Robust Markers and Sample Sizes for Multicenter Trials of Huntington Disease.

OBJECTIVE: The identification of sensitive biomarkers is essential to validate therapeutics for Huntington disease (HD). We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD. METHODS: We used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease-affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers. RESULTS: We identified a set of common anatomical regions that have similarly large standardized effect sizes (>0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of >80% was achieved for the caudate (n = 661), pallidum (n = 687), and nonventricular CSF (n = 939), and, crucially, these imaging markers provided greater power than standard clinical markers. INTERPRETATION: Our findings provide the first cross-study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. ANN NEUROL 2020;87:751-762.

Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures.

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

Apathy Is Related to Cognitive Control and Striatum Volumes in Prodromal Huntington's Disease.

OBJECTIVES: Apathy is a debilitating symptom of Huntington's disease (HD) and manifests before motor diagnosis, making it an excellent therapeutic target in the preclinical phase of Huntington's disease (prHD). HD is a neurological genetic disorder characterized by cognitive and motor impairment, and psychiatric abnormalities. Apathy is not well characterized within the prHD. In previous literature, damage to the caudate and putamen has been correlated with increased apathy in other neurodegenerative and movement disorders. The objective of this study was to determine whether apathy severity in individuals with prHD is related to striatum volumes and cognitive control. We hypothesized that, within prHD individuals, striatum volumes and cognitive control scores would be related to apathy. METHODS: We constructed linear mixed models to analyze striatum volumes and cognitive control, a composite measure that includes tasks assessing with apathy scores from 797 prHD participants. The outcome variable for each model was apathy, and the independent variables for the four separate models were caudate volume, putamen volume, cognitive control score, and motor symptom score. We also included depression as a covariate to ensure that our results were not solely related to mood. RESULTS: Caudate and putamen volumes, as well as measures of cognitive control, were significantly related to apathy scores even after controlling for depression. CONCLUSIONS: The behavioral apathy expressed by these individuals was related to regions of the brain commonly associated with isolated apathy, and not a direct result of mood symptoms. (JINS, 2019, 25, 462-469).

Correction to: SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research.

This paper had published originally without open access, but has since been republished with open access.

Robust automated constellation-based landmark detection in human brain imaging.

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research.

Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks .

Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration.

INTRODUCTION: Huntington's disease (HD) is a genetic neurodegenerative disorder that primarily affects striatal neurons. Striatal volume loss is present years before clinical diagnosis; however, white matter degradation may also occur prior to diagnosis. Diffusion-weighted imaging (DWI) can measure microstructural changes associated with degeneration that precede macrostructural changes. DWI derived measures enhance understanding of degeneration in prodromal HD (pre-HD). METHODS: As part of the PREDICT-HD study, N = 191 pre-HD individuals and 70 healthy controls underwent two or more (baseline and 1-5 year follow-up) DWI, with n = 649 total sessions. Images were processed using cutting-edge DWI analysis methods for large multicenter studies. Diffusion tensor imaging (DTI) metrics were computed in selected tracts connecting the primary motor, primary somato-sensory, and premotor areas of the cortex with the subcortical caudate and putamen. Pre-HD participants were divided into three CAG-Age Product (CAP) score groups reflecting clinical diagnosis probability (low, medium, or high probabilities). Baseline and longitudinal group differences were examined using linear mixed models. RESULTS: Cross-sectional and longitudinal differences in DTI measures were present in all three CAP groups compared with controls. The high CAP group was most affected. CONCLUSIONS: This is the largest longitudinal DWI study of pre-HD to date. Findings showed DTI differences, consistent with white matter degeneration, were present up to a decade before predicted HD diagnosis. Our findings indicate a unique role for disrupted connectivity between the premotor area and the putamen, which may be closely tied to the onset of motor symptoms in HD. Hum Brain Mapp 38:1460-1477, 2017. © 2017 Wiley Periodicals, Inc.

Prefrontal cortex white matter tracts in prodromal Huntington disease.

Huntington disease (HD) is most widely known for its selective degeneration of striatal neurons but there is also growing evidence for white matter (WM) deterioration. The primary objective of this research was to conduct a large-scale analysis using multisite diffusion-weighted imaging (DWI) tractography data to quantify diffusivity properties along major prefrontal cortex WM tracts in prodromal HD. Fifteen international sites participating in the PREDICT-HD study collected imaging and neuropsychological data on gene-positive HD participants without a clinical diagnosis (i.e., prodromal) and gene-negative control participants. The anatomical prefrontal WM tracts of the corpus callosum (PFCC), anterior thalamic radiations (ATRs), inferior fronto-occipital fasciculi (IFO), and uncinate fasciculi (UNC) were identified using streamline tractography of DWI. Within each of these tracts, tensor scalars for fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity coefficients were calculated. We divided prodromal HD subjects into three CAG-age product (CAP) groups having Low, Medium, or High probabilities of onset indexed by genetic exposure. We observed significant differences in WM properties for each of the four anatomical tracts for the High CAP group in comparison to controls. Additionally, the Medium CAP group presented differences in the ATR and IFO in comparison to controls. Furthermore, WM alterations in the PFCC, ATR, and IFO showed robust associations with neuropsychological measures of executive functioning. These results suggest long-range tracts essential for cross-region information transfer show early vulnerability in HD and may explain cognitive problems often present in the prodromal stage. Hum Brain Mapp 36:3717-3732, 2015. © 2015 Wiley Periodicals, Inc.

A longitudinal study of magnetic resonance spectroscopy Huntington's disease biomarkers.

Putaminal metabolites examined using cross-sectional magnetic resonance spectroscopy (MRS) can distinguish pre-manifest and early Huntington's Disease (HD) individuals from controls. An ideal biomarker, however, will demonstrate longitudinal change over short durations. The objective here was to evaluate longitudinal in vivo brain metabolite profiles in HD over 24 months. Eighty-four participants (30 controls, 25 pre-manifest HD, 29 early HD) recruited as part of TRACK-HD were imaged at baseline, 12 months, and 24 months using 3T MRS of left putamen. Automated putaminal volume measurement was performed simultaneously. To quantify partial volume effects, spectroscopy was performed in a second, white matter voxel adjacent to putamen in six subjects. Subjects underwent TRACK-HD motor assessment. Statistical analyses included linear regression and one-way analysis of variance (ANOVA). At all time-points N-acetyl aspartate and total N-acetyl aspartate (NAA), neuronal integrity markers, were lower in early HD than in controls. Total NAA was lower in pre-manifest HD than in controls, whereas the gliosis marker myo-inositol (MI) was robustly elevated in early HD. Metabolites were stable over 24 months with no longitudinal change. Total NAA was not markedly different in adjacent white matter than putamen, arguing against partial volume confounding effects in cross-sectional group differences. Total NAA correlations with disease burden score suggest that this metabolite may be useful in identifying neurochemical responses to therapeutic agents. We demonstrate almost consistent group differences in putaminal metabolites in HD-affected individuals compared with controls over 24 months. Future work establishing spectroscopy as an HD biomarker should include multi-site assessments in large, pathologically diverse cohorts.

Diffusion weighted imaging of prefrontal cortex in prodromal Huntington's disease.

Huntington's disease (HD) is a devastating neurodegenerative disease with no effective disease-modifying treatments. There is considerable interest in finding reliable indicators of disease progression to judge the efficacy of novel treatments that slow or stop disease onset before debilitating signs appear. Diffusion-weighted imaging (DWI) may provide a reliable marker of disease progression by characterizing diffusivity changes in white matter (WM) in individuals with prodromal HD. The prefrontal cortex (PFC) may play a role in HD progression due to its prominent striatal connections and documented role in executive function. This study uses DWI to characterize diffusivity in specific regions of PFC WM defined by FreeSurfer in 53 prodromal HD participants and 34 controls. Prodromal HD individuals were separated into three CAG-Age Product (CAP) groups (16 low, 22 medium, 15 high) that indexed baseline progression. Statistically significant increases in mean diffusivity (MD) and radial diffusivity (RD) among CAP groups relative to controls were seen in inferior and lateral PFC regions. For MD and RD, differences among controls and HD participants tracked with baseline disease progression. The smallest difference was for the low group and the largest for the high group. Significant correlations between Trail Making Test B (TMTB) and mean fractional anisotropy (FA) and/or RD paralleled group differences in mean MD and/or RD in several right hemisphere regions. The gradient of effects that tracked with CAP group suggests DWI may provide markers of disease progression in future longitudinal studies as increasing diffusivity abnormalities in the lateral PFC of prodromal HD individuals.

White matter fractional anisotropy is inversely related to anxious symptoms in older adults with atherosclerosis.

OBJECTIVE: Clinical anxiety disorders are associated with white matter hyperintensities and diffusion abnormalities measured using diffusion tensor imaging. However, it is not known if this association extends into individuals with mild anxious symptoms without formal diagnosis, in those who are older, or in those who have atherosclerosis. The current study explores whether white matter integrity and/or organization significantly associates with anxious symptoms in older adults with and without atherosclerosis. METHODS: We recruited older adults (ages 55-90 years); 35 with clinically diagnosed atherosclerotic vascular disease (AVD) and 22 without AVD. Anxious symptoms were measured using the validated Symptom Checklist-90-Revised. Fractional anisotropy (FA), a proxy for white matter organization and health, was measured in the white matter globally, by lobe, and in several smaller regions of interest suggested by the literature. Partial correlations between anxious symptoms and FA were calculated, controlling for significant covariates. RESULTS: Participants with and without AVD did not differ in severity of anxious symptom endorsement. There was a unique inverse relationship between white matter health and anxious symptoms in the AVD participants, but not in healthy comparisons. Significant relationships were observed in the superior longitudinal fasciculus (r = -0.476, df = 32, p = 0.004), as well as the cingulum bundle, the frontal lobes, and the parietal lobes. CONCLUSIONS: Anxiety symptoms uniquely correlated with low FA in older adults with atherosclerosis. These findings may have implications for future research on the topic of anxiety in aging and vascular disease and warrant replication.

Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model.

Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (- 9.06 cm3), gray matter (- 4.37% 95 CI - 7.41; - 1.83), caudate (- 0.16%, 95 CI - 0.24; - 0.08) and putamen (- 0.11% 95 CI - 0.23; - 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ 3.42%, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (- 8.27%, 95 CI - 10.1; - 5.56) and cerebrospinal fluid (+ 6.88%, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation.

Smaller intracranial volume in prodromal Huntington's disease: evidence for abnormal neurodevelopment.

Huntington's disease is an autosomal dominant brain disease. Although conceptualized as a neurodegenerative disease of the striatum, a growing number of studies challenge this classic concept of Huntington's disease aetiology. Intracranial volume is the tissue and fluid within the calvarium and is a representation of the maximal brain growth obtained during development. The current study reports intracranial volume obtained from an magnetic resonance imaging brain scan in a sample of subjects (n = 707) who have undergone presymptomatic gene testing. Participants who are gene-expanded but not yet manifesting the disease (prodromal Huntington's disease) are compared with subjects who are non-gene expanded. The prodromal males had significantly smaller intracranial volume measures with a mean volume that was 4% lower compared with controls. Although the prodromal females had smaller intracranial volume measures compared with their controls, this was not significant. The current findings suggest that mutant huntingtin can cause abnormal development, which may contribute to the pathogenesis of Huntington's disease.

A Novel Porcine Model of CLN2 Batten Disease that Recapitulates Patient Phenotypes.

CLN2 Batten disease is a lysosomal disorder in which pathogenic variants in CLN2 lead to reduced activity in the enzyme tripeptidyl peptidase 1. The disease typically manifests around 2 to 4 years of age with developmental delay, ataxia, seizures, inability to speak and walk, and fatality between 6 and 12 years of age. Multiple Cln2 mouse models exist to better understand the etiology of the disease; however, these models are unable to adequately recapitulate the disease due to differences in anatomy and physiology, limiting their utility for therapeutic testing. Here, we describe a new CLN2R208X/R208X porcine model of CLN2 disease. We present comprehensive characterization showing behavioral, pathological, and visual phenotypes that recapitulate those seen in CLN2 patients. CLN2R208X/R208X miniswine present with gait abnormalities at 6 months of age, ERG waveform declines at 6-9 months, vision loss at 11 months, cognitive declines at 12 months, seizures by 15 months, and early death at 18 months due to failure to thrive. CLN2R208X/R208X miniswine also showed classic storage material accumulation and glial activation in the brain at 6 months, and cortical atrophy at 12 months. Thus, the CLN2R208X/R208X miniswine model is a valuable resource for biomarker discovery and therapeutic development in CLN2 disease.

Longitudinal change in regional brain volumes in prodromal Huntington disease.

OBJECTIVE: As therapeutics are being developed to target the underlying neuropathology of Huntington disease, interest is increasing in methodologies for conducting clinical trials in the prodromal phase. This study was designed to examine the potential utility of structural MRI measures as outcome measures for such trials. METHODS: Data are presented from 211 prodromal individuals and 60 controls, scanned both at baseline and at the 2-year follow-up. Prodromal participants were divided into groups based on proximity to estimated onset of diagnosable clinical disease: far (>15 years from estimated onset), mid (9-15 years) and near (<9 years). Volumetric measurements of caudate, putamen, total striatum, globus pallidus, thalamus, total grey and white matter and cerebrospinal fluid were performed. RESULTS: All prodromal groups showed a faster rate of atrophy than controls in striatum, total brain and cerebral white matter (especially in the frontal lobe). Neither prodromal participants nor controls showed any significant longitudinal change in cortex (either total cortical grey or within individual lobes). When normal age-related atrophy (ie, change observed in the control group) was taken into account, there was more statistically significant disease-related atrophy in white matter than in striatum. CONCLUSION: Measures of volume change in striatum and white-matter volume, particularly in the frontal lobe, may serve as excellent outcome measures for future clinical trials in prodromal Huntington disease. Clinical trials using white matter or striatal volume change as an outcome measure will be most efficient if the sample is restricted to individuals who are within 15 years of estimated onset of diagnosable disease.

The ANTsX ecosystem for quantitative biological and medical imaging.

The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

Cerebral cortex structure in prodromal Huntington disease.

Neuroimaging studies of subjects who are gene-expanded for Huntington Disease, but not yet diagnosed (termed prodromal HD), report that the cortex is "spared," despite the decrement in striatal and cerebral white-matter volume. Measurement of whole-cortex volume can mask more subtle, but potentially clinically relevant regional changes in volume, thinning, or surface area. The current study addressed this limitation by evaluating cortical morphology of 523 prodromal HD subjects. Participants included 693 individuals enrolled in the PREDICT-HD protocol. Of these participants, 523 carried the HD gene mutation (prodromal HD group); the remaining 170 were non gene-expanded and served as the comparison group. Based on age and CAG repeat length, gene-expanded subjects were categorized as "Far from onset," "Midway to onset," "Near onset," and "already diagnosed." MRI scans were processed using FreeSurfer. Cortical volume, thickness, and surface area were not significantly different between the Far from onset group and controls. However, beginning in the Midway to onset group, the cortex showed significant volume decrement, affecting most the posterior and superior cerebral regions. This pattern progressed when evaluating the groups further into the disease process. Areas that remained mostly unaffected included ventral and medial regions of the frontal and temporal cortex. Morphologic changes were mostly in thinning as surface area did not substantially change in most regions. Early in the course of HD, the cortex shows changes that are manifest as cortical thinning and are most robust in the posterior and superior regions of the cerebrum.

SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization.

PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Concurrent Cross-Sectional and Longitudinal Analyses of Multivariate White Matter Profiles and Clinical Functioning in Pre-Diagnosis Huntington Disease.

BACKGROUND: Gray matter (GM) atrophy in the striatum and across the brain is a consistently reported feature of the Huntington Disease (HD) prodrome. More recently, widespread prodromal white matter (WM) degradation has also been detected. However, longitudinal WM studies are limited and conflicting, and most analyses comparing WM and clinical functioning have also been cross-sectional. OBJECTIVE: We simultaneously assessed changes in WM and cognitive and motor functioning at various prodromal HD stages. METHODS: Data from 1,336 (1,047 prodromal, 289 control) PREDICT-HD participants were analyzed (3,700 sessions). MRI images were used to create GM, WM, and cerebrospinal fluid probability maps. Using source-based morphometry, independent component analysis was applied to WM probability maps to extract covarying spatial patterns and their subject profiles. WM profiles were analyzed in two sets of linear mixed model (LMM) analyses: one to compare WM profiles across groups cross-sectionally and longitudinally, and one to concurrently compare WM profiles and clinical variables cross-sectionally and longitudinally within each group. RESULTS: Findings illustrate widespread prodromal changes in GM-adjacent-WM, with premotor, supplementary motor, middle frontal and striatal changes early in the prodrome that subsequently extend sub-gyrally with progression. Motor functioning agreed most with WM until the near-onset prodromal stage, when Stroop interference was the best WM indicator. Across groups, Trail-Making Test part A outperformed other cognitive variables in its similarity to WM, particularly cross-sectionally. CONCLUSIONS: Results suggest that distinct regions coincide with cognitive compared to motor functioning. Furthermore, at different prodromal stages, distinct regions appear to align best with clinical functioning. Thus, the informativeness of clinical measures may vary according to the type of data available (cross-sectional or longitudinal) as well as age and CAG-number.

Association of CAG Repeats With Long-term Progression in Huntington Disease.

IMPORTANCE: In Huntington disease (HD), mutation severity is defined by the length of the CAG trinucleotide sequence, a well-known predictor of clinical onset age. The association with disease trajectory is less well characterized. Quantifiable summary measures of trajectory applicable over decades of early disease progression are lacking. An accurate model of the age-CAG association with early progression is critical to clinical trial design, informing both sample size and intervention timing. OBJECTIVE: To succinctly capture the decades-long early progression of HD and its dependence on CAG repeat length. DESIGN, SETTING, AND PARTICIPANTS: Prospective study at 4 academic HD treatment and research centers. Participants were the combined sample from the TRACK-HD and Track-On HD studies consisting of 290 gene carriers (presymptomatic to stage II), recruited from research registries at participating centers, and 153 nonbiologically related controls, generally spouses or friends. Recruitment was targeted to match a balanced, prespecified spectrum of age, CAG repeat length, and diagnostic status. In the TRACK-HD and Track-On HD studies, 13 and 5 potential participants, respectively, failed study screening. Follow-up ranged from 0 to 6 years. The study dates were January 2008 to November 2014. These analyses were performed between December 2015 and January 2019. MAIN OUTCOMES AND MEASURES: The outcome measures were principal component summary scores of motor-cognitive function and of brain volumes. The main outcome was the association of these scores with age and CAG repeat length. RESULTS: We analyzed 2065 visits from 443 participants (247 female [55.8%]; mean [SD] age, 44.4 [10.3] years). Motor-cognitive measures were highly correlated and had similar CAG repeat length-dependent associations with age. A composite summary score accounted for 67.6% of their combined variance. This score was well approximated by a score combining 3 items (total motor score, Symbol Digit Modalities Test, and Stroop word reading) from the Unified Huntington's Disease Rating Scale. For either score, initial progression age and then acceleration rate were highly CAG repeat length dependent. The acceleration continues through at least stage II disease. In contrast, 3 distinct patterns emerged among brain measures (basal ganglia, gray matter, and a combination of whole-brain, ventricular, and white matter volumes). The basal ganglia pattern showed considerable change in even the youngest participants but demonstrated minimal acceleration of loss with aging. Each clinical and brain summary score was strongly associated with the onset and rate of decline in total functional capacity. CONCLUSIONS AND RELEVANCE: Results of this study suggest that succinct summary measures of function and brain loss characterize HD progression across a wide disease span. CAG repeat length strongly predicts their decline rate. This work aids our understanding of the age and CAG repeat length-dependent association between changes in the brain and clinical manifestations of HD.