A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity

• Reihaneh Hassanzadeh ,

  Roles Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: rhassanzadeh6@gatech.edu

  Affiliations Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America

  https://orcid.org/0000-0001-8022-8619

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• Anees Abrol,

  Roles Supervision, Writing – review & editing

  Affiliation Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America

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• Godfrey Pearlson,

  Roles Writing – review & editing

  Affiliation Department of Psychiatry & Neuroscience, Yale University School of Medicine, New Haven, CT, United States of America

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• Jessica A. Turner,

  Roles Writing – review & editing

  Affiliation Department of Psychiatry and Behavioral Health, Ohio State University, Columbus, OH, United States of America

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• Vince D. Calhoun

  Roles Funding acquisition, Resources, Supervision, Writing – review & editing

  Affiliations Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America

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