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The ENIGMA Toolbox: Cross-disorder integration and multiscale neural contextualization of multisite neuroimaging datasets

By Sara Lariviere, Casey Paquola, Bo-yong Park, Jessica Royer, Yezhou Wang, Oualid Benkarim, Reinder Vos de Wael, Sofie Louise Valk, Sophia I Thomopoulos, Matthias Kirschner, ENIGMA Consortium, Lindsay B Lewis, Alan C. Evans, Sanjay Sisodiya, Carrie McDonald, Paul M. Thompson, Boris C. Bernhardt

Posted 22 Dec 2020
bioRxiv DOI: 10.1101/2020.12.21.423838

Among 'big data' initiatives, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium - a worldwide alliance of over 2,000 scientists diversified into over 50 Working Groups - has yielded some of the largest studies of the healthy and diseased brain. Integration of multisite datasets to assess transdiagnostic similarities and differences and to contextualize findings with respect to neural organization, however, have been limited. Here, we introduce the ENIGMA Toolbox, a Python/Matlab ecosystem for (i) accessing ENIGMA datasets, allowing for cross-disorder analysis, (ii) visualizing data on brain surfaces, and (iii) contextualizing findings at the microscale (postmortem cytoarchitecture and gene expression) and macroscale (structural and functional connectomes). Our Toolbox equips scientists with tutorials to explore molecular, histological, and network correlates of noninvasive neuroimaging markers of brain disorders. Moreover, our Toolbox bridges the gap between standardized data processing protocols and analytic workflows and facilitates cross-consortia initiatives. The Toolbox is documented and openly available at http://enigma-toolbox.readthedocs.io.

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