The ENIGMA-Epilepsy working group: Mapping disease from large data sets.

Sisodiya, Sanjay M; Whelan, Christopher D; Hatton, Sean N; Huynh, Khoa; Altmann, Andre; Ryten, Mina; Vezzani, Annamaria; Caligiuri, Maria Eugenia; Labate, Angelo; Gambardella, Antonio; Ives-Deliperi, Victoria; Meletti, Stefano; Munsell, Brent C; Bonilha, Leonardo; Tondelli, Manuela; Rebsamen, Michael; Rummel, Christian; Vaudano, Anna Elisabetta; Wiest, Roland; Balachandra, Akshara R; ... (2022). The ENIGMA-Epilepsy working group: Mapping disease from large data sets. Human brain mapping, 43(1), pp. 113-128. Wiley-Blackwell 10.1002/hbm.25037

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Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.

Item Type:	Journal Article (Review Article)
Division/Institute:	04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
UniBE Contributor:	Rebsamen, Michael Andreas, Rummel, Christian, Wiest, Roland Gerhard Rudi
Subjects:	600 Technology > 610 Medicine & health 500 Science > 570 Life sciences; biology
ISSN:	1065-9471
Publisher:	Wiley-Blackwell
Language:	English
Submitter:	Martin Zbinden
Date Deposited:	30 Jun 2020 17:25
Last Modified:	03 Mar 2024 00:10
Publisher DOI:	10.1002/hbm.25037
PubMed ID:	32468614
Uncontrolled Keywords:	DTI MRI covariance deep learning event-based modeling gene expression genetics imaging quantitative rsfMRI
URI:	https://boris.unibe.ch/id/eprint/144655