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SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform.

ABSTRACT: Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.

SUBMITTER: Mandal PK

PROVIDER: S-EPMC10582723 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform.

Mandal Pravat K PK Jindal Komal K Roy Saurav S Arora Yashika Y Sharma Shallu S Joon Shallu S Goel Anshika A Ahasan Zoheb Z Maroon Joseph C JC Singh Kuldeep K Sandal Kanika K Tripathi Manjari M Sharma Pooja P Samkaria Avantika A Gaur Shradha S Shandilya Sandhya S

Frontiers in neurology 20231004

Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and ...[more]

PMID: 37859652

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Project description:BackgroundThe study of complex neurodegenerative diseases is moving away from hypothesis-driven biological methods toward large scale multimodal approaches, requiring standardized collaborative efforts. Enroll-HD exemplifies such an integrated clinical research platform, designed and implemented to meet the research and clinical needs of Huntington's disease (HD). The aim of this study was to describe the unique organization of Enroll-HD and report baseline data analyses of its core study.MethodsThe Enroll-HD platform incorporates electronic data capture, biosampling, and a longitudinal observational study spanning four continents (ClinicalTrials.gov Identifier: NCT01574053). The primary study population includes HD gene expansion carriers (HDGECs; CAG expansion ≥36), subdivided into manifest/premanifest HD. The control population consists of genotype-negative first-degree relatives and family controls not genetically related. The study includes 10 core clinical assessments covering motor, cognitive, and behavioral domains.ResultsThis data set comprises 1,534 participants (HDGEC = 1,071; controls = 463). Participant retention was high; 42 participants prematurely withdrew from the study. Mean ± standard deviation SD CAG repeat size was 43.5 ± 3.5 for HDGECs and 19.8 ± 3.4 for controls. Motor and behavioral assessments identified numerical differences between controls and HDGECs (manifest > premanifest > controls). Functional and independence assessments were generally similar for the premanifest and control groups with overlap in range of scores obtained. For the majority of cognitive tests, there were large differences between participants with manifest HD and all other groups.ConclusionsThese first data from the Enroll-HD clinical research platform demonstrate the maturity and potential of the platform in collecting high-quality, clinically relevant data. Future data sets will be substantially larger as the platform expands longitudinally and regionally.

Project description:Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

Dataset Information

SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform.

Publications

SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform.

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