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.

Early white matter pathology in the fornix of the limbic system in Huntington disease.

Huntington disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin (HTT) gene. The typical motor symptoms have been associated with basal ganglia pathology. However, psychiatric and cognitive symptoms often precede the motor component and may be due to changes in the limbic system. Recent work has indicated pathology in the hypothalamus in HD but other parts of the limbic system have not been extensively studied. Emerging evidence suggests that changes in HD also include white matter pathology. Here we investigated if the main white matter tract of the limbic system, the fornix, is affected in HD. We demonstrate that the fornix is 34% smaller already in prodromal HD and 41% smaller in manifest HD compared to controls using volumetric analyses of MRI of the IMAGE-HD study. In post-mortem fornix tissue from HD cases, we confirm the smaller fornix volume in HD which is accompanied by signs of myelin breakdown and reduced levels of the transcription factor myelin regulating factor but detect no loss of oligodendrocytes. Further analyses using RNA-sequencing demonstrate downregulation of oligodendrocyte identity markers in the fornix of HD cases. Analysis of differentially expressed genes based on transcription-factor/target-gene interactions also revealed enrichment for binding sites of SUZ12 and EZH2, components of the Polycomb Repressive Complex 2, as well as RE1 Regulation Transcription Factor. Taken together, our data show that there is early white matter pathology of the fornix in the limbic system in HD likely due to a combination of reduction in oligodendrocyte genes and myelin break down.

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

BACKGROUND: Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants. OBJECTIVE: The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups. METHODS: Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups. RESULTS: Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression. CONCLUSIONS: Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.

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

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

Exploring the brain-body composition relationship in Huntington's disease.

OBJECTIVE: Changes in body composition are a common feature of Huntington's disease (HD) and are associated with disease progression. However, whether these changes in body composition are associated with degeneration of the striatum is unknown. This study aimed to explore the associations between body composition metrics and striatal brain volume in individuals with premanifest HD and healthy controls. METHODS: Twenty-one individuals with premanifest HD and 22 healthy controls participated in this cross-sectional study. Body composition metrics were measured via dual-energy X-ray absorptiometry. Structural magnetic resonance imaging of subcortical structures of the brain was performed to evaluate striatal volume. RESULTS: There were no significant differences in body composition metrics between the premanifest HD and healthy controls group. Striatal volume was significantly reduced in individuals with premanifest HD compared to healthy controls. A significant association between bone mineral density (BMD) and right putamen volume was also observed in individuals with premanifest HD. CONCLUSION: These findings show striatal degeneration is evident during the premanifest stages of HD and associated with BMD. Additional longitudinal studies are nevertheless needed to confirm these findings.

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

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

Temporal evolution of neural activity and connectivity during microsleeps when rested and following sleep restriction.

Even when it is critical to stay awake, such as when driving, sleep deprivation weakens one's ability to do so by substantially increasing the propensity for microsleeps. Microsleeps are complete lapses of consciousness but, paradoxically, are associated with transient increases in cortical activity. But do microsleeps provide a benefit in terms of attenuating the need for sleep? And is the neural response to microsleeps altered by the degree of homeostatic drive to sleep? In this study, we continuously monitored eye-video, visuomotor responsiveness, and brain activity via fMRI in 20 healthy subjects during a 20-min visuomotor tracking task following a normally-rested night and a sleep-restricted (4-h) night. As expected, sleep restriction led to an increased number of microsleeps and an increased variability in tracking error. Microsleeps exhibited transient increases in regional activity in the fronto-parietal and parahippocampal area. Network analyses revealed divergent transient changes in the right fronto-parietal, dorsal-attention, default-mode, and thalamo-cortical functional networks. In all subjects, tracking error immediately following microsleeps was improved compared to before the microsleeps. Importantly, post-microsleep recovery in tracking response speed was associated with hyperactivation in the thalamo-cortical network. The temporal evolution of functional connectivity within the frontal and posterior nodes of the default-mode network and between the right fronto-parietal and default-mode networks was associated with temporal changes in visuomotor responsiveness. These findings demonstrate distinct brain-network-level changes in brain activity during microsleeps and suggest that neural activity in the thalamo-cortical network may facilitate the transient recovery from microsleeps. The temporal pattern of evolution in brain activity and performance is indicative of dynamic changes in vigilance during the struggle to stay awake following sleep loss.

Time-varying effective connectivity of the cortical neuroelectric activity associated with behavioural microsleeps.

An episode of complete failure to respond during an attentive task accompanied by behavioural signs of sleep is called a behavioural microsleep. We proposed a combination of high-resolution EEG and an advanced method for time-varying effective connectivity estimation for reconstructing the temporal evolution of the causal relations between cortical regions when microsleeps occur during a continuous visuomotor task. We found connectivity patterns involving left-right frontal, left-right parietal, and left-frontal/right-parietal connections commencing in the interval [-500; -250] ms prior to the onset of microsleeps and disappearing at the end of the microsleeps. Our results from global graph indices derived from effective connectivity analysis have revealed EEG-based biomarkers of all stages of microsleeps (preceding, onset, pre-recovery, recovery). In particular, this raises the possibility of being able to predict microsleeps in real-world tasks and initiate a 'wake-up' intervention to avert the microsleeps and, hence, prevent injurious and even multi-fatality accidents.

Fronto-cerebellar dysfunction and dysconnectivity underlying cognition in friedreich ataxia: The IMAGE-FRDA study.

Friedreich ataxia (FRDA) is a progressive neurodegenerative disorder defined by pathology within the cerebellum and spinal tracts. Although FRDA is most readily linked to motor and sensory dysfunctions, reported impairments in working memory and executive functions indicate that abnormalities may also extend to associations regions of the cerebral cortex and/or cerebello-cerebral interactions. To test this hypothesis, 29 individuals with genetically confirmed FRDA and 34 healthy controls performed a verbal n-back working memory task while undergoing functional magnetic resonance imaging. No significant group differences were evident in task performance. However, individuals with FRDA had deficits in brain activations both in the lateral cerebellar hemispheres, principally encompassing lobule VI, and the prefrontal cortex, including regions of the anterior insular and rostrolateral prefrontal cortices. Functional connectivity between these brain regions was also impaired, supporting a putative link between primary cerebellar dysfunction and subsequent cerebral abnormalities. Disease severity and genetic markers of disease liability were correlated specifically with cerebellar dysfunction, while correlations between behavioural performance and both cerebral activations and cerebello-cerebral connectivity were observed in controls, but not in the FRDA cohort. Taken together, these findings support a diaschisis model of brain dysfunction, whereby primary disease effects in the cerebellum result in functional changes in downstream fronto-cerebellar networks. These fronto-cerebellar disturbances provide a putative biological basis for the nonmotor symptoms observed in FRDA, and reflect the consequence of localized cerebellar pathology to distributed brain function underlying higher-order cognition.

Longitudinal change in white matter microstructure in Huntington's disease: The IMAGE-HD study.

OBJECTIVE: To quantify 18-month changes in white matter microstructure in premanifest (pre-HD) and symptomatic Huntington's disease (symp-HD). To investigate baseline clinical, cognitive and motor symptoms that are predictive of white matter microstructural change over 18months. METHOD: Diffusion tensor imaging (DTI) data were analyzed for 28 pre-HD, 25 symp-HD, and 27 controls scanned at baseline and after 18months. Unbiased tract-based spatial statistics (TBSS) methods were used to identify longitudinal changes in fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) of white matter. Stepwise linear regression models were used to identify baseline clinical, cognitive, and motor measures that are predictive of longitudinal diffusion changes. RESULTS: Symp-HD compared to controls showed 18-month reductions in FA in the corpus callosum and cingulum white matter. Symp-HD compared to pre-HD showed increased RD in the corpus callosum and striatal projection pathways. FA in the body, genu, and splenium of the corpus callosum was significantly associated with a baseline clinical motor measure (Unified Huntington's Disease Rating Scale: total motor scores: UHDRS-TMS) across both HD groups. This measure was also the only independent predictor of longitudinal decline in FA in all parts of the corpus callosum across both HD groups. CONCLUSIONS: We provide direct evidence of longitudinal decline in white matter microstructure in symp-HD. Although pre-HD did not show longitudinal change, clinical symptoms and motor function predicted white matter microstructural changes for all gene positive subjects. These findings suggest that loss of axonal integrity is an early hallmark of neurodegenerative changes which are clinically relevant.

White matter connectivity reflects clinical and cognitive status in Huntington's disease.

OBJECTIVE: To investigate structural connectivity and the relationship between axonal microstructure and clinical, cognitive, and motor functions in premanifest (pre-HD) and symptomatic (symp-HD) Huntington's disease. METHOD: Diffusion tensor imaging (DTI) data were acquired from 35 pre-HD, 36 symp-HD, and 35 controls. Structural connectivity was mapped between 40 brain regions of interest using tractography. Between-group differences in structural connectivity were identified using network based statistics. Radial diffusivity (RD) and fractional anisotropy (FA) were compared in the white matter tracts from aberrant networks. RD values in aberrant tracts were correlated with clinical severity, and cognitive and motor performance. RESULTS: A network connecting putamen with prefrontal and motor cortex demonstrated significantly reduced tractography streamlines in pre-HD. Symp-HD individuals showed reduced streamlines in a network connecting prefrontal, motor, and parietal cortices with both caudate and putamen. The symp-HD group, compared to controls and pre-HD, showed both increased RD and decreased FA in the fronto-parietal and caudate-paracentral tracts and increased RD in the putamen-prefrontal and putamen-motor tracts. The pre-HDclose, compared to controls, showed increased RD in the putamen-prefrontal and fronto-parietal tracts. In the pre-HD group, significant negative correlations were observed between SDMT and Stroop performance and RD in the bilateral putamen-prefrontal tract. In the symp-HD group, RD in the fronto-parietal tract was significantly positively correlated with UHDRS motor scores and significantly negatively correlated with performance on SDMT and Stroop tasks. CONCLUSIONS: We have provided evidence of aberrant connectivity and microstructural integrity in white matter networks in HD. Microstructural changes in the cortico-striatal fibers were associated with cognitive and motor performance in pre-HD, suggesting that changes in axonal integrity provide an early marker for clinically relevant impairment in HD.

Losing the struggle to stay awake: divergent thalamic and cortical activity during microsleeps.

Maintaining alertness is critical for safe and successful performance of most human activities. Consequently, microsleeps during continuous visuomotor tasks, such as driving, can be very serious, not only disrupting performance but sometimes leading to injury or death due to accidents. We have investigated the neural activity underlying behavioral microsleeps--brief (0.5-15 s) episodes of complete failure to respond accompanied by slow eye-closures--and EEG theta activity during drowsiness in a continuous task. Twenty healthy normally-rested participants performed a 50-min continuous tracking task while fMRI, EEG, eye-video, and responses were simultaneously recorded. Visual rating of performance and eye-video revealed that 70% of the participants had frequent microsleeps. fMRI analysis revealed a transient decrease in thalamic, posterior cingulate, and occipital cortex activity and an increase in frontal, posterior parietal, and parahippocampal activity during microsleeps. The transient activity was modulated by the duration of the microsleep. In subjects with frequent microsleeps, power in the post-central EEG theta was positively correlated with the BOLD signal in the thalamus, basal forebrain, and visual, posterior parietal, and prefrontal cortices. These results provide evidence for distinct neural changes associated with microsleeps and with EEG theta activity during drowsiness in a continuous task. They also suggest that the occurrence of microsleeps during an active task is not a global deactivation process but involves localized activation of fronto-parietal cortex, which, despite a transient loss of arousal, may constitute a mechanism by which these regions try to restore responsiveness.

Abnormal synchrony of resting state networks in premanifest and symptomatic Huntington disease: the IMAGE-HD study.

BACKGROUND: Functional neural impairments have been documented in people with symptomatic Huntington disease (symp-HD) and in premanifest gene carriers (pre-HD). This study aimed to characterize synchrony in resting state cerebral networks in both pre-HD and symp-HD populations and to determine its association with disease burden and neurocognitive functions. METHODS: We acquired functional magnetic resonance imaging (fMRI) data from pre-HD, symp-HD and healthy control participants. The fMRI data were analyzed using multisubject independent component analysis and dual regression. We compared networks of interest among the groups using a nonparametric permutation method and correcting for multiple comparisons. RESULTS: Our study included 25 people in the pre-HD, 23 in the symp-HD and 18 in the healthy control groups. Compared with the control group, the pre-HD group showed decreased synchrony in the sensorimotor and dorsal attention networks; decreased level of synchrony in the sensorimotor network was associated with poorer motor performance. Compared with the control group, the symp-HD group showed widespread reduction in synchrony in the dorsal attention network, which was associated with poorer cognitive performance. The posterior putamen and superior parietal cortex were functionally disconnected from the frontal executive network in the symp-HD compared with control and pre-HD groups. Furthermore, the left frontoparietal network showed areas of increased synchrony in the symp-HD compared with the pre-HD group. LIMITATIONS: We could not directly correct for influence of autonomic changes (e.g., heart rate) and respiration on resting state synchronization. CONCLUSION: Our findings suggest that aberrant synchrony in the sensorimotor and dorsal attention networks may serve as an early signature of neural change in pre-HD individuals. The altered synchrony in dorsal attention, frontoparietal and corticostriatal networks may contribute to the development of clinical symptoms in people with Huntington disease.

Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability.

Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability. We also characterised the strength of connectivity obtained using degree synchrony of visibility graphs. We found that strong correlation (r > 0.5) between visibility graph properties, such as the number of communities and average degrees, and motion in the fMRI data. The test-retest reliability (Intraclass correlation coefficient (ICC)) of graph theoretical features was high for the average degrees (0.74, 95% CI = [0.73, 0.75]), and moderate for clustering coefficient (0.43, 95% CI = [0.41, 0.44]) and average path length (0.41, 95% CI = [0.38, 0.44]). Functional connectivity between brain regions was measured by correlating the visibility graph degrees. However, the strength of correlation was found to be moderate to low (r < 0.35). These findings suggest that even small movement in fMRI data can strongly influence robustness and reliability of visibility graph features, thus, requiring robust motion correction strategies prior to data analysis. Further studies are necessary for better understanding of the potential application of visibility graph features in fMRI.

Distinct neural correlates of time-on-task and transient errors during a visuomotor tracking task after sleep restriction.

Sleep loss leads to both time-on-task slowing of responsiveness and increased frequency of transient response errors. The consequences of such errors during real-world visuomotor tasks, such as driving, are serious and life threatening. To investigate the neuronal underpinning of time-on-task and transient errors during a visuomotor tracking task following sleep restriction, we performed fMRI on 20 healthy individuals when well-rested and when sleep-restricted while they performed a 2-D pursuit-tracking task. Sleep restriction to 4-h time-in-bed was associated with significant time-on-task decline in tracking performance and an increased number of transient tracking errors. Sleep restriction was associated with time-on-task decreases in BOLD activity in task-related areas, including the lateral occipital cortex, intraparietal cortex, and primary motor cortex. In contrast, thalamic, anterior cingulate, and medial frontal cortex areas showed overall increases irrespective of time-on-task after sleep-restriction. Furthermore, transient errors after sleep-restriction were associated with distinct transient BOLD activations in areas not involved in tracking task per se, in the right superior parietal cortex, bilateral temporal cortex, and thalamus. These results highlight the distinct cerebral underpinnings of sustained and transient modulations in alertness during increased homeostatic drive to sleep. Ability to detect neuronal changes associated with both sustained and transient changes in performance in a single task allowed us to disentangle neuronal mechanisms underlying two important aspects of sustained task performance following sleep loss.

Functional and connectivity changes during working memory in Huntington's disease: 18 month longitudinal data from the IMAGE-HD study.

BACKGROUND: This study aimed to characterize, for the first time, 18 month longitudinal changes in both functional activation and functional connectivity during working memory in premanifest Huntington's disease (pre-HD) and symptomatic HD (symp-HD). METHODS: Functional magnetic resonance imaging (fMRI) was used to investigate longitudinal changes in neuronal activity during working memory performance via an N-BACK task (0-BACK and 1-BACK) in 27 pre-HD, 17 symp-HD, and 23 control participants. Whole-brain analysis of activation and region-of-interest analysis of functional connectivity was applied to longitudinal fMRI data collected at baseline and 18 months follow-up. RESULTS: Compared with controls, the pre-HD group showed significantly increased activation longitudinally during 1-BACK versus 0-BACK in the lateral and medial prefrontal, anterior cingulate, primary motor, and temporal areas cortically, and caudate and putamen subcortically. Pre-HD far from onset, compared with controls, showed further longitudinal increases in the right and left dorsolateral prefrontal cortex (DLPFC). Longitudinal increased activation in anterior cingulate and medial primary motor areas were associated with disease burden in the pre-HD group. Moreover, in pre-HD increased activation over time in primary motor and putamen regions were associated with average response time during 1-BACK performance. During 1-BACK, functional connectivity between the right DLPFC and posterior parietal, anterior cingulate, and caudate was significantly reduced over 18months only in the pre-HD group. CONCLUSIONS: Longitudinal reductions in connectivity over 18 months may represent an early signature of cortico-cortical and cortico-striatal functional disconnectivity in pre-HD, whereas the concomitant increased cortical and subcortical activation may reflect a compensatory response to the demands for cognitive resources required during task performance. Our findings demonstrate that functional imaging modalities have the potential to serve as sensitive methods for the assessment of cortical and subcortical responses to future treatment measures.

Increased cerebral activity during microsleeps reflects an unconscious drive to re-establish consciousness.

BACKGROUND: Microsleeps are brief instances of sleep, causing complete lapses in responsiveness and partial or total extended closure of both eyes. Microsleeps can have devastating consequences, particularly in the transportation sector. STUDY OBJECTIVES: Questions remain regarding the neural signature and underlying mechanisms of microsleeps. This study aimed to gain a better understanding of the physiological substrates of microsleeps, which might lead to a better understanding of the phenomenon. METHODS: Data from an earlier study, involving 20 healthy non-sleep-deprived subjects, were analysed. Each session lasted 50 min and required subjects to perform a 2-D continuous visuomotor tracking task. Simultaneous data collection included tracking performance, eye-video, EEG, and fMRI. A human expert visually inspected each participant's tracking performance and eye-video recordings to identify microsleeps. Our interest was in microsleeps of ≥4-s duration, leaving us with a total of 226 events from 10 subjects. The microsleep events were divided into four 2-s segments (pre, start, end, and post) (with a gap in the middle, between start and end segments, for microsleeps >4 s), then each segment was analysed relative to its prior segment by examining changes in source-reconstructed EEG power in the delta, theta, alpha, beta, and gamma bands. RESULTS: EEG power increased in the theta and alpha bands between the pre and start of microsleeps. There was also increased power in the delta, beta, and gamma bands between the start and end of microsleeps. Conversely, there was a reduction in power between the end and post of microsleeps in the delta and alpha bands. These findings support previous findings in the delta, theta, and alpha bands. However, increased power in the beta and gamma bands has not been previously reported. CONCLUSIONS: We contend that increased high-frequency activity during microsleeps reflects unconscious 'cognitive' activity aimed at re-establishing consciousness following falling asleep during an active task.

Hippocampal morphology in Huntington's disease, implications for plasticity and pathogenesis: The IMAGE-HD study.

While striatal changes in Huntington's Disease (HD) are well established, few studies have investigated changes in the hippocampus, a key neuronal hub. Using MRI scans obtained from the IMAGE-HD study, hippocampi were manually traced and then analysed with the Spherical Harmonic Point Distribution Method (SPHARM-PDM) in 36 individuals with presymptomatic-HD, 37 with early symptomatic-HD, and 36 healthy matched controls. There were no significant differences in overall hippocampal volume between groups. Interestingly we found decreased bilateral hippocampal volume in people with symptomatic-HD who took selective serotonin reuptake inhibitors compared to those who did not, despite no significant differences in anxiety, depressive symptoms, or motor incapacity between the two groups. In symptomatic-HD, there was also significant shape deflation in the right hippocampal head, showing the utility of using manual tracing and SPHARM-PDM to characterise subtle shape changes which may be missed by other methods. This study confirms previous findings of the lack of hippocampal volumetric differentiation in presymptomatic-HD and symptomatic-HD compared to controls. We also find novel shape and volume findings in those with symptomatic-HD, especially in relation to decreased hippocampal volume in those treated with SSRIs.

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

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

Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors.

The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34-97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.