Comprehensive shape analysis of the cortex in Huntington's disease.

The striatum has traditionally been the focus of Huntington's disease research due to the primary insult to this region and its central role in motor symptoms. Beyond the striatum, evidence of cortical alterations caused by Huntington's disease has surfaced. However, findings are not coherent between studies which have used cortical thickness for Huntington's disease since it is the well-established cortical metric of interest in other diseases. In this study, we propose a more comprehensive approach to cortical morphology in Huntington's disease using cortical thickness, sulcal depth, and local gyrification index. Our results show consistency with prior findings in cortical thickness, including its limitations. Our comparison between cortical thickness and local gyrification index underscores the complementary nature of these two measures-cortical thickness detects changes in the sensorimotor and posterior areas while local gyrification index identifies insular differences. Since local gyrification index and cortical thickness measures detect changes in different regions, the two used in tandem could provide a clinically relevant measure of disease progression. Our findings suggest that differences in insular regions may correspond to earlier neurodegeneration and may provide a complementary cortical measure for detection of subtle early cortical changes due to Huntington's disease.

Role of Hounsfield Unit in Predicting Outcomes of Shock Wave Lithotripsy for Renal Calculi: Outcomes of a Systematic Review.

PURPOSE OF REVIEW: Extracorporeal shock wave lithotripsy success rates depend on several stone and patient-related factors, one of which is stone density which is calculated on computed tomography scan in Hounsfield Units. Studies have shown inverse correlation between SWL success and HU; however, there remains considerable variation between studies. We performed a systematic review regarding the use of HU in SWL for renal calculi to consolidate the current evidence and address current knowledge gaps. RECENT FINDINGS: Database including MEDLINE, EMBASE, and Scopus were searched from inception through August 2022. Studies in English language analysing stone density/attenuation in adult patients undergoing SWL for renal calculi were included for assessment of Shockwave lithotripsy outcomes, use of stone attenuation to predict success, use of mean and peak stone density and Hounsfield unit density, determination of optimum cut-off values, nomograms/scoring systems, and assessment of stone heterogeneity. 28 studies with a total of 4,206 patients were included in this systematic review with sample size ranging from 30 to 385 patients. Male to female ratio was 1.8, with an average age of 46.3 years. Mean overall ESWL success was 66.5%. Stone size ranged from 4 to 30 mm in diameter. Mean stone density was used by two-third of the studies to predict the appropriate cut-off for SWL success, ranging from 750 to 1000 HU. Additional factors such as peak HU and stone heterogeneity index were also evaluated with variable results. Stone heterogeneity index was considered a better indicator for success in larger stones (cut-off value of 213) and predicting SWL stone clearance in one session. Prediction scores had been attempted, with researchers looking into combining stone density with other factors such as skin to stone distance, stone volume, and differing heterogeneity indices with variable results. Numerous studies demonstrate a link between shockwave lithotripsy outcomes and stone density. Hounsfield unit < 750 has been found to be associated with shockwave lithotripsy success, with likelihood of failure strongly associated with values over 1000. Prospective standardisation of Hounsfield unit measurement and predictive algorithm for shockwave lithotripsy outcome should be considered to strengthen future evidence and help clinicians in the decision making. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) database: CRD42020224647.

Sleep Duration, Sleep Apnea, and Gray Matter Volume.

This study aimed to evaluate the effect of sleep duration on brain structures in the presence versus absence of sleep apnea in middle-aged and older individuals. The study investigated a population-based sample of 2,560 individuals, aged 49-80 years. The presence of sleep apnea and self-reported sleep duration were examined in relation to gray matter volume (GMV) in total and lobar brain regions. We identified ranges of sleep duration associated with maximal GMV using quadratic regression and bootstrap sampling. A significant quadratic association between sleep duration and GMV was observed in total and lobar brain regions of men with sleep apnea. In the fully adjusted model, optimal sleep durations associated with peak GMV between brain regions ranged from 6.7 to 7.0 hours. Shorter and longer sleep durations were associated with lower GMV in total and 4 sub-regions of the brain in men with sleep apnea.

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.

Regional subcortical shape analysis in premanifest Huntington's disease.

Huntington's disease (HD) involves preferential and progressive degeneration of striatum and other subcortical regions as well as regional cortical atrophy. It is caused by a CAG repeat expansion in the Huntingtin gene, and the longer the expansion the earlier the age of onset. Atrophy begins prior to manifest clinical signs and symptoms, and brain atrophy in premanifest expansion carriers can be studied. We employed a diffeomorphometric pipeline to contrast subcortical structures' morphological properties in a control group with three disease groups representing different phases of premanifest HD (far, intermediate, and near to onset) as defined by the length of the CAG expansion and the participant's age (CAG-Age-Product). A total of 1,428 magnetic resonance image scans from 694 participants from the PREDICT-HD cohort were used. We found significant region-specific atrophies in all subcortical structures studied, with the estimated abnormality onset time varying from structure to structure. Heterogeneous shape abnormalities of caudate nuclei were present in premanifest HD participants estimated furthest from onset and putaminal shape abnormalities were present in participants intermediate to onset. Thalamic, hippocampal, and amygdalar shape abnormalities were present in participants nearest to onset. We assessed whether the estimated progression of subcortical pathology in premanifest HD tracked specific pathways. This is plausible for changes in basal ganglia circuits but probably not for changes in hippocampus and amygdala. The regional shape analyses conducted in this study provide useful insights into the effects of HD pathology in subcortical structures.

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

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

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

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

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

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

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

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

Genetic load determines atrophy in hand cortico-striatal pathways in presymptomatic Huntington's disease.

Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.

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

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

Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that progressively affects motor, cognitive, and emotional functions. Structural MRI studies have demonstrated brain atrophy beginning many years prior to clinical onset ("premanifest" period), but the order and pattern of brain structural changes have not been fully characterized. In this study, we investigated brain regional volumes and diffusion tensor imaging (DTI) measurements in premanifest HD, and we aim to determine (1) the extent of MRI changes in a large number of structures across the brain by atlas-based analysis, and (2) the initiation points of structural MRI changes in these brain regions. We adopted a novel multivariate linear regression model to detect the inflection points at which the MRI changes begin (namely, "change-points"), with respect to the CAG-age product (CAP, an indicator of extent of exposure to the effects of CAG repeat expansion). We used approximately 300 T1-weighted and DTI data from premanifest HD and control subjects in the PREDICT-HD study, with atlas-based whole brain segmentation and change-point analysis. The results indicated a distinct topology of structural MRI changes: the change-points of the volumetric measurements suggested a central-to-peripheral pattern of atrophy from the striatum to the deep white matter; and the change points of DTI measurements indicated the earliest changes in mean diffusivity in the deep white matter and posterior white matter. While interpretation needs to be cautious given the cross-sectional nature of the data, these findings suggest a spatial and temporal pattern of spread of structural changes within the HD brain. Hum Brain Mapp 38:5035-5050, 2017. © 2017 Wiley Periodicals, Inc.

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

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

Cognitive Control, Learning, and Clinical Motor Ratings Are Most Highly Associated with Basal Ganglia Brain Volumes in the Premanifest Huntington's Disease Phenotype.

OBJECTIVES: Huntington's disease (HD) is a debilitating genetic disorder characterized by motor, cognitive and psychiatric abnormalities associated with neuropathological decline. HD pathology is the result of an extended chain of CAG (cytosine, adenine, guanine) trinucleotide repetitions in the HTT gene. Clinical diagnosis of HD requires the presence of an otherwise unexplained extrapyramidal movement disorder in a participant at risk for HD. Over the past 15 years, evidence has shown that cognitive, psychiatric, and subtle motor dysfunction is evident decades before traditional motor diagnosis. This study examines the relationships among subcortical brain volumes and measures of emerging disease phenotype in prodromal HD, before clinical diagnosis. METHODS: The dataset includes 34 cognitive, motor, psychiatric, and functional variables and five subcortical brain volumes from 984 prodromal HD individuals enrolled in the PREDICT HD study. Using cluster analyses, seven distinct clusters encompassing cognitive, motor, psychiatric, and functional domains were identified. Individual cluster scores were then regressed against the subcortical brain volumetric measurements. RESULTS: Accounting for site and genetic burden (the interaction of age and CAG repeat length) smaller caudate and putamen volumes were related to clusters reflecting motor symptom severity, cognitive control, and verbal learning. CONCLUSIONS: Variable reduction of the HD phenotype using cluster analysis revealed biologically related domains of HD and are suitable for future research with this population. Our cognitive control cluster scores show sensitivity to changes in basal ganglia both within and outside the striatum that may not be captured by examining only motor scores. (JINS, 2017, 23, 159-170).

Prefrontal cortex white matter tracts in prodromal Huntington disease.

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

Short-interval observational data to inform clinical trial design in Huntington's disease.

OBJECTIVES: To evaluate candidate outcomes for disease-modifying trials in Huntington's disease (HD) over 6-month, 9-month and 15-month intervals, across multiple domains. To present guidelines on rapid efficacy readouts for disease-modifying trials. METHODS: 40 controls and 61 patients with HD, recruited from four EU sites, underwent 3 T MRI and standard clinical and cognitive assessments at baseline, 6 and 15 months. Neuroimaging analysis included global and regional change in macrostructure (atrophy and cortical thinning), and microstructure (diffusion metrics). The main outcome was longitudinal effect size (ES) for each outcome. Such ESs can be used to calculate sample-size requirements for clinical trials for hypothesised treatment efficacies. RESULTS: Longitudinal changes in macrostructural neuroimaging measures such as caudate atrophy and ventricular expansion were significantly larger in HD than controls, giving rise to consistently large ES over the 6-month, 9-month and 15-month intervals. Analogous ESs for cortical metrics were smaller with wide CIs. Microstructural (diffusion) neuroimaging metrics ESs were also typically smaller over the shorter intervals, although caudate diffusivity metrics performed strongly over 9 and 15 months. Clinical and cognitive outcomes exhibited small longitudinal ESs, particularly over 6-month and 9-month intervals, with wide CIs, indicating a lack of precision. CONCLUSIONS: To exploit the potential power of specific neuroimaging measures such as caudate atrophy in disease-modifying trials, we propose their use as (1) initial short-term readouts in early phase/proof-of-concept studies over 6 or 9 months, and (2) secondary end points in efficacy studies over longer periods such as 15 months.

T1ρ imaging in premanifest Huntington disease reveals changes associated with disease progression.

BACKGROUND: Imaging biomarkers sensitive to Huntington's disease (HD) during the premanifest phase preceding motor diagnosis may accelerate identification and evaluation of potential therapies. For this purpose, quantitative MRI sensitive to tissue microstructure and metabolism may hold great potential. We investigated the potential value of T1ρ relaxation to detect pathological changes in premanifest HD (preHD) relative to other quantitative relaxation parameters. METHODS: Quantitative MR parametric mapping was used to assess differences between 50 preHD subjects and 26 age- and sex-matched controls. Subjects with preHD were classified into two progression groups based on their CAG-age product (CAP) score; a high and a low/moderate CAP group. Voxel-wise and region-of-interest analyses were used to assess changes in the quantitative relaxation times. RESULTS: T1ρ showed a significant increase in the relaxation times in the high-CAP group, as compared to controls, largely in the striatum. The T1ρ changes in the preHD subjects showed a significant relationship with CAP score. No significant changes in T2 or T2* relaxation times were found in the striatum. T2* relaxation changes were found in the globus pallidus, but no significant changes with disease progression were found. CONCLUSION: These data suggest that quantitative T1ρ mapping may provide a useful marker for assessing disease progression in HD. The absence of T2 changes suggests that the T1ρ abnormalities are unlikely owing to altered water content or tissue structure. The established sensitivity of T1ρ to pH and glucose suggests that these factors are altered in HD perhaps owing to abnormal mitochondrial function.

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

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

Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing.

Automated analysis of structural magnetic resonance images is a promising way to improve early detection of neurodegenerative brain diseases. Clinical applications of such methods involve multiple scanners with potentially different hardware and/or acquisition sequences and demographically heterogeneous groups. To improve classification performance, we propose to correct effects of subject-specific covariates (such as age, total intracranial volume, and sex) as well as effects of scanner by using a non-linear Gaussian process model. To test the efficacy of the correction, we performed classification of carriers of the genetic mutation leading to Huntington's disease (HD) versus healthy controls. Half of the HD carriers were free of typical HD symptoms and had an estimated 5 to 20years before onset of clinical symptoms, thus providing a model for preclinical diagnosis of a neurodegenerative disease. Structural magnetic resonance brain images were acquired at four sites with pairs of sites which had the identical scanner type, equipment, and acquisition parameters. For automatic classification, we used spatially normalized probabilistic maps of gray matter, then removed confounding effects by Gaussian process regression, and then performed classification with a support vector machine. Voxel-based morphometry of gray matter maps showed disease effects that were spatially wider spread than effects of scanner, but no significant interactions between scanner and disease were found. A model trained with data from a single scanner generalized well to data from a different scanner. When confounding diagnostics groups and scanner during training, e.g. by using controls from one scanner and gene carriers from another, classification accuracy dropped significantly in many cases. By regressing out confounds with Gaussian process regression, the performance levels were comparable to those obtained in scenarios without confound. We conclude that models trained on data acquired with a single scanner generalized well to data acquired with a different same-generation scanner even when the vendor differed. When confounding grouping and scanner during training is unavoidable to gather training data, regressing out inter-scanner and between-subject variability can reduce the loss in accuracy due to the confound.

Regionally selective atrophy of subcortical structures in prodromal HD as revealed by statistical shape analysis.

Huntington disease (HD) is a neurodegenerative disorder that involves preferential atrophy in the striatal complex and related subcortical nuclei. In this article, which is based on a dataset extracted from the PREDICT-HD study, we use statistical shape analysis with deformation markers obtained through "Large Deformation Diffeomorphic Metric Mapping" of cortical surfaces to highlight specific atrophy patterns in the caudate, putamen, and globus pallidus, at different prodromal stages of the disease. On the basis of the relation to cortico-basal ganglia circuitry, we propose that statistical shape analysis, along with other structural and functional imaging studies, may help expand our understanding of the brain circuitry affected and other aspects of the neurobiology of HD, and also guide the most effective strategies for intervention.