Cortical Similarities in Psychiatric and Mood Disorders Identified in Federated VBM Analysis via COINSTAC
Authors: Rootes-Murdy Kelly , Panta Sandeep , Kelly Ross , Romero Javier , Quidé Yann , Cairns Murray J. , Loughland Carmel , Carr Vaughan J. , Catts Stanley V. , Jablensky Assen , Green Melissa J. , Henskens Frans , Kiltschewskij Dylan , Michie Patricia T. , Mowry Bryan , Pantelis Christos , Rasser Paul E. , Reay William R. , Schall Ulrich , Scott Rodney J. , Watkeys Oliver J. , Cairns Murray J. , Roberts Gloria , Mitchell Philip B. , Fullerton Janice M. , Overs Bronwyn J. , Kikuchi Masataka , Hashimoto Ryota , Matsumoto Junya , Fukunaga Masaki , Sachdev Perminder S. , Brodaty Henry , Wen Wei , Jiang Jiyang , Fani Negar , Ely Timothy D. , Lorio Adriana , Stevens Jennifer S. , Ressler Kerry , Jovanovic Tanja , van Rooij Sanne J. H. , Plis Sergey , Sarwate Anand , Calhoun Vince D. 
Published: September 2023

AbstractPsychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have areas of significant overlap across multiple domains including genetics, neurochemistry, symptom profiles, and regional gray matter alterations. Various structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across these different disorders, though few direct comparisons have been conducted. Given the overlap in symptom presentations and GM alterations, these disorders may have a common etiology or neuroanatomical basis that may relate to a certain vulnerability for mental illness. Pooling large data or heterogeneous data can ensure representation of several participant factors, providing more accurate results. However, ensuring large enough datasets at any one site can be cumbersome, costly, and may take many years of data collection. Large scale collaborative research is already facilitated by current data repositories and neuroinformatics consortia such as the Enhacing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium, institutionally supported databases, and data archives. However, these data sharing methodologies can still suffer from significant barriers. Federated approaches can augment these approaches and mitigate some of these barriers by enabling access or more sophisticated, shareable and scaled up analyses of large-scale data which may not be shareable and can easily be scaled up with the number of sites. In the current study, we examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). Briefly, COINSTAC (https://coinstac.trendscenter.org) is an open-source decentralized analysis application that provides a venue for analyses of neuroimaging datasets without sharing individual level data, while maintaining granular control of privacy. Through federated analysis, we examined T1-weighted images (N = 3,287) from eight psychiatric diagnostic groups across seven sites. We identified significant overlap in the GM patterns of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions, specifically the bilateral insula, that may indicate a possible shared vulnerability to psychiatric disorders.