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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: 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.

Objective Clinical, behavioural, and neurophysiological effects of cerebellar transcranial direct current stimulation (tDCS) are highly variable and difficult to predict. We aimed to examine associations between cerebellar tDCS-induced electric field strength, morphometric posterior fossa parameters, and skin-cerebellum distance. As a secondary objective, field characteristics were compared between cephalic and extracephalic electrode configurations. Methods Electric field simulations of midline cerebellar tDCS (7 × 5 cm electrodes, current intensities of 2 mA) were performed on MRI-based head models from 37 healthy adults using buccinator, frontopolar, and lower neck reference electrodes. Average field strengths were determined in eight regions of interest (ROIs) covering the anterior and posterior vermis and cerebellar hemispheres. Besides skin-cerebellum distance, various angles were measured between posterior fossa structures. Multivariable linear regression models were used to identify predictors of field strength in different ROIs. Results Skin-cerebellum distance and “pons angle” were independently associated with field strength in the anterior and posterior vermis. “Cerebellar angle” and skin-cerebellum distance affected field strength in anterior and posterior regions of the right cerebellar hemisphere. Field strengths in all examined cerebellar areas were highest in the frontopolar and lowest in the lower neck montage, while the opposite was found for field focality. The lower neck montage induced considerably less spreading toward anterior cerebellar regions compared with the buccinator and frontopolar montages, which resulted in a more evenly distributed field within the cerebellum. Conclusion In addition to skin-cerebellum distance, interindividual differences in posterior fossa morphometry, specifically pons and cerebellar angle, explain part of the variability in cerebellar tDCS-induced electric field strengt

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: 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 description and modeling of disease progression. Deciphering potential underlying unknowns solely 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. The aim was to improve the application HMMs to multivariate time-series of categorically distributed data. Here, we used HHMs to study prediction of the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. We used HHMs to investigate the prediction of loss of the ability to walk freely, representing a major clinical deterioration in the most common autosomal-dominant inherited ataxia disorder worldwide. Results: We present a prediction pipeline which processes data paired with a configuration file, enabling to construct, validate and query a fully parameterized HMM-based model. 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 done on random data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: HHMs are a promising approach to study biomedical data that naturally are represented as multivariate time-series. Our implementation of a HHMs framework is publicly available and can easily be adapted for further applications.

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role in linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains. We present a knowledge graph initially built on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder. The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to (2) convert the data efficiently into a knowledge graph. Due to the limited amount of patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising graph structures and Conditional Random Fields. In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role in linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains. We present a knowledge graph initially built on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder. The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to (2) convert the data efficiently into a knowledge graph. Due to the limited amount of patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising graph structures and Conditional Random Fields. In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role in linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains. We present a knowledge graph initially built on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder. The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to (2) convert the data efficiently into a knowledge graph. Due to the limited amount of patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising graph structures and Conditional Random Fields. In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role in linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains. We present a knowledge graph initially built on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder. The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to (2) convert the data efficiently into a knowledge graph. Due to the limited amount of patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising graph structures and Conditional Random Fields. In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role in linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains. We present a knowledge graph initially built on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder. The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to (2) convert the data efficiently into a knowledge graph. Due to the limited amount of patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising graph structures and Conditional Random Fields. In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

Contains fulltext : 221548.pdf (publisher's version ) (Open Access)

Contains fulltext : 221548.pdf (publisher's version ) (Open Access)

Contains fulltext : 221548.pdf (publisher's version ) (Open Access)

Contains fulltext : 221548.pdf (publisher's version ) (Open Access)

The clinical heterogeneity of Alzheimer's disease is not reflected in the rather diffuse cortical deposition of amyloid-beta. We assessed the relationship between clinical symptoms, in vivo tau pathology, amyloid distribution, and hypometabolism in variants of Alzheimer's disease using novel multimodal PET imaging techniques. Tau pathology was primarily observed in brain regions related to clinical symptoms and overlapped with areas of hypometabolism. In contrast, amyloid-beta deposition was diffusely distributed over the entire cortex. Tau PET imaging may thus serve as a valuable biomarker for the localization of neuronal injury in vivo and may help to validate atypical subtypes of Alzheimer's disease.

Cognitive reserve (CR) is defined as the ability to maintain functionality despite accumulating pathology. Education has been used as a proxy for CR. For example, by using positron emission tomography imaging, higher educated Alzheimer's disease (AD) patients presented increased amyloid beta pathology than lower educated patients despite equal symptomatology. Whether similar associations exist for in vivo tau pathology remains elusive. We utilized [F-18] AV-1451 positron emission tomography imaging to examine whether high-educated AD patients (n = 12) present more severe tau pathology compared with low-educated patients (n = 12) despite equal clinical severity in regions of interest corresponding to the pathologic disease stages defined by Braak & Braak. We report tau pathology in advanced Braak stages associated with parietal and frontal regions in high-educated AD patients, whereas in low-educated AD patients tau accumulation is still confined to lower Braak stages associated with temporal and cingulate regions. Highly educated AD patients seem to be able to tolerate more tau tangle pathology than lower educated patients with comparable cognitive impairment supporting the cognitive reserve hypothesis. (C) 2017 Elsevier Inc. All rights reserved.

The clinical heterogeneity of Alzheimer's disease is not reflected in the rather diffuse cortical deposition of amyloid-beta. We assessed the relationship between clinical symptoms, in vivo tau pathology, amyloid distribution, and hypometabolism in variants of Alzheimer's disease using novel multimodal PET imaging techniques. Tau pathology was primarily observed in brain regions related to clinical symptoms and overlapped with areas of hypometabolism. In contrast, amyloid-beta deposition was diffusely distributed over the entire cortex. Tau PET imaging may thus serve as a valuable biomarker for the localization of neuronal injury in vivo and may help to validate atypical subtypes of Alzheimer's disease.

Cognitive reserve (CR) is defined as the ability to maintain functionality despite accumulating pathology. Education has been used as a proxy for CR. For example, by using positron emission tomography imaging, higher educated Alzheimer's disease (AD) patients presented increased amyloid beta pathology than lower educated patients despite equal symptomatology. Whether similar associations exist for in vivo tau pathology remains elusive. We utilized [F-18] AV-1451 positron emission tomography imaging to examine whether high-educated AD patients (n = 12) present more severe tau pathology compared with low-educated patients (n = 12) despite equal clinical severity in regions of interest corresponding to the pathologic disease stages defined by Braak & Braak. We report tau pathology in advanced Braak stages associated with parietal and frontal regions in high-educated AD patients, whereas in low-educated AD patients tau accumulation is still confined to lower Braak stages associated with temporal and cingulate regions. Highly educated AD patients seem to be able to tolerate more tau tangle pathology than lower educated patients with comparable cognitive impairment supporting the cognitive reserve hypothesis. (C) 2017 Elsevier Inc. All rights reserved.