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Decentralized Mixed Effects Modeling in COINSTAC.
Basodi, Sunitha; Raja, Rajikha; Gazula, Harshvardhan; Romero, Javier Tomas; Panta, Sandeep; Maullin-Sapey, Thomas; Nichols, Thomas E; Calhoun, Vince D.
Afiliação
  • Basodi S; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
  • Raja R; St. Jude Children's Research Hospital, Memphis, TN, USA.
  • Gazula H; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Romero JT; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
  • Panta S; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
  • Maullin-Sapey T; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Nichols TE; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Calhoun VD; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Neuroinformatics ; 22(2): 163-175, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38424371
ABSTRACT
Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esquizofrenia / Neuroimagem Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esquizofrenia / Neuroimagem Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article