile of HD before onset of motor symptoms.

Impairments in Spatiotemporal Gait Adaptation During Obstacle Navigation in Huntington's Disease

BACKGROUND

Navigating obstacles whilst walking might be associated with poorer balance and a higher risk of falling in individuals with symptomatic Huntington's disease (symp-HD). However, this issue has not been investigated within the literature.

OBJECTIVE

A unique obstacle navigation experiment was designed to examine adaptive gait patterns in order to identify spatiotemporal gait characteristics that might be associated with poorer balance and a higher risk of falling in symp-HD.

METHOD

Sixteen diagnosed symp-HD participants and 16 age- and sex-matched healthy controls were included. Gait was examined in 3 experimental conditions: baseline walking, walking while navigating around 1 obstacle, and walking while navigating around 2 obstacles. Navigation around obstacle walks was divided into three step phases (approach, navigation, recovery). Group differences in gait variables were analyzed at baseline and during walking for each obstacle condition respectively. Gait variables were also correlated with the Berg Balance Scale (BBS) and Timed Up and Go (TUG) test.

RESULTS

Symp-HD participants, compared with controls, performed significantly poorer on most gait variables during baseline walking. Symp-HD participants significantly decreased their step-length while navigating around 1 obstacle, and increased their step-time while navigating around 1 and 2 obstacles. There were no significant group differences in step-width. Variables associated with navigating around obstacles correlated significantly with BBS and TUG clinical tools, which have been associated in the literature with an increased risk of falling in symp-HD.

CONCLUSION

These findings could aid clinicians in better managing risk of falls in people with Huntington's disease through targeted and effective strategies.

Emotion Evaluation and Social Inference Impairments in Huntington's Disease

BACKGROUND

Huntington's disease (HD) is an inherited neurodegenerative disorder characterised by motor, cognitive and neuropsychiatric symptoms. Recent research has established that individuals with HD display reduced discrimination of emotional facial expressions and impaired higher-order social cognitive skills, including 'theory of mind'.

OBJECTIVE

This study aimed to further characterise the emotion evaluation and theory of mind deficits in HD in an ecologically-valid context, and determine their impact on socially-relevant functional abilities.

METHODS

A sample of 17 HD participants and 24 healthy controls were assessed using The Awareness of Social Inference Test (TASIT), together with additional self- and informant rated measures of cognition, social communication, empathy and neuropsychiatric symptoms.

RESULTS

Participants with HD showed significant difficulties in evaluating negative emotions, and understanding sincere, sarcastic and 'paradoxical sarcastic' statements, compared with controls. The ability to evaluate positive emotions was negatively correlated with behavioural problems, but no other clinical, behavioural or communication measures correlated significantly with TASIT subscales.

CONCLUSIONS

These findings suggest that social cognitive difficulties in symptomatic HD may be underpinned by more generalised impairments, related to deciphering social exchanges, as opposed to a selective theory of mind deficit. Such difficulties have the potential to place significant strain on interpersonal relationships, and thus warrant thorough clinical assessment, using ecologically-valid tools, to promote early detection and development of person-centred interventions.

Striatal morphology correlates with frontostriatal electrophysiological motor processing in Huntington's disease: an IMAGE-HD study

BACKGROUND

Huntington's disease (HD) causes progressive atrophy to the striatum, a critical node in frontostriatal circuitry. Maintenance of motor function is dependent on functional connectivity of these premotor, motor, and dorsolateral frontostriatal circuits, and structural integrity of the striatum itself. We aimed to investigate whether size and shape of the striatum as a measure of frontostriatal circuit structural integrity was correlated with functional frontostriatal electrophysiological neural premotor processing (contingent negative variation, CNV), to better understand motoric structure-function relationships in early HD.

METHODS

Magnetic resonance imaging (MRI) scans and electrophysiological (EEG) measures of premotor processing were obtained from a combined HD group (12 presymptomatic, 7 symptomatic). Manual segmentation of caudate and putamen was conducted with subsequent shape analysis. Separate correlational analyses (volume and shape) included covariates of age, gender, intracranial volume, and time between EEG and MRI.

RESULTS

Right caudate volume correlated with early CNV latency over frontocentral regions and late CNV frontally, whereas right caudate shape correlated with early CNV latency centrally. Left caudate volume correlated with early CNV latency over centroparietal regions and late CNV frontally. Right and left putamen volumes correlated with early CNV latency frontally, and right and left putamen shape/volume correlated with parietal CNV slope.

CONCLUSIONS

Timing (latency) and pattern (slope) of frontostriatal circuit-mediated premotor functional activation across scalp regions were correlated with abnormalities in structural integrity of the key frontostriatal circuit component, the striatum (size and shape). This was accompanied by normal reaction times, suggesting it may be undetected in regular tasks due to preserved motor "performance." Such differences in functional activation may reflect atrophy-based frontostriatal circuitry despecialization and/or compensatory recruitment of additional brain regions.

Diffusion imaging studies of Huntington's disease: A meta-analysis

BACKGROUND

Diffusion tensor imaging (DTI) could detect abnormal brain microstructural alterations. DTI studies of Huntington's Disease(HD) have yielded inconsistent results.

OBJECTIVE

To integrate the existing DTI studies of HD and explore the validity of DTI to detect microstructural damages in HD brain via meta-analysis.

METHODS

Systematic and comprehensive searches of the databases were performed for DTI studies of HD. The data from the studies that met our inclusion criteria were extracted and analyzed using the CMA2 software. Random effect models were utilized to minimize the potential between-study heterogeneity. One-way sensitivity analysis was conducted to test the robustness of the results.

RESULTS

The meta-analysis included 140 pre-symptomatic HD (PreHD), 235 symptomatic HD (SymHD) patients and 302 controls, revealing significantly increased fractional anisotropy (FA) in the caudate, putamen, and globus pallidus, while decreased FA in the corpus callosum of both PreHD and SymHD patients compared with controls. In addition, significantly increased mean diffusivity (MD) was identified in the putamen and thalamus of both PreHD and SymHD patients, and in the caudate of SymHD patients, while no significant difference in MD in the caudate of PreHD patients. In the corpus callosum, there was a significant increase of radial diffusivity and axial diffusivity in SymHD patients compared with controls. Meta-regression showed gender-based difference in MD values of the caudate.

CONCLUSIONS

Our meta-analysis provides further evidence that DTI detects microstructural damage of both white matter and gray matter even in PreHD gene carriers. MD is less sensitive than FA in detecting structural changes in PreHD.

Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network

Degeneracy is a fundamental source of biological robustness, complexity and evolvability in many biological systems. However, degeneracy is often confused with redundancy. Furthermore, the quantification of degeneracy has not been addressed for realistic neuronal networks. The objective of this paper is to characterize degeneracy in neuronal network models via quantitative mathematic measures. Firstly, we establish Hodgkin-Huxley neuronal networks with Newman-Watts small world network architectures. Secondly, in order to calculate the degeneracy, redundancy and complexity in the ensuing networks, we use information entropy to quantify the information a neuronal response carries about the stimulus - and mutual information to measure the contribution of each subset of the neuronal network. Finally, we analyze the interdependency of degeneracy, redundancy and complexity - and how these three measures depend upon network architectures. Our results suggest that degeneracy can be applied to any neuronal network as a formal measure, and degeneracy is distinct from redundancy. Qualitatively degeneracy and complexity are more highly correlated over different network architectures, in comparison to redundancy. Quantitatively, the relationship between both degeneracy and redundancy depends on network coupling strength: both degeneracy and redundancy increase with complexity for small coupling strengths; however, as coupling strength increases, redundancy decreases with complexity (in contrast to degeneracy, which is relatively invariant). These results suggest that the degeneracy is a general topologic characteristic of neuronal networks, which could be applied quantitatively in neuroscience and connectomics.

Multimodal imaging biomarkers in premanifest and early Huntington's disease: 30-month IMAGE-HD data

BACKGROUND

The discovery of potential disease-modifying therapies in a neurodegenerative condition like Huntington's disease depends on the availability of sensitive biomarkers that reflect decline across disease stages and that are functionally and clinically relevant.

AIMS

To quantify macrostructural and microstructural changes in participants with premanifest and symptomatic Huntington's disease over 30 months, and to establish their functional and clinical relevance.

METHOD

Multimodal magnetic resonance imaging study measuring changes in macrostructural (volume) and microstructural (diffusivity) measures in 40 patients with premanifest Huntington's disease, 36 patients with symptomatic Huntington's disease and 36 healthy control participants over three testing sessions spanning 30 months.

RESULTS

Relative to controls, there was greater longitudinal atrophy in participants with symptomatic Huntington's disease in whole brain, grey matter, caudate and putamen, as well as increased caudate fractional anisotropy; caudate volume loss was the only measure to differ between premanifest Huntington's disease and control groups. Changes in caudate volume and fractional anisotropy correlated with each other and neurocognitive decline; caudate volume loss also correlated with clinical and disease severity.

CONCLUSIONS

Caudate neurodegeneration, especially atrophy, may be the most suitable candidate surrogate biomarker for consideration in the development of upcoming clinical trials.

Iron accumulation in the basal ganglia in Huntington's disease: cross-sectional data from the IMAGE-HD study

OBJECTIVES

To measure iron accumulation in the basal ganglia in Huntington's disease (HD) using quantitative susceptibility mapping (QSM), and to ascertain its relevance in terms of clinical and disease severity.

METHODS

In this cross-sectional investigation, T2* weighted imaging was undertaken on 31 premanifest HD, 32 symptomatic HD and 30 control participants as part of the observational IMAGE-HD study. Group differences in iron accumulation were ascertained with QSM. Associations between susceptibility values and disease severity were also investigated.

RESULTS

Compared with controls, both premanifest and symptomatic HD groups showed significantly greater iron content in pallidum, putamen and caudate. Additionally, iron accumulation in both putamen and caudate was significantly associated with disease severity.

CONCLUSIONS

These findings provide the first evidence that QSM is sensitive to iron deposition in subcortical target areas across premanifest and symptomatic stages of HD. Such findings could open up new avenues for biomarker development and therapeutic intervention.

Subjective sleep problems in Huntington's disease: A pilot investigation of the relationship to brain structure, neurocognitive, and neuropsychiatric function

Subjective reports of sleep disturbance are a common feature of Huntington's disease (HD); however, there is limited research investigating the relationship between sleep problems with changes in brain and behaviour. This study aimed to investigate whether subjective reports of sleep problems in HD are associated with brain volume, neurocognitive decline, and neuropsychiatric symptoms. This retrospective pilot study used brain volume, neurocognitive and neuropsychiatric data from premanifest (pre-HD) and symptomatic HD (symp-HD). Subjective sleep problem was measured using the sleep item of the Beck's Depression Inventory-II (BDI-II). Pre-HD individuals reporting sleep problems had significantly poorer neuropsychiatric outcomes compared to those not reporting sleep problems. In the symp-HD group, those with sleep problems had significantly accelerated thalamic degeneration and poorer neuropsychiatric outcomes compared to those without sleep problems. There was no relationship between subjective sleep problems and neurocognitive measures. These findings suggest an association between subjective sleep disturbance, neuropathology, and development of neuropsychiatric symptoms in HD. Further studies using quantitative EEG-based monitoring of sleep in HD and changes in the brain and behaviour will be necessary to establish the causal nature of this relationship.

Cognitive assessment strategies in Huntington's disease research

The number of studies examining cognition in Huntington's disease (HD) has increased dramatically in recent decades, and cognitive research methods in HD have become much more sophisticated. In this review, we provide a summary of the advances in cognitive research in HD to date, and outline the key considerations for researchers planning to include cognitive assessment in their studies of HD. In particular, we discuss consideration of structure-function relationships, selection of tests appropriate to the population, choice of materials and issues of intellectual property, consideration of variables which can confound studies of cognition in HD, practice effects, and specific issues for multi-site research. Finally, we discuss future directions for cognitive assessment in HD research.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice.

Abnormal Electrophysiological Motor Responses in Huntington's Disease: Evidence of Premanifest Compensation

Background

Huntington's disease (HD) causes progressive motor dysfunction through characteristic atrophy. Changes to neural structure begin in premanifest stages yet individuals are able to maintain a high degree of function, suggesting involvement of supportive processing during motor performance. Electroencephalography (EEG) enables the investigation of subtle impairments at the neuronal level, and possible compensatory strategies, by examining differential activation patterns. We aimed to use EEG to investigate neural motor processing (via the Readiness Potential; RP), premotor processing and sensorimotor integration (Contingent Negative Variation; CNV) during simple motor performance in HD.

Methods

We assessed neural activity associated with motor preparation and processing in 20 premanifest (pre-HD), 14 symptomatic HD (symp-HD), and 17 healthy controls. Participants performed sequential tapping within two experimental paradigms (simple tapping; Go/No-Go). RP and CNV potentials were calculated separately for each group.

Results

Motor components and behavioural measures did not distinguish pre-HD from controls. Compared to controls and pre-HD, symp-HD demonstrated significantly reduced relative amplitude and latency of the RP, whereas controls and pre-HD did not differ. However, early CNV was found to significantly differ between control and pre-HD groups, due to enhanced early CNV in pre-HD.

Conclusions

For the first time, we provide evidence of atypical activation during preparatory processing in pre-HD. The increased activation during this early stage of the disease may reflect ancillary processing in the form of recruitment of additional neural resources for adequate motor preparation, despite atrophic disruption to structure and circuitry. We propose an early adaptive compensation mechanism in pre-HD during motor preparation.

Characterising Upper Limb Movements in Huntington's Disease and the Impact of Restricted Visual Cues

BACKGROUND

Voluntary motor deficits are a common feature in Huntington's disease (HD), characterised by movement slowing and performance inaccuracies. This deficit may be exacerbated when visual cues are restricted.

OBJECTIVE

To characterize the upper limb motor profile in HD with various levels of difficulty, with and without visual targets.

METHODS

Nine premanifest HD (pre-HD), nine early symptomatic HD (symp-HD) and nine matched controls completed a motor task incorporating Fitts' law, a model of human movement enabling the quantification of movement timing, via the manipulation of task difficulty (i.e., target size, and distance between targets). The task required participants to make reciprocal movements under cued and blind conditions. Dwell times (time stationary between movements), speed, accuracy and variability of movements were compared between groups.

RESULTS

Symp-HD showed significantly prolonged and less consistent movement times, compared with controls and pre-HD. Furthermore, movement planning and online control were significantly impaired in symp-HD, compared with controls and pre-HD, evidenced by prolonged dwell times and deceleration times. Speed and accuracy were comparable across groups, suggesting that group differences observed in movement time, variability, dwell time and deceleration time were evident over and above simple performance measures. The presence of cues resulted in greater movement time variability in symp-HD, compared with pre-HD and controls, suggesting that the deficit in movement consistency manifested only in response to targeted movements.

CONCLUSIONS

Collectively, these findings provide evidence of a deficiency in both motor planning, particularly in relation to movement timing and online control, which became exacerbated as a function of task difficulty during symp-HD stages. These variables may provide a more sensitive measure of motor dysfunction than speed and/or accuracy alone in symp-HD.

Discovering networks altered by potential threat ("anxiety") using quadratic discriminant analysis

Researchers have only recently begun using functional neuroimaging to explore the human response to periods of sustained anxious anticipation, namely potential threat. Here, we investigated brain responses acquired with functional MRI during an instructed threat of shock paradigm used to create sustained periods of aversive anticipation. In this re-analysis of previously published data, we employed quadratic discriminant analysis to classify the multivariate pattern of whole-brain functional connectivity and to identify connectivity changes during periods of potential threat. Our method identifies clusters with altered connectivity on a voxelwise basis, thus eschewing the need to define regions a priori. Classifier generalization was evaluated by testing on data from participants not used during training. Robust classification between threat and safe contexts was possible, and inspection of "diagnostic features" revealed altered functional connectivity involving the intraparietal sulcus, task-negative regions, striatum, and anterior cingulate cortex. We anticipate that the proposed method will prove useful to experimenters wishing to identify large-scale functional networks that distinguish between experimental conditions or groups.

Diffusion-MRI in neurodegenerative disorders

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.

Cognitive interventions to enhance neural compensation in Huntington's disease

SUMMARY 

In Huntington's disease (HD), there is growing evidence of neural compensation during neurodegeneration, and that these processes might be modifiable by environmental factors. Cognitive intervention to improve brain function has been trialled only to a very limited extent in HD; however, it has shown promise in other neurodegenerative diseases. In this review, we discuss the evidence for the use of cognitive intervention to boost neural compensation in HD, and find it has potential to delay clinical decline, particularly if applied early in the disease process. Randomized controlled trials of cognitive intervention in HD should be implemented as a next step to gauging the efficacy of this approach to improve outcomes for those with the HD gene.

Volumetric analysis of the hypothalamus in Huntington Disease using 3T MRI: the IMAGE-HD Study

Huntington disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Non-motor symptoms and signs such as psychiatric disturbances, sleep problems and metabolic dysfunction are part of the disease manifestation. These aspects may relate to changes in the hypothalamus, an area of the brain involved in the regulation of emotion, sleep and metabolism. Neuropathological and imaging studies using both voxel-based morphometry (VBM) of magnetic resonance imaging (MRI) as well as positron emission tomography (PET) have demonstrated pathological changes in the hypothalamic region during early stages in symptomatic HD. In this investigation, we aimed to establish a robust method for measurements of the hypothalamic volume in MRI in order to determine whether the hypothalamic dysfunction in HD is associated with the volume of this region. Using T1-weighted imaging, we describe a reproducible delineation procedure to estimate the hypothalamic volume which was based on the same landmarks used in histologically processed postmortem hypothalamic tissue. Participants included 36 prodromal HD (pre-HD), 33 symptomatic HD (symp-HD) and 33 control participants who underwent MRI scanning at baseline and 18 months follow-up as part of the IMAGE-HD study. We found no evidence of cross-sectional or longitudinal changes between groups in hypothalamic volume. Our results suggest that hypothalamic pathology in HD is not associated with volume changes.