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BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

With SCAview, we present a prompt and comprehensive tool that enables scientists to browse large datasets of the most common spinocerebellar ataxias intuitively and without technical effort. Basic concept is a visualization of data, with a graphical handling and filtering to select and define subgroups and their comparison. Several plot types to visualize all data points resulting from the selected attributes are provided. The underlying synthetic cohort is based on clinical data from five different European and US longitudinal multicenter cohorts in spinocerebellar ataxia type 1, 2, 3, and 6 (SCA1, 2, 3, and 6) comprising > 1400 patients with overall > 5500 visits. First, we developed a common data model to integrate the clinical, demographic, and characterizing data of each source cohort. Second, the available datasets from each cohort were mapped onto the data model. Third, we created a synthetic cohort based on the cleaned dataset. With SCAview, we demonstrate the feasibility of mapping cohort data from different sources onto a common data model. The resulting browser-based visualization tool with a thoroughly graphical handling of the data offers researchers the unique possibility to visualize relationships and distributions of clinical data, to define subgroups and to further investigate them without any technical effort. Access to SCAview can be requested via the Ataxia Global Initiative and is free of charge.

BACKGROUND AND OBJECTIVES: In spinocerebellar ataxia, ataxia onset can be preceded by mild clinical manifestation, cerebellar and/or brainstem alterations, or biomarker modifications. READISCA is a prospective, longitudinal observational study of patients with spinocerebellar ataxia type 1 (SCA1) and 3 (SCA3) to provide essential markers for therapeutic interventions. We looked for clinical, imaging, or biological markers that are present at an early stage of the disease. METHODS: We enrolled carriers of a pathologic ATXN1 or ATXN3 expansion and controls from 18 US and 2 European ataxia referral centers. Clinical, cognitive, quantitative motor, neuropsychological measures and plasma neurofilament light chain (NfL) measurements were compared between expansion carriers with and without ataxia and controls. RESULTS: We enrolled 200 participants: 45 carriers of a pathologic ATXN1 expansion (31 patients with ataxia [median Scale for the Assessment and Rating of Ataxia: 9; 7-10] and 14 expansion carriers without ataxia [1; 0-2]) and 116 carriers of a pathologic ATXN3 expansion (80 patients with ataxia [7; 6-9] and 36 expansion carriers without ataxia [1; 0-2]). In addition, we enrolled 39 controls who did not carry a pathologic expansion in ATXN1 or ATXN3. Plasma NfL levels were significantly higher in expansion carriers without ataxia than controls, despite similar mean age (controls: 5.7 pg/mL, SCA1: 18.0 pg/mL [p < 0.0001], SCA3: 19.8 pg/mL [p < 0.0001]). Expansion carriers without ataxia differed from controls by significantly more upper motor signs (SCA1 p = 0.0003, SCA3 p = 0.003) and by the presence of sensor impairment and diplopia in SCA3 (p = 0.0448 and 0.0445, respectively). Functional scales, fatigue and depression scores, swallowing difficulties, and cognitive impairment were worse in expansion carriers with ataxia than those without ataxia. Ataxic SCA3 participants showed extrapyramidal signs, urinary dysfunction, and lower motor neuron signs significantly more often than expansion carriers without ataxia. DISCUSSION: READISCA showed the feasibility of harmonized data acquisition in a multinational network. NfL alterations, early sensory ataxia, and corticospinal signs were quantifiable between preataxic participants and controls. Patients with ataxia differed in many parameters from controls and expansion carriers without ataxia, with a graded increase of abnormal measures from control to preataxic to ataxic cohorts. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT03487367.

OBJECTIVE: This study was undertaken to identify magnetic resonance (MR) metrics that are most sensitive to early changes in the brain in spinocerebellar ataxia type 1 (SCA1) and type 3 (SCA3) using an advanced multimodal MR imaging (MRI) protocol in the multisite trial setting. METHODS: SCA1 or SCA3 mutation carriers and controls (n = 107) underwent MR scanning in the US-European READISCA study to obtain structural, diffusion MRI, and MR spectroscopy data using an advanced protocol at 3T. Morphometric, microstructural, and neurochemical metrics were analyzed blinded to diagnosis and compared between preataxic SCA (n = 11 SCA1, n = 28 SCA3), ataxic SCA (n = 14 SCA1, n = 37 SCA3), and control (n = 17) groups using nonparametric testing accounting for multiple comparisons. MR metrics that were most sensitive to preataxic abnormalities were identified using receiver operating characteristic (ROC) analyses. RESULTS: Atrophy and microstructural damage in the brainstem and cerebellar peduncles and neurochemical abnormalities in the pons were prominent in both preataxic groups, when patients did not differ from controls clinically. MR metrics were strongly associated with ataxia symptoms, activities of daily living, and estimated ataxia duration. A neurochemical measure was the most sensitive metric to preataxic changes in SCA1 (ROC area under the curve [AUC] = 0.95), and a microstructural metric was the most sensitive metric to preataxic changes in SCA3 (AUC = 0.92). INTERPRETATION: Changes in cerebellar afferent and efferent pathways underlie the earliest symptoms of both SCAs. MR metrics collected with a harmonized advanced protocol in the multisite trial setting allow detection of disease effects in individuals before ataxia onset with potential clinical trial utility for subject stratification. ANN NEUROL 2023;93:686-701.

Background: Clinical trials for upcoming disease‐modifying therapies of spinocerebellar ataxias (SCA), a group of rare movement disorders, lack endpoints sensitive to early disease progression, when therapeutics will be most effective. In addition, regulatory agencies emphasize the importance of biological outcomes. Objectives: READISCA, a transatlantic clinical trial readiness consortium, investigated whether advanced multimodal magnetic resonance imaging (MRI) detects pathology progression over 6 months in preataxic and early ataxic carriers of SCA mutations. Methods: A total of 44 participants (10 SCA1, 25 SCA3, and 9 controls) prospectively underwent 3‐T MR scanning at baseline and a median [interquartile range] follow‐up of 6.2 [5.9–6.7] months; 44% of SCA participants were preataxic. Blinded analyses of annual changes in structural, diffusion MRI, MR spectroscopy, and the Scale for Assessment and Rating of Ataxia (SARA) were compared between groups using nonparametric testing. Sample sizes were estimated for 6‐month interventional trials with 50% to 100% treatment effect size, leveraging existing large cohort data (186 SCA1, 272 SCA3) for the SARA estimate. Results: Rate of change in microstructural integrity (decrease in fractional anisotropy, increase in diffusivities) in the middle cerebellar peduncle, corona radiata, and superior longitudinal fasciculus significantly differed in SCAs from controls (P < 0.005), with high effect sizes (Cohen's d = 1–2) and moderate‐to‐high responsiveness (|standardized response mean| = 0.6–0.9) in SCAs. SARA scores did not change, and their rate of change did not differ between groups. Conclusions: Diffusion MRI is sensitive to disease progression at very early‐stage SCA1 and SCA3 and may provide a >5‐fold reduction in sample sizes relative to SARA as endpoint for 6‐month‐long trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository. [ABSTRACT FROM AUTHOR]

Transcriptional dysregulation has been described in spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD), an autosomal dominant ataxia caused by a polyglutamine expansion in the ataxin-3 protein. As ataxin-3 is ubiquitously expressed, transcriptional alterations in blood may reflect early changes that start before clinical onset and might serve as peripheral biomarkers in clinical and research settings. Our goal was to describe enriched pathways and report dysregulated genes, which can track disease onset, severity or progression in carriers of the ATXN3 mutation (pre-ataxic subjects and patients). Global dysregulation patterns were identified by RNA sequencing of blood samples from 40 carriers of ATXN3 mutation and 20 controls and further compared with transcriptomic data from post-mortem cerebellum samples of MJD patients and controls. Ten genes— ABCA1 , CEP72 , PTGDS , SAFB2 , SFSWAP , CCDC88C , SH2B1 , LTBP4 , MEG3 and TSPOAP1 —whose expression in blood was altered in the pre-ataxic stage and simultaneously, correlated with ataxia severity in the overt disease stage, were analysed by quantitative real-time PCR in blood samples from an independent set of 170 SCA3/MJD subjects and 57 controls. Pathway enrichment analysis indicated the Gαi signalling and the oestrogen receptor signalling to be similarly affected in blood and cerebellum. SAFB2 , SFSWAP and LTBP4 were consistently dysregulated in pre-ataxic subjects compared to controls, displaying a combined discriminatory ability of 79%. In patients, ataxia severity was associated with higher levels of MEG3 and TSPOAP1. We propose expression levels of SAFB2 , SFSWAP and LTBP4 as well as MEG3 and TSPOAP1 as stratification markers of SCA3/MJD progression, deserving further validation in longitudinal studies and in independent cohorts. [ABSTRACT FROM AUTHOR]