Visualizing functional network connectivity differences using an explainable machine-learning method.

Journal: Physiological measurement
PMID: 40245920

Abstract

. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. While machine learning models can improve classification accuracy, they often lack interpretability, making it difficult to understand how they arrive at their decisions.. Explainable machine learning helps address this issue by identifying which features contribute most to the model's predictions. In this study, we introduce a novel framework leveraging SHapley Additive exPlanations (SHAPs) to identify crucial FNC features distinguishing between two distinct population classes.. Initially, we validate our approach using synthetic data. Subsequently, applying our framework, we ascertain FNC biomarkers distinguishing between, controls and schizophrenia (SZ) patients with accuracy of 81.04% as well as middle aged adults and old aged adults with accuracy 71.38%, respectively, employing random forest, XGBoost, and CATBoost models.. Our analysis underscores the pivotal role of the cognitive control network (CCN), subcortical network (SCN), and somatomotor network in discerning individuals with SZ from controls. In addition, our platform found CCN and SCN as the most important networks separating young adults from older.

Authors

  • Mohammad S E Sendi
    Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia.
  • Vaibhavi S Itkyal
    Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.
  • Sabrina J Edwards-Swart
    Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.
  • Ji Ye Chun
    Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
  • Daniel H Mathalon
    Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States.
  • Judith M Ford
    Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
  • Adrian Preda
    Department of Psychiatry and Human Behavior, University of California, Irvine, CA, United States of America.
  • Theo G M van Erp
    Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
  • Godfrey D Pearlson
    Olin Neuropsychiatry Research Center, Hartford Hospital (IOL Campus), Hartford, CT, USA; Department of Psychiatry and Neurobiology, Yale University School of Medicine, New Haven, CT, USA.
  • Jessica A Turner
    Psychology Department & Neuroscience Institute, Georgia State University, Atlanta GA, USA.
  • Vince D Calhoun
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico; Department of Neurosciences, University of New Mexico, Albuquerque, New Mexico.

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