Cortical cell loss is a core feature of Huntington’s disease (HD), beginning many years before clinical motor diagnosis, during the premanifest stage. However, it is unclear how genetic topography... Show moreCortical cell loss is a core feature of Huntington’s disease (HD), beginning many years before clinical motor diagnosis, during the premanifest stage. However, it is unclear how genetic topography relates to cortical cell loss. Here, we explore the biological processes and cell types underlying this relationship and validate these using cell-specific post-mortem data.Eighty premanifest participants on average 15 years from disease onset and 71 controls were included. Using volumetric and diffusion MRI we extracted HD-specific whole brain maps where lower grey matter volume and higher grey matter mean diffusivity, relative to controls, were used as proxies of cortical cell loss. These maps were combined with gene expression data from the Allen Human Brain Atlas (AHBA) to investigate the biological processes relating genetic topography and cortical cell loss.Cortical cell loss was positively correlated with the expression of developmental genes (i.e. higher expression correlated with greater atrophy and increased diffusivity) and negatively correlated with the expression of synaptic and metabolic genes that have been implicated in neurodegeneration. These findings were consistent for diffusion MRI and volumetric HD-specific brain maps.As wild-type huntingtin is known to play a role in neurodevelopment, we explored the association between wild-type huntingtin (HTT) expression and developmental gene expression across the AHBA. Co-expression network analyses in 134 human brains free of neurodegenerative disorders were also performed. HTT expression was correlated with the expression of genes involved in neurodevelopment while co-expression network analyses also revealed that HTT expression was associated with developmental biological processes.Expression weighted cell-type enrichment (EWCE) analyses were used to explore which specific cell types were associated with HD cortical cell loss and these associations were validated using cell specific single nucleus RNAseq (snRNAseq) data from post-mortem HD brains.The developmental transcriptomic profile of cortical cell loss in preHD was enriched in astrocytes and endothelial cells, while the neurodegenerative transcriptomic profile was enriched for neuronal and microglial cells. Astrocyte-specific genes differentially expressed in HD post-mortem brains relative to controls using snRNAseq were enriched in the developmental transcriptomic profile, while neuronal and microglial-specific genes were enriched in the neurodegenerative transcriptomic profile.Our findings suggest that cortical cell loss in preHD may arise from dual pathological processes, emerging as a consequence of neurodevelopmental changes, at the beginning of life, followed by neurodegeneration in adulthood, targeting areas with reduced expression of synaptic and metabolic genes. These events result in age-related cell death across multiple brain cell types. Show less
Cunha, P.; Petit, E.; Coutelier, M.; Coarelli, G.; Mariotti, C.; Faber, J.; ... ; Durr, A. 2023
Although the best-known spinocerebellar ataxias (SCAs) are triplet repeat diseases, many SCAs are not caused by repeat expansions. The rarity of individual non-expansion SCAs, however, has made it... Show moreAlthough the best-known spinocerebellar ataxias (SCAs) are triplet repeat diseases, many SCAs are not caused by repeat expansions. The rarity of individual non-expansion SCAs, however, has made it difficult to discern genotype-phenotype correlations. We therefore screened individuals who had been found to bear variants in a non-expansion SCA-associated gene through genetic testing, and after we eliminated genetic groups that had fewer than 30 subjects, there were 756 subjects bearing single-nucleotide variants or deletions in one of seven genes: CACNA1A (239 subjects), PRKCG (175), AFG3L2 (101), ITPR1 (91), STUB1 (77), SPTBN2 (39), or KCNC3 (34). We compared age at onset, disease features, and progression by gene and variant. There were no features that reliably distinguished one of these SCAs from another, and several genes—CACNA1A, ITPR1, SPTBN2, and KCNC3—were associated with both adult-onset and infantile-onset forms of disease, which also differed in presentation. Nevertheless, progression was overall very slow, and STUB1-associated disease was the fastest. Several variants in CACNA1A showed particularly wide ranges in age at onset: one variant produced anything from infantile developmental delay to ataxia onset at 64 years of age within the same family. For CACNA1A, ITPR1, and SPTBN2, the type of variant and charge change on the protein greatly affected the phenotype, defying pathogenicity prediction algorithms. Even with next-generation sequencing, accurate diagnosis requires dialogue between the clinician and the geneticist. Show less
Background Cognitive impairment is a core feature of Huntington's disease (HD), however, the onset and rate of cognitive decline is highly variable. Apathy is the most common neuropsychiatric... Show moreBackground Cognitive impairment is a core feature of Huntington's disease (HD), however, the onset and rate of cognitive decline is highly variable. Apathy is the most common neuropsychiatric symptom of HD, and is associated with cognitive impairment. The aim of this study was to investigate apathy as a predictor of subsequent cognitive decline over 2 years in premanifest and early HD, using a prospective, longitudinal design. Methods A total of 118 premanifest HD gene carriers, 111 early HD and 118 healthy control participants from the multi-centre TRACK-HD study were included. Apathy symptoms were assessed at baseline using the apathy severity rating from the Short Problem Behaviours Assessment. A composite of 12 outcome measures from nine cognitive tasks was used to assess cognitive function at baseline and after 24 months. Results In the premanifest group, after controlling for age, depression and motor signs, more apathy symptoms predicted faster cognitive decline over 2 years. In contrast, in the early HD group, more motor signs, but not apathy, predicted faster subsequent cognitive decline. In the control group, only older age predicted cognitive decline. Conclusions Our findings indicate that in premanifest HD, apathy is a harbinger for cognitive decline. In contrast, after motor onset, in early diagnosed HD, motor symptom severity more strongly predicts the rate of cognitive decline. Show less
Background: Neuroimaging shows considerable promise in generating sensitive and objective outcome measures for therapeutic trials across a range of neurodegenerative conditions. For volumetric... Show moreBackground: 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. Show less
Background: Determination of disease onset in Huntington's disease is made by clinical experience. The diagnostic confidence level is an assessment regarding the certainty about the clinical... Show moreBackground: Determination of disease onset in Huntington's disease is made by clinical experience. The diagnostic confidence level is an assessment regarding the certainty about the clinical diagnosis based on motor signs. A level of 4 means the rater has >= 99% confidence motor abnormalities are unequivocal signs of disease. However, it does not state which motor abnormalities are signs of disease and how many must be present.Objective: Our aim is to explore how accurate the diagnostic confidence level is in estimating disease onset using the Enroll-HD data set. For clinical disease onset we use a cut-off total motor score >5 of the Unified Huntington's Disease Rating Scale. This score is used in the TRACK-HD study, with <= 5 indicating no substantial motor signs in premanifests.Methods: At baseline premanifest participants who converted to manifest (converters) and non-converters were compared for clinical symptoms and diagnostic confidence level. Clinical symptoms and diagnostic confidence levels were longitudinally displayed in converters.Results: Of 3731 eligible participants, 455 were converters and 3276 non-converters. Baseline diagnostic confidence levels were significantly higher in converters compared to non-converters (P < 0.001). 232 (51%) converters displayed a baseline motor score >5 (mean = 6.7). Converters had significantly more baseline clinical symptoms, and higher disease burden compared to non-converters (P < 0.001). Diagnostic confidence level before disease onset ranged between 1 and 3 in converters.Conclusions: According to this data the diagnostic confidence level is not an accurate instrument to determine phenoconversion. With trials evaluating disease modifying therapies it is important to develop more reliable diagnostic criteria. Show less
Background The composite Unified Huntington's Disease Rating Scale (cUHDRS) is a multidimensional measure of progression in Huntington's disease (HD) being used as a primary outcome in clinical... Show moreBackground The composite Unified Huntington's Disease Rating Scale (cUHDRS) is a multidimensional measure of progression in Huntington's disease (HD) being used as a primary outcome in clinical trials investigating potentially disease-modifying huntingtin-lowering therapies.Objective Evaluating volumetric and structural connectivity correlates of the cUHDRS.Methods One hundred and nineteen premanifest and 119 early-HD participants were included. Gray and white matter (WM) volumes were correlated with cUHDRS cross-sectionally and longitudinally using voxel-based morphometry. Correlations between baseline fractional anisotropy (FA); mean, radial, and axial diffusivity; and baseline cUHDRS were examined using tract-based spatial statistics.Results Worse performance in the cUHDRS over time correlated with longitudinal volume decreases in the occipito-parietal cortex and centrum semiovale, whereas lower baseline scores correlated with decreased volume in the basal ganglia and surrounding WM. Lower cUHDRS scores were also associated with reduced FA and increased diffusivity at baseline.Conclusion The cUHDRS correlates with imaging biomarkers and tracks atrophy progression in HD supporting its biological relevance. (c) 2021 International Parkinson and Movement Disorder Society Show less
Roux, T.; Barbier, M.; Papin, M.; Davoine, C.S.; Sayah, S.; Coarelli, G.; ... ; SPATAX Network 2020
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... Show moreBackground 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 Phillips 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. Show less
