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Connectome-Based Predictive Models Optimized for Sleep Differentiate Patients With Depression From Psychiatrically

Anurima Mummaneni1, Carolyn Amir1, Nicholas B Allen2

  • 1Department of Psychology, University of California, Los Angeles, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.

Biological Psychiatry. Cognitive Neuroscience and Neuroimaging
|October 18, 2025
PubMed
Summary
This summary is machine-generated.

Brain-based models predicting sleep duration show promise for diagnosing major depressive disorder (MDD) in adolescents. These models identified differences in brain connectivity linked to depression, suggesting shared neural underpinnings.

Keywords:
AdolescenceDepressionPredictive modelingResting-state fMRISleep

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Biology

Background:

  • The utility of brain-based predictive models for diagnosing major depressive disorder (MDD) using sleep features remains unclear.
  • Adolescent brain development and its relationship with mental health disorders are areas of active research.

Purpose of the Study:

  • To investigate if connectome-based predictive models (CPMs) trained on adolescent brain imaging data can predict sleep duration and aid in the clinical diagnosis of MDD.
  • To explore the neural correlates of MDD by examining functional connectivity patterns associated with sleep prediction.

Main Methods:

  • A connectome-based predictive model (CPM) was trained using resting-state functional magnetic resonance imaging (rs-fMRI) data from the Adolescent Brain Cognitive Development (ABCD) Study.
  • The CPM utilized 35,778 pairwise brain connections from 2349 adolescents (ages 11-12) to predict sleep duration.
  • Model performance was validated in an independent cohort of 78 adolescents (ages 14-18), including participants with MDD and controls, comparing predicted sleep duration with self-reported measures and diagnostic status.

Main Results:

  • The CPM successfully predicted self-reported sleep duration in adolescents with MDD (partial r = 0.332, p = .009).
  • Despite no significant difference in self-reported sleep duration between groups, the CPM accurately distinguished between adolescents with MDD and controls (partial r = 0.334, p < .001).
  • CPM-predicted sleep durations correlated with depression symptom severity (partial r = 0.294, p < .001), driven by hypoconnectivity in resting-state networks.

Conclusions:

  • CPMs trained to predict objective sleep duration are robust and generalizable for adolescent populations.
  • Intrinsic functional connectivity differences in adolescents with MDD are detectable by CPMs optimized for sleep prediction.
  • These findings highlight shared neural bases between sleep health and depression, suggesting CPMs as a potential tool for understanding and diagnosing MDD.