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Background In this study, we asked whether differences in striatal activity during a reinforcement learning (RL) task with gain and loss domains could be one of the earliest functional imaging features associated with carrying the Huntington's disease (HD) gene. Based on previous work, we hypothesized that HD gene carriers would show either neural or behavioral asymmetry between gain and loss learning. Methods We recruited 35 HD gene carriers, expected to demonstrate onset of motor symptoms in an average of 26 years, and 35 well-matched gene-negative control subjects. Participants were placed in a functional magnetic resonance imaging scanner, where they completed an RL task in which they were required to learn to choose between abstract stimuli with the aim of gaining rewards and avoiding losses. Task behavior was modeled using an RL model, and variables from this model were used to probe functional magnetic resonance imaging data. Results In comparison with well-matched control subjects, gene carriers more than 25 years from motor onset showed exaggerated striatal responses to gain-predicting stimuli compared with loss-predicting stimuli (p = .002) in our RL task. Using computational analysis, we also found group differences in striatal representation of stimulus value (p = .0004). We found no group differences in behavior, cognitive scores, or caudate volumes. Conclusions Behaviorally, gene carriers 9 years from predicted onset have been shown to learn better from gains than from losses. Our data suggest that a window exists in which HD-related functional neural changes are detectable long before associated behavioral change and 25 years before predicted motor onset. These represent the earliest functional imaging differences between HD gene carriers and control subjects.

An important step towards the development of treatments for cognitive impairment in ageing and neurodegenerative diseases is to identify genetic and environmental modifiers of cognitive function and understand the mechanism by which they exert an effect. In Huntington’s disease, the most common autosomal dominant dementia, a small number of studies have identified intellectual enrichment, i.e. a cognitively stimulating lifestyle and genetic polymorphisms as potential modifiers of cognitive function. The aim of our study was to further investigate the relationship and interaction between genetic factors and intellectual enrichment on cognitive function and brain atrophy in Huntington’s disease. For this purpose, we analysed data from Track-HD, a multi-centre longitudinal study in Huntington’s disease gene carriers and focused on the role of intellectual enrichment (estimated at baseline) and the genes FAN1, MSH3, BDNF, COMT and MAPT in predicting cognitive decline and brain atrophy. We found that carrying the 3a allele in the MSH3 gene had a positive effect on global cognitive function and brain atrophy in multiple cortical regions, such that 3a allele carriers had a slower rate of cognitive decline and atrophy compared with non-carriers, in agreement with its role in somatic instability. No other genetic predictor had a significant effect on cognitive function and the effect of MSH3 was independent of intellectual enrichment. Intellectual enrichment also had a positive effect on cognitive function; participants with higher intellectual enrichment, i.e. those who were better educated, had higher verbal intelligence and performed an occupation that was intellectually engaging, had better cognitive function overall, in agreement with previous studies in Huntington’s disease and other dementias. We also found that intellectual enrichment interacted with the BDNF gene, such that the positive effect of intellectual enrichment was greater in Met66 allele carriers than non-carriers. A similar relationship was also identified for changes in whole brain and caudate volume; the positive effect of intellectual enrichment was greater for Met66 allele carriers, rather than for non-carriers. In summary, our study provides additional evidence for the beneficial role of intellectual enrichment and carrying the 3a allele in MSH3 in cognitive function in Huntington’s disease and their effect on brain structure.

Converging lines of evidence from several models, and post‐mortem human brain tissue studies, support the involvement of the kynurenine pathway (KP) in Huntington's disease (HD) pathogenesis. Quantifying KP metabolites in HD biofluids is desirable, both to study pathobiology and as a potential source of biomarkers to quantify pathway dysfunction and evaluate the biochemical impact of therapeutic interventions targeting its components. In a prospective single‐site controlled cohort study with standardised collection of cerebrospinal fluid (CSF), blood, phenotypic and imaging data, we used high‐performance liquid‐chromatography to measure the levels of KP metabolites—tryptophan, kynurenine, kynurenic acid, 3‐hydroxykynurenine, anthranilic acid and quinolinic acid—in CSF and plasma of 80 participants (20 healthy controls, 20 premanifest HD and 40 manifest HD). We investigated short‐term stability, intergroup differences, associations with clinical and imaging measures and derived sample‐size calculation for future studies. Overall, KP metabolites in CSF and plasma were stable over 6 weeks, displayed no significant group differences and were not associated with clinical or imaging measures. We conclude that the studied metabolites are readily and reliably quantifiable in both biofluids in controls and HD gene expansion carriers. However, we found little evidence to support a substantial derangement of the KP in HD, at least to the extent that it is reflected by the levels of the metabolites in patient‐derived biofluids., Converging lines of evidence from non‐human models support the involvement of the kynurenine pathway in Huntington's disease. However, evidence in living humans is limited. In a prospective study with standardised collection of cerebrospinal fluid and blood, we used high‐performance liquid‐chromatography to analyse this pathway. We concluded that the selected metabolites are readily and reliably quantifiable. Yet, we found little evidence to support a substantial derangement of the kynurenine pathway in Huntington's disease, at least to the extent that it is reflected by the levels of the metabolites in patient‐derived biofluids. 3‐HK, 3‐hydroxykynurenine; KYNA, kynurenic acid; QUIN, quinolinic acid.

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.

Magnetic resonance spectroscopy (MRS) is a non-invasive method of exploring cerebral metabolism. In Huntington’s disease, altered MRS-determined concentrations of several metabolites have been described; however, findings are often discrepant and longitudinal studies of metabolite trajectory are lacking. MRS metabolites may represent a valuable source of biomarkers, thus their relationship with established biofluid and structural imaging markers of disease progression require further exploration to assess prognostic value and elucidate biochemical pathways associated with neurodegeneration. In a prospective single-site controlled cohort study with standardised collection of CSF, blood, phenotypic and imaging data, we used MRS to evaluate metabolic profiles in the putamen of 56 participants at baseline (15 healthy controls, 15 premanifest and 26 manifest gene expansion carriers) and at 2-year follow-up. Intergroup differences and associations with established measures were assessed cross-sectionally using generalized linear models and partial correlation, controlling for age and CAG repeat length. We report no significant groupwise differences in metabolite concentration but found several metabolites to be associated with measures of disease progression; however, only two relationships were replicated across both time points, with total Creatine (creatine + phosphocreatine) and myo-inositol displaying significant associations with reduced caudate volume. Although relationships were observed between MRS metabolites and biofluid measures, these were not consistent across time points. To further assess prognostic value of the metabolites, we examined whether baseline MRS values, or rate of change, predicted subsequent change in established measures of disease progression. Several associations were found but were inconsistent across known indicators of disease progression. Finally, longitudinal mixed effects models, controlling for age, revealed no significant change in metabolite concentration over time in gene expansion carriers. Altogether, our findings show some interesting cross-sectional associations between select metabolites, namely total creatine and myo-inositol, and markers of disease progression, potentially highlighting the proposed roles of neuroinflammation and metabolic dysfunction in disease pathogenesis. However, the absence of group differences, inconsistency between baseline and follow-up, and lack of clear longitudinal change over two years suggests that MRS metabolites have limited potential as biomarkers in Huntington’s disease.

Proton magnetic resonance spectroscopy is a non-invasive method of exploring cerebral metabolism. In Huntington’s disease, altered proton magnetic resonance spectroscopy-determined concentrations of several metabolites have been described; however, findings are often discrepant and longitudinal studies are lacking. Proton magnetic resonance spectroscopy metabolites may represent a source of biomarkers, thus their relationship with established markers of disease progression require further exploration to assess prognostic value and elucidate pathways associated with neurodegeneration. In a prospective single-site controlled cohort study with standardized collection of CSF, blood, phenotypic and volumetric imaging data, we used 3 T proton magnetic resonance spectroscopy in conjunction with the linear combination of model spectra method to quantify seven metabolites (total n-acetylaspartate, total creatine, total choline, myo-inositol, GABA, glutamate and glutathione) in the putamen of 59 participants at baseline (15 healthy controls, 15 premanifest and 29 manifest Huntington’s disease gene expansion carriers) and 48 participants at 2-year follow-up (12 healthy controls, 13 premanifest and 23 manifest Huntington’s disease gene expansion carriers). Intergroup differences in concentration and associations with CSF and plasma biomarkers; including neurofilament light chain and mutant Huntingtin, volumetric imaging markers; namely whole brain, caudate, grey matter and white matter volume, measures of disease progression and cognitive decline, were assessed cross-sectionally using generalized linear models and partial correlation. We report no significant groupwise differences in metabolite concentration at baseline but found total creatine and total n-acetylaspartate to be significantly reduced in manifest compared with premanifest participants at follow-up. Additionally, total creatine and myo-inositol displayed significant associations with reduced caudate volume across both time points in gene expansion carriers. Although relationships were observed between proton magnetic resonance spectroscopy metabolites and biofluid measures, these were not consistent across time points. To further assess prognostic value, we examined whether baseline proton magnetic resonance spectroscopy values, or rate of change, predicted subsequent change in established measures of disease progression. Several associations were found but were inconsistent across known indicators of disease progression. Finally, longitudinal mixed-effects models revealed glutamine + glutamate to display a slow linear decrease over time in gene expansion carriers. Altogether, our findings show some evidence of reduced total n-acetylaspartate and total creatine as the disease progresses and cross-sectional associations between select metabolites, namely total creatine and myo-inositol, and markers of disease progression, potentially highlighting the proposed roles of neuroinflammation and metabolic dysfunction in disease pathogenesis. However, the absence of consistent group differences, inconsistency between baseline and follow-up, and lack of clear longitudinal change suggests that proton magnetic resonance spectroscopy metabolites have limited potential as Huntington’s disease biomarkers.

Upregulation of functional network connectivity in the presence of structural degeneration is seen in the premanifest stages of Huntington’s disease (preHD) 10–15 years from clinical diagnosis. However, whether widespread network connectivity changes are seen in gene carriers much further from onset has yet to be explored. We characterized functional network connectivity throughout the brain and related it to a measure of disease pathology burden (CSF neurofilament light, NfL) and measures of structural connectivity in asymptomatic gene carriers, on average 24 years from onset. We related these measurements to estimates of cortical and subcortical gene expression. We found no overall differences in functional (or structural) connectivity anywhere in the brain comparing control and preHD participants. However, increased functional connectivity, particularly between posterior cortical areas, correlated with increasing CSF NfL level in preHD participants. Using the Allen Human Brain Atlas and expression-weighted cell-type enrichment analysis, we demonstrated that this functional connectivity upregulation occurred in cortical regions associated with regional expression of genes specific to neuronal cells. This relationship was validated using single-nucleus RNAseq data from post-mortem Huntington’s disease and control brains showing enrichment of neuronal-specific genes that are differentially expressed in Huntington’s disease. Functional brain networks in asymptomatic preHD gene carriers very far from disease onset show evidence of upregulated connectivity correlating with increased disease burden. These changes occur among brain areas that show regional expression of genes specific to neuronal GABAergic and glutamatergic cells.

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.

Background Characterizing changing brain structure in neurodegeneration is fundamental to understanding long-term effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington’s disease (HD) gene carriers undergo progressive brain changes during the course of disease, yet the long-term trajectory of cortical atrophy is not well defined. Given that genetic therapies currently tested in HD are primarily expected to target the cortex, understanding atrophy across this region is essential. Methods Capitalizing on a unique longitudinal dataset with a minimum of 3 and maximum of 7 brain scans from 49 HD gene carriers and 49 age-matched control subjects, we implemented a novel dynamical systems approach to infer patterns of regional neurodegeneration over 10 years. We use Bayesian hierarchical modeling to map participant- and group-level trajectories of atrophy spatially and temporally, additionally relating atrophy to the genetic marker of HD (CAG-repeat length) and motor and cognitive symptoms. Results We show, for the first time, that neurodegenerative changes exhibit complex temporal dynamics with substantial regional variation around the point of clinical diagnosis. Although widespread group differences were seen across the cortex, the occipital and parietal regions undergo the greatest rate of cortical atrophy. We have established links between atrophy and genetic markers of HD while demonstrating that specific cortical changes predict decline in motor and cognitive performance. Conclusions HD gene carriers display regional variability in the spatial pattern of cortical atrophy, which relates to genetic factors and motor and cognitive symptoms. Our findings indicate a complex pattern of neuronal loss, which enables greater characterization of HD progression.

Background Pathological processes in Huntington's disease (HD) begin many years prior to symptom onset. Recently we demonstrated that in a premanifest cohort approximately 24 years from predicted disease onset, despite intact function, there was evidence of subtle neurodegeneration. Here, we use novel imaging techniques to determine whether macro- and micro-structural changes can be detected across the whole-brain in the same cohort. Methods 62 premanifest HD (PreHD) and 61 controls from the HD Young Adult Study (HD-YAS) were included. Grey and white matter volume, diffusion weighted imaging (DWI) measures of white matter microstructure, multiparametric maps (MPM) estimating myelin and iron content from magnetization transfer (MT), proton density (PD), longitudinal relaxation (R1) and effective transverse relaxation (R2*), and myelin g-ratio were examined. Group differences between PreHD and controls were assessed; associations between all imaging metrics and disease burden and CSF neurofilament light (NfL) were also performed. Volumetric and MPM results were corrected at a cluster-wise value of familywise error (FWE) 0.05. Diffusion and g-ratio results were corrected via threshold-free cluster enhancement at FWE 0.05. Findings We showed significantly increased R1 and R2*, suggestive of increased iron, in the putamen, globus pallidum and external capsule of PreHD participants. There was also a significant association between lower cortical R2*, suggestive of reduced myelin or iron, and higher CSF NfL in the frontal lobe and the parieto-occipital cortices. No other results were significant at corrected levels. Interpretation Increased iron in subcortical structures and the surrounding white matter is a feature of very early PreHD. Furthermore, increases in CSF NfL were linked to microstructural changes in the posterior parietal-occipital cortex, a region previously shown to undergo some of the earliest cortical changes in HD. These findings suggest that disease related process are occurring in both subcortical and cortical regions more than 20 years from predicted disease onset.

The longitudinal dynamics of the most promising biofluid biomarker candidates for Huntington’s disease (HD)— mutant huntingtin (mHTT) and neurofilament light (NfL)—are incompletely defined. Characterizing changes in these candidates during disease progression could increase our understanding of disease pathophysiology and help the identification of effective therapies. In an 80-participant cohort over 24 months, mHTT in cerebrospinal fluid (CSF), as well as NfL in CSF and blood, had distinct longitudinal trajectories in HD mutation carriers compared with controls. Baseline analyte values predicted clinical disease status, subsequent clinical progression, and brain atrophy, better than did the rate of change in analytes. Overall, NfL was a stronger monitoring and prognostic biomarker for HD than mHTT. Nonetheless, mHTT has prognostic value and might be a valuable pharmacodynamic marker for huntingtin-lowering trials.

Brain-derived neurotrophic factor (BDNF) is implicated in the survival of striatal neurons. BDNF function is reduced in Huntington’s disease (HD), possibly because mutant huntingtin impairs its cortico-striatal transport, contributing to striatal neurodegeneration. The BDNF trophic pathway is a therapeutic target, and blood BDNF has been suggested as a potential biomarker for HD, but BDNF has not been quantified in cerebrospinal fluid (CSF) in HD.BDNF in CSF and plasma in the HD-CSF cohort (20 pre-manifest and 40 manifest HD mutation carriers and 20 age and gender-matched controls) were quantified using conventional ELISAs and an ultra-sensitive immunoassay.BDNF concentration was below the limit of detection of the conventional ELISAs, raising doubt about previous CSF reports in neurodegeneration. Using the ultra-sensitive method, BDNF concentration was quantifiable in all samples but did not differ between controls and HD mutation carriers in CSF or plasma, was not associated with clinical scores or MRI brain volumetric measures, and had poor ability to discriminate controls from HD mutation carriers, and premanifest from manifest HD.BDNF in CSF and plasma is unlikely to be a biomarker of HD progression, and urge caution in interpreting studies where conventional ELISA was used to quantify CSF BDNF.

ObjectivesCognitive flexibility, which is key for adaptive decision-making, engages prefrontal cortex (PFC)-striatal circuitry and is impaired in both manifest and premanifest Huntington’s disease (pre-HD). The aim of this study was to examine cognitive flexibility in a far from onset pre-HD cohort to determine whether an early impairment exists and if so, whether fronto-striatal circuits were associated with this deficit.MethodsIn the present study, we examined performance of 51 pre-HD participants (mean age=29.22 (SD=5.71) years) from the HD Young Adult Study cohort and 53 controls matched for age, sex and IQ, on the Cambridge Neuropsychological Test Automated Battery (CANTAB) Intra-Extra Dimensional Set-Shift (IED) task. This cohort is unique as it is the furthest from disease onset comprehensively studied to date (mean years=23.89 (SD=5.96) years). The IED task measures visual discrimination learning, cognitive flexibility and specifically attentional set-shifting. We used resting-state functional MRI to examine whether the functional connectivity between specific fronto-striatal circuits was dysfunctional in pre-HD, compared with controls, and whether these circuits were associated with performance on the critical extradimensional shift stage.ResultsOur results demonstrated that the CANTAB IED task detects a mild early impairment in cognitive flexibility in a pre-HD group far from onset. Attentional set-shifting was significantly related to functional connectivity between the ventrolateral PFC and ventral striatum in healthy controls and to functional connectivity between the dorsolateral PFC and caudate in pre-HD participants.ConclusionWe postulate that this incipient impairment of cognitive flexibility may be associated with intrinsically abnormal functional connectivity of fronto-striatal circuitry in pre-HD.

BACKGROUND: Disease-modifying treatments are in development for Huntington's disease; crucial to their success is to identify a timepoint in a patient's life when there is a measurable biomarker of early neurodegeneration while clinical function is still intact. We aimed to identify this timepoint in a novel cohort of young adult premanifest Huntington's disease gene carriers (preHD) far from predicted clinical symptom onset. METHODS: We did the Huntington's disease Young Adult Study (HD-YAS) in the UK. We recruited young adults with preHD and controls matched for age, education, and sex to ensure each group had at least 60 participants with imaging data, accounting for scan fails. Controls either had a family history of Huntington's disease but a negative genetic test, or no known family history of Huntington's disease. All participants underwent detailed neuropsychiatric and cognitive assessments, including tests from the Cambridge Neuropsychological Test Automated Battery and a battery assessing emotion, motivation, impulsivity and social cognition (EMOTICOM). Imaging (done for all participants without contraindications) included volumetric MRI, diffusion imaging, and multiparametric mapping. Biofluid markers of neuronal health were examined using blood and CSF collection. We did a cross-sectional analysis using general least-squares linear models to assess group differences and associations with age and CAG length, relating to predicted years to clinical onset. Results were corrected for multiple comparisons using the false discovery rate (FDR), with FDR 0·16). CSF neurofilament light protein (NfL), plasma NfL, and CSF YKL-40 were elevated in this far-from-onset preHD cohort compared with controls (FDR

Background Structural brain MRI measures are frequently examined in both healthy and clinical groups, so an understanding of how these measures vary over time is desirable. Purpose To test the stability of structural brain MRI measures over time. Population In all, 112 healthy volunteers across four sites. Study type Retrospective analysis of prospectively acquired data. Field strength/sequence 3 T, magnetization prepared - rapid gradient echo, and single-shell diffusion sequence. Assessment Diffusion, cortical thickness, and volume data from the sensorimotor network were assessed for stability over time across 3 years. Two sites used a Siemens MRI scanner, two sites a Philips scanner. Statistical tests The stability of structural measures across timepoints was assessed using intraclass correlation coefficients (ICC) for absolute agreement, cutoff ≥0.80, indicating high reliability. Mixed-factorial analysis of variance (ANOVA) was used to examine between-site and between-scanner type differences in individuals over time. Results All cortical thickness and gray matter volume measures in the sensorimotor network, plus all diffusivity measures (fractional anisotropy plus mean, axial and radial diffusivities) for primary and premotor cortices, primary somatosensory thalamic connections, and the cortico-spinal tract met ICC. The majority of measures differed significantly between scanners, with a trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and volume measures than sites using Philips scanners. Data conclusion Levels of reliability over time for all tested structural MRI measures were generally high, indicating that any differences between measurements over time likely reflect underlying biological differences rather than inherent methodological variability. Level of evidence 4. Technical efficacy stage 1.

The longitudinal dynamics of the most promising biofluid biomarker candidates for Huntington’s disease (HD) – mutant huntingtin (mHTT) and neurofilament light (NfL) – are incompletely defined, but could help understand the natural history of the disease and how these biomarkers might help in therapeutic development and the clinic. In an 80-participant cohort over 24 months, mHTT in cerebrospinal fluid (CSF), and NfL in CSF and blood, had distinct longitudinal trajectories in HD mutation carriers compared with controls. Baseline analyte values predicted clinical disease status and subsequent clinical progression and brain atrophy, better than did the rate of change in analytes. Overall NfL was a stronger monitoring and prognostic biomarker for HD than mHTT. Nonetheless, mHTT possesses prognostic value and is a valuable pharmacodynamic marker for huntingtin-lowering trials.

Objective Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages. Methods We employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides. Results The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers. Interpretation We used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.

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

Background Investigating early white matter (WM) change in Huntington's disease (HD) can improve our understanding of the way in which disease spreads from the striatum. Objectives We provide a detailed characterization of pathology‐related WM change in HD. We first examined WM microstructure using diffusion‐weighted imaging and then investigated both underlying biological properties of WM and products of WM damage including iron, myelin plus neurofilament light, a biofluid marker of axonal degeneration—in parallel with the mutant huntingtin protein. Methods We examined WM change in HD gene carriers from the HD–CSFcohort, baseline visit. We used standard‐diffusion magnetic resonance imaging to measure metrics including fractional anisotropy, a marker of WM integrity, and diffusivity; a novel diffusion model (neurite orientation dispersion and density imaging) to measure axonal density and organization; T1‐weighted and T2‐weighted structural magnetic resonance imaging images to derive proxy iron content and myelin‐contrast measures; and biofluid concentrations of neurofilament light (in cerebrospinal fluid (CSF) and plasma) and mutant huntingtin protein (in CSF). Results HD gene carriers displayed reduced fractional anisotropy and increased diffusivity when compared with controls, both of which were also associated with disease progression, CSF, and mutant huntingtin protein levels. HD gene carriers also displayed proxy measures of reduced myelin contrast and iron in the striatum. Conclusion Collectively, these findings present a more complete characterization of HD‐related microstructural brain changes. The correlation between reduced fractional anisotropy, increased axonal orientation, and biofluid markers suggest that axonal breakdown is associated with increased WM degeneration, whereas higher quantitative T2 signal and lower myelin‐contrast may indicate a process of demyelination limited to the striatum.

The neurodegenerative process is typically slowly progressive and complex. While simple models of neurodegeneration suggest that brain changes progress at a near constant rate, previous research shows regional variation within the temporal progression of atrophy, indicating that over the course of neurodegeneration, different regions may undergo changing rates of atrophy. Characterization of long-term dynamic brain changes in neurodegeneration requires both extensive longitudinal MRI datasets and an advanced modeling framework. Until recently, both of these elements were not available. Here, we implement a novel dynamic systems approach to infer patterns of regional progression spatially and temporally in a unique longitudinal dataset with up to seven annual individual brain scans per participant from 49 Huntington’s Disease (HD) gene-carriers. We map participant-and group-level trajectories of cortical atrophy in HD using a decade of data that encompasses motor symptom onset and, for the first time, show that neurodegenerative brain changes exhibit complex temporal dynamics of atrophy with substantial regional variation in progressive cortical atrophy. Some fronto-occipital cortical areas show an almost constant rate of atrophy, while medial-inferior temporal areas undergo only minor change. Interestingly, cortical sensory-motor areas were found to show a noticeable acceleration of atrophy following HD diagnosis. Furthermore, we establish links between individual atrophy and genetic markers of HD (CAG repeat length), as well as showing that cortical motor network changes predict subsequent decline in task-based motor performance, demonstrating face-validity of the model. Our findings highlight the complex pattern of dynamic cortical change occurring in HD that can help to resolve the biological underpinnings of HD progression.

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

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

Multiple targeted therapeutics for Huntington's disease are now in clinical trials, including intrathecally delivered compounds. Previous research suggests that CSF dynamics may be altered in Huntington's disease, which could be of paramount relevance to intrathecal drug delivery to the brain. To test this hypothesis, we conducted a prospective cross‐sectional study comparing people with early stage Huntington's disease with age‐ and gender‐matched healthy controls. CSF peak velocity, mean velocity and mean flow at the level of the cerebral aqueduct, and sub‐arachnoid space in the upper and lower spine, were quantified using phase contrast MRI. We calculated Spearman's rank correlations, and tested inter‐group differences with Wilcoxon rank‐sum test. Ten people with early Huntington's disease, and 10 controls were included. None of the quantified measures was associated with potential modifiers of CSF dynamics (demographics, osmolality, and brain volumes), or by known modifiers of Huntington's disease (age and HTT CAG repeat length); and no significant differences were found between the two studied groups. While external validation is required, the attained results are sufficient to conclude tentatively that a clinically relevant alteration of CSF dynamics – that is, one that would justify dose‐adjustments of intrathecal drugs – is unlikely to exist in Huntington's disease.

Structural connectomes derived using diffusion tractography are increasingly used to investigate white matter connectivity in neurological diseases. However inherent biases in diffusion tractography algorithms may lead to both false negatives and false positives in connectome construction. A range of graph thresholding approaches and more recently several streamline filtering algorithms have been developed to address these issues. However there is no consensus in the literature regarding the best available approach. Using a cohort of Huntington’s disease patients and healthy controls we compared the effect of several graph thresholding strategies: proportional, absolute, consensus and consistency thresholding, with and without streamline filtering, using Spherical Deconvolution Informed Filtering of tractograms (SIFT2) algorithm. We examined the effect of thresholding strategies on the stability of graph theory metrics and the sensitivity of these measures in neurodegeneration. We show that while a number of graph thresholding procedures result in stable metrics across thresholds, the detection of group differences is highly variable. We also showed that the application of streamline filtering using SIFT2 resultes in better detection of group differences and stronger clinical correlations. We therefore conclude that the application of SIFT2 streamline filtering without graph thresholding may be sufficient for structural connectome construction.

Huntington's disease (HD) is a genetic progressive neurodegenerative disorder, caused by a mutation in the HTT gene, for which there is currently no cure. The identification of sensitive indicators of disease progression and therapeutic outcome could help the development of effective strategies for treating HD. We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) protein concentrations in cerebrospinal fluid (CSF) and blood in parallel with clinical evaluation and magnetic resonance imaging in premanifest and manifest HD mutation carriers. Among HD mutation carriers, NfL concentrations in plasma and CSF correlated with all nonbiofluid measures more closely than did CSF mHTT concentration. Longitudinal analysis over 4 to 8 weeks showed that CSF mHTT, CSF NfL, and plasma NfL concentrations were highly stable within individuals. In our cohort, concentration of CSF mHTT accurately distinguished between controls and HD mutation carriers, whereas NfL concentration, in both CSF and plasma, was able to segregate premanifest from manifest HD. In silico modeling indicated that mHTT and NfL concentrations in biofluids might be among the earliest detectable alterations in HD, and sample size prediction suggested that low participant numbers would be needed to incorporate these measures into clinical trials. These findings provide evidence that biofluid concentrations of mHTT and NfL have potential for early and sensitive detection of alterations in HD and could be integrated into both clinical trials and the clinic.

Background The dynamics of cerebrospinal fluid (CSF) flow have never been studied in Huntington’s disease (HD), and could have a clinically significant impact on the expected distribution of central nervous system delivered drugs such as huntingtin-lowering therapies. Phase contrast MRI (PCMRI) generates a signal contrast between flowing and stationary nuclei, enabling the characterisation of fluid dynamics in vivo. Objectives The objective of this study was to generate pilot data on CSF flow dynamics in HD using PCMRI, to inform the design of future intrathecal drug trials in HD. Methods We performed a prospective cross-sectional analysis of 10 age- and gender-matched healthy controls and 10 manifest HD gene expansion carriers. All participants underwent extensive clinical evaluation and cardiac-gated PCMRI at the level of the cerebral aqueduct, T1 and T8. CSF velocities and flow measurements were derived using a semi-automated method. The influence of age, gender, CAG repeat-length, serum osmolality, whole-brain volume, and ventricle volume on these measurements were tested using Spearman correlations or Fisher’s exact tests. Group comparisons between healthy controls and manifest carriers were achieved via two-sample Wilcoxon rank-sum tests. All tests were two-sided with a significance level of 0.05, and corrected for multiple comparisons. Results Twenty participants were recruited, and no significant age- and gender-imbalances were found. None of the studied covariables was found to have an effect on the CSF velocities and flow measurements after corrected for multiple comparisons. No apparent differences were found between study groups in regards to CSF velocities and flow measurements. Conclusions Although exploratory, our pilot results add to the view that CSF dynamics are not altered in HD. These results need external validation but offer reassurance that clinically-relevant disease-related alterations in CSF flow, that might justify dose-adjustments of intrathecal drugs, are very unlikely to exist.

Background Despite histological evidence of widespread grey matter atrophy by end-stage Huntington’s disease (HD), the progression of cortical atrophy in HD has yet to be fully characterised. Aims Here, we aim to map cortical atrophy during the transition from premanifest to motor clinical diagnosis of manifest HD, modelling both non-linear and linear atrophy rates in a large cohort of participants, with MRI data spanning 6 years before and 5 years after clinical diagnosis. Methods Structural MR images from 49 HD gene-carrier participants were included in the analysis, collected as part of the longitudinal, multi-site TRACK-HD and TrackOn-HD studies. Participants underwent yearly 3T MRI scans, with some participants returning for 7 visits, and were classified as premanifest at recruitment with diagnosis as manifest HD occurring at a later time-point. Each set of images for each participant underwent longitudinal registration and were segmented using MALP-EM, with cortical and subcortical volumes calculated. A hierarchical dynamical model was applied at the individual level to map individual trajectories. A group level model was then used to estimate group-wise change, accounting for covariates. This approach was used to measure gross atrophy, linear rate of atrophy and non-linear accelerations in atrophy. Results Atrophy was highest in frontal and motor regions, particularly the supplementary motor cortex (11.66% per decade) with greatest linear rates of atrophy in the frontal cortex and greatest acceleration of atrophy in the motor cortex. Conclusions This study provides the most complete characterisation of cortical atrophy in HD presented to-date. The results suggest that the amount of atrophy occurring in the cortex around time of HD diagnosis is much higher than previously reported, and a number of regions show accelerations in atrophy. These findings have important implications for the understanding and treatment of HD and other neurodegenerative conditions.

Background Compensation accounts for the dissociation between pathology and absence of behavioural change during premanifest stages of Huntington’s disease (HD). Despite neuronal loss, individuals with premanifest HD function at a level similar to that of a healthy population. Neural mechanisms underlying compensation, however, are generally poorly understood due to the lack of an operational definition of compensation. Here, we describe the first example of the modelling and empirical testing of compensation in HD. Aims We hypothesise that compensation occurs where increased brain activation is required to maintain normal levels of behaviour until pathology becomes too severe. A compensatory relationship is thus characterised by non-linear longitudinal trajectories of brain activity and behaviour as disease load increases linearly across sequential phases of disease progression. Methods We tested our model in a large cohort of premanifest and early HD gene-carriers from the TrackOn-HD study. Focusing on both cognitive and motor networks, brain activity was measured using task and resting-state fMRI, volumetric loss by structural MRI and behaviour by task performance. Compensation was tested for across three sequential phases of disease progression. Results Maintained global cognition was associated with increased effective connectivity between the left and right dorsolateral prefrontal cortex, an important region for cognitive processing, while maintained motor performance was associated with increased connectivity between bilateral premotor cortex. Conclusions Our empirical findings demonstrate theoretically-defined compensation in HD in networks central to the HD phenotype and can now be used to test both cross-sectional and longitudinal compensation in other neurodegenerative disease with similar patterns to HD.

Background Huntington’s disease (HD) is a progressive neurodegenerative disorder where there is a pressing need for sensitive biomarkers. Aims We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) in parallel. Methods CSF mHTT, CSF NfL and plasma NfL were measured using immunoassays in 80 participants (20 healthy controls, 20 premanifest HD and 40 manifest HD) underwent clinical assessments, and standardized CSF and blood collections. Analysis included multiple linear regression models, Pearson’s correlations, receiver operating characteristics curves and samples sizes calculations. An event-based model was used to assess the temporal sequence of HD-related biomarker alterations. Results CSF mHTT, CSF NfL and plasma NfL were significantly higher as disease progressed and associated with all clinical measures. Both CSF and plasma NfL were associated with brain volume measures, but CSF mHTT was not. CSF mHTT, CSF NfL and plasma NfL were closely correlated, and highly stable within individuals. CSF mHTT had perfect accuracy for distinguishing between controls and HD mutation carriers, and both CSF and plasma NfL had excellent accuracy for distinguishing between premanifest and manifest HD. Sample size calculations suggest low participant numbers needed to incorporate these measures into clinical trials. The biofluid biomarkers emerged as the earliest detectable alterations in HD, followed by brain volume, motor and cognitive measures. Conclusion In this cross-sectional study we provide evidence to support mHTT and NfL as having favourable properties as biofluid biomarkers for HD. Our data suggests that these key biofluid biomarkers are some of the earliest detectable changes in HD.

Background Neurofilament light (NfL) protein in blood plasma has been proposed as a prognostic biomarker of neurodegeneration in a number of conditions, including Huntington’s disease (HD). Previous work has demonstrated elevated levels of NfL in HD gene expansion carriers when compared with controls, with NfL levels reflecting baseline motor and cognitive deficits as well as reduced global and regional brain volume. However, global measures provide limited insight into the regional distribution of NfL-associated pathology due to the HD gene mutation. Aims This study aims to establish the regional distribution of NfL-associated pathology in HD using voxel-based morphometry (VBM). Methods We examine associations between NfL measured in plasma and regionally-specific atrophy in cross-sectional (n=198) and longitudinal data (n=177) in HD gene expansion carriers from the international multisite TRACK-HD study. Using VBM we measured associations between baseline NfL levels and both baseline grey matter and white matter volume; and longitudinal change in grey matter and white matter over the subsequent three years in HD gene expansion carriers. Results After controlling for demographics, associations between increased NfL levels and reduced brain volume were seen in cortical and subcortical grey matter and within the white matter. After also controlling for known predictors of disease progression (age and CAG repeat length), associations were limited to the caudate and putamen. Longitudinally, NfL predicted subsequent occipital grey matter atrophy and widespread white matter reduction, both before and after correction for other predictors of disease progression. Conclusions These findings highlight the value of NfL as a dynamic marker of brain atrophy and, more generally, provide further evidence of the strong association between plasma NfL level, a candidate blood biomarker, and pathological neuronal change.

Background The TRACK, PREDICT and IMAGE-HD studies provide rich and varied datasets with which to identify robust imaging and clinical biomarkers of Huntington’s disease (HD) progression. A comparative analysis of biomarkers between studies has potential use in observational study design. Estimating the sequence in which these biomarkers become abnormal can provide important insights into HD pathology and a mechanism for disease staging. Aims We have, for the first time, analysed and statistically compared structural imaging and phenotypic clinical data from these three observational studies. We hence aim to identify a common set of robust biomarkers, and explain observational differences between studies. We also propose how to use these biomarkers to inform a model of HD progression. Methods We analysed structural imaging, clinical and behavioural data from a total of 357 TRACK, 1091 PREDICT, and 96 IMAGE-HD participants at baseline. The imaging data were segmented and parcellated using a common framework. Groupwise comparisons were made between controls, pre-manifest and manifest groups, and effect sizes compared between studies. An event-based model1 was trained to infer the most likely sequence of biomarker abnormality, and to stage participants. Results We identified a core set of significant imaging, clinical and behavioural biomarkers common to all studies, plus biomarkers that were significant within, but not between studies. Consequently, the disease progression model reveals a distinct, cross-validated pattern of imaging and phenotypic abnormality. Conclusions We successfully identified a set of robust biomarkers common to all studies, explored observational differences, and demonstrated that these biomarkers can be used to model HD progression. Reference . Wijeratne, et al. Ann Clin Trans Neurol2018. doi:10.1002/acn3.558

Biomarkers of Huntington’s disease (HD) in cerebrospinal fluid (CSF) could be of value in elucidating the biology of this genetic neurodegenerative disease, as well as in the development of novel therapeutics. Deranged synaptic and immune function have been reported in HD, and concentrations of the synaptic protein neurogranin and the microglial protein TREM2 are increased in other neurodegenerative diseases. We therefore used ELISAs to quantify neurogranin and TREM2 in CSF samples from HD mutation carriers and controls. CSF neurogranin concentration was not significantly altered in HD compared to controls, nor was it significantly associated with disease burden score, total functional capacity or motor score. An apparent increase in CSF TREM2 in manifest HD was determined to be due to increasing TREM2 with age. After age adjustment, there was no significant alteration of TREM2 in either HD group, nor any association with motor, functional or cognitive score, or brain volume quantified by MRI. Both analyses were well-powered, and sample size calculations indicated that several thousand samples per group would be needed to prove that disease-associated alterations do in fact exist. We conclude that neither neurogranin nor TREM2 is a useful biofluid biomarker for disease processes in Huntington’s disease.

ObjectiveNeurofilament light (NfL) protein in blood plasma has been proposed as a prognostic biomarker of neurodegeneration in a number of conditions, including Huntington disease (HD). This study investigates the regional distribution of NfL-associated neural pathology in HD gene expansion carriers.MethodsWe examined associations between NfL measured in plasma and regionally specific atrophy in cross-sectional (n = 198) and longitudinal (n = 177) data in HD gene expansion carriers from the international multisite TRACK-HD study. Using voxel-based morphometry, we measured associations between baseline NfL levels and both baseline gray matter and white matter volume; and longitudinal change in gray matter and white matter over the subsequent 3 years in HD gene expansion carriers.ResultsAfter controlling for demographics, associations between increased NfL levels and reduced brain volume were seen in cortical and subcortical gray matter and within the white matter. After also controlling for known predictors of disease progression (age and CAG repeat length), associations were limited to the caudate and putamen. Longitudinally, NfL predicted subsequent occipital gray matter atrophy and widespread white matter reduction, both before and after correction for other predictors of disease progression.ConclusionsThese findings highlight the value of NfL as a dynamic marker of brain atrophy and, more generally, provide further evidence of the strong association between plasma NfL level, a candidate blood biomarker, and pathologic neuronal change.

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543

An unresolved goal in face perception is to identify brain areas involved in face processing and simultaneously understand the timing of their involvement. Currently, high spatial resolution imaging techniques identify the fusiform gyrus as subserving processing of invariant face features relating to identity. High temporal resolution imaging techniques localize an early latency evoked component—the N/M170—as having a major generator in the fusiform region; however, this evoked component is not believed to be associated with the processing of identity. To resolve this, we used novel magnetoencephalographic beamformer analyses to localize cortical regions in humans spatially with trial-by-trial activity that differentiated faces and objects and to interrogate their functional sensitivity by analyzing the effects of stimulus repetition. This demonstrated a temporal sequence of processing that provides category-level and then item-level invariance. The right fusiform gyrus showed adaptation to faces (not objects) at ∼150 ms after stimulus onset regardless of face identity; however, at the later latency of ∼200–300 ms, this area showed greater adaptation to repeated identity faces than to novel identities. This is consistent with an involvement of the fusiform region in both early and midlatency face-processing operations, with only the latter showing sensitivity to invariant face features relating to identity.SIGNIFICANCE STATEMENTNeuroimaging techniques with high spatial-resolution have identified brain structures that are reliably activated when viewing faces and techniques with high temporal resolution have identified the time-varying temporal signature of the brain's response to faces. However, until now, colocalizing face-specific mechanisms in both time and space has proven notoriously difficult. Here, we used novel magnetoencephalographic analysis techniques to spatially localize cortical regions with trial-by-trial temporal activity that differentiates between faces and objects and to interrogate their functional sensitivity by analyzing effects of stimulus repetition on the time-locked signal. These analyses confirm a role for the right fusiform region in early to midlatency responses consistent with face identity processing and convincingly deliver upon magnetoencephalography's promise to resolve brain signals in time and space simultaneously.

Background In pre-manifest Huntington’s disease there is an increase in symptom severity that is thought to be accompanied by an acceleration of cortical grey matter (CGM) volume loss in the period immediately prior to diagnosis that has not been well characterised. Before examining CGM change, the tools used to calculate CGM volume need evaluation as it is unclear which software most accurately and sensitively measures between-group differences and within-group change in CGM in HD. Aims Conduct a validation of software in order to determine the most sensitive and accurate tools for quantifying disease-related between-group differences and within-group change in CGM in HD. Methods 20 controls, 40 premanifest HD (20 preHD-A, >10.8 yrs from predicted onset; 20 preHD-B, Results Visual QC highlighted that some techniques over- and under-estimate GM, especially in occipital and temporal regions. Most methods showed high reliability. Results for between-group and within-group analysis varied depending on the software used. Conclusions Whilst reliability was high, between and within-group results and detailed QC suggest that accuracy of the software is variable. This study provides recommendations for the measurement of total, cortical and lobular GM in HD.

Background Previous Huntington’s disease studies have revealed occipital lobe atrophy and cortical thinning, although it is unclear when this develops and whether it is uniform across the lobe. Visual deficits are not typical of HD symptomatology, however it is possible that more subtle visual functions are impaired and, in turn, influence performance on cognitive tasks with a visual component. Aims To identify the regional distribution of disease-related occipital cortical thinning and investigate associations with cognitive and motor impairment. Methods T1-weighted 3T MR scans from controls (n = 107), pre-HD (n = 116) and early HD subjects (n = 90) from the TRACK-HD study were processed using the FreeSurfer software; cuneus, pericalcarine, lingual and lateral occipital regions were outputted. Generalised least squares regression models were fitted to assess between-group differences. Relationships between visual cognitive and motor task performance and cortical occipital thinning in gene-positive participants were assessed using general linear regression models. Results The occipital cortex was significantly thinner in pre-HD and early HD participants. These between-group differences were heterogeneous across regions, with the lateral occipital region showing the most pronounced effects. Significant associations were found between reduced occipital cortical thickness and poorer performance on a number of cognitive tasks involving a visual component. No association was found with finger tapping. Conclusions Regionally-specific thinning of the occipital cortex was evident more than a decade before estimated HD symptom onset. Associations were found with cognitive tasks, but not finger tapping, suggesting that occipital thickness plays an important role in cognitive tasks with a visual component. This has implications for the interpretation of impaired cognitive performances in HD, which is typically attributed to frontal and executive circuits, rather than to visual regions.