The Psychosis MRI Shared Data Resource (Psy-ShareD)

IF 3.5 2区 医学 Q1 NEUROIMAGING Human Brain Mapping Pub Date : 2025-02-21 DOI:10.1002/hbm.70165
Paul Allen, Mariana Zurita, Rubaida Easmin, Sara Bucci, Matthew J. Kempton, Jack Rogers, Urvakhsh M. Mehta, Philip K. McGuire, Stephen M. Lawrie, Heather Whalley, Ary Gadelha, Graham K. Murray, Jane R. Garrison, Sophia Frangou, Rachel Upthegrove, Simon L. Evans, Veena Kumari, the Psy-ShareD Partnership
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Abstract

Neuroimaging research in the field of schizophrenia and other psychotic disorders has sought to investigate neuroanatomical markers, relative to healthy control groups. In recent decades, a large number of structural magnetic resonance imaging (MRI) studies have been funded and undertaken, but their small sample sizes and heterogeneous methods have led to inconsistencies across findings. To tackle this, efforts have been made to combine datasets across studies and sites. While notable recent multicentre initiatives and the resulting meta- and mega-analytical outputs have progressed the field, efforts have generally been restricted to MRI scans in one or two illness stages, often overlook patient heterogeneity, and study populations have rarely been globally representative of the diversity of patients who experience psychosis. Furthermore, access to these datasets is often restricted to consortia members who can contribute data, likely from research institutions located in high-income countries. The Psychosis MRI Shared Data Resource (Psy-ShareD) is a new open access structural MRI data sharing partnership that will host pre-existing structural T1-weighted MRI data collected across multiple sites worldwide, including the Global South. MRI T1 data included in Psy-ShareD will be available in image and feature-level formats, having been harmonised using state-of-the-art approaches. All T1 data will be linked to demographic and illness-related (diagnosis, symptoms, medication status) measures, and in a number of datasets, IQ and cognitive data, and medication history will also be available, allowing subgroup and dimensional analyses. Psy-ShareD will be free-to-access for all researchers. Importantly, comprehensive data catalogues, scientific support and training resources will be available to facilitate use by early career researchers and build capacity in the field. We are actively seeking new collaborators to contribute further T1 data. Collaborators will benefit in terms of authorships, as all publications arising from Psy-ShareD will include data contributors as authors.

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来源期刊
Human Brain Mapping
Human Brain Mapping 医学-核医学
CiteScore
8.30
自引率
6.20%
发文量
401
审稿时长
3-6 weeks
期刊介绍: Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged. Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.
期刊最新文献
Infraslow Dynamic Patterns in Human Cortical Networks Track a Spectrum of External to Internal Attention Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis The Psychosis MRI Shared Data Resource (Psy-ShareD) From Big Data to the Clinic: Methodological and Statistical Enhancements to Implement the UK Biobank Imaging Framework in a Memory Clinic Transdiagnostic Connectome-Based Prediction of Response Inhibition
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