A deep learning approach for mental health quality prediction using functional network connectivity and assessment data.

Journal: Brain imaging and behavior
Published Date: Feb 10, 2024

Abstract

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

Authors

  • Meenu Ajith
    Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, GA, 30303, USA. majith@gsu.edu.
  • Dawn M Aycock
    Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University, P.O. Box 4019, Atlanta, GA, 30302, USA.
  • Erin B Tone
    Department of Psychology, Georgia State University, Atlanta, GA, USA.
  • Jingyu Liu
    Interventional Department, Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
  • Maria B Misiura
    Department of Psychology, Georgia State University, Atlanta, GA, USA.
  • Rebecca Ellis
    Department of Kinesiology and Health, Georgia State University, Atlanta, GA, USA.
  • Sergey M Plis
    Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA.
  • Tricia Z King
    Department of Psychology, Georgia State University, Atlanta, GA, USA.
  • Vonetta M Dotson
    Department of Psychology, Georgia State University, P.O. Box 5010, Atlanta, GA, 30302-5010, USA.
  • Vince Calhoun
    The Mind Research Network, Albuquerque, NM, USA.

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