Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.
Authors
Affiliations (20)
Affiliations (20)
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; Laboratory for Drug Discovery and Disease Research, Shionogi & Co., Ltd., Osaka, Japan. Electronic address: [email protected].
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; Laboratory for Drug Discovery and Development, Shionogi & Co., Ltd., Osaka, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; Department of Biomedical Data Science, School of Medicine, Fujita Health University, Aichi, Japan; International Center for Brain Science, Fujita Health University, Aichi, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; XNef, Inc., Kyoto, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Japan; Center for Brain Integration Research, Tokyo Medical and Dental University, Japan.
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Japan.
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; UTokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan.
- Department of Radiology, International University of Health and Welfare, Mita Hospital, Tokyo, Japan.
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan.
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan; Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan.
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan.
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan.
- Laboratory for Drug Discovery and Disease Research, Shionogi & Co., Ltd., Osaka, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; RIKEN, Center for Advanced Intelligence Project, Tokyo, Japan. Electronic address: [email protected].
Abstract
Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD). We explored combinations of options in four sub-processes of the analysis pipelines: six types of brain parcellation, four types of functional connectivity (FC) estimations, three types of site-difference harmonization, and five types of machine-learning methods. A total of 360 different MDD classification biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four sites) as the discovery dataset, and datasets from other projects acquired with heterogeneous protocols (449 participants from four sites) were used for independent validation. We repeated the procedure after swapping the roles of the two datasets to identify superior pipelines, regardless of the discovery dataset. The classification results of the top 10 biomarkers showed high similarity, and weight similarity was observed between eight of the biomarkers, except for two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other psychiatric disorders (autism spectrum disorder and schizophrenia), and eight of the biomarkers exhibited sufficient classification performance for both disorders. Our results will be useful for establishing a standardized pipeline for classification biomarkers.