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Functional Connectivity Predicting Transdiagnostic Treatment Outcomes in Internalizing Psychopathologies.

Kai Zhang1, Heide Klumpp2,3, Jagan Jimmy4

  • 1Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston.

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Summary

Whole-brain functional connectivity (FC) reliably predicts treatment outcomes for internalizing psychopathologies, including depression and anxiety. This finding supports personalized psychiatry by linking neural patterns to treatment response across different therapies.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Precision Medicine

Background:

  • Internalizing psychopathologies (IPs) like depression and anxiety require effective, personalized treatments.
  • Predicting treatment response using neuroimaging could advance precision medicine in psychiatry.
  • The predictive power of whole-brain functional connectivity (FC) for IP treatment outcomes across modalities is not well understood.

Purpose of the Study:

  • To determine if pretreatment FC patterns can predict multidimensional treatment outcomes in patients with IPs.
  • To assess if predictive performance generalizes across different diagnoses and treatment modalities for IPs.

Main Methods:

  • A prognostic study analyzed baseline neuroimaging and clinical data from 181 patients with IPs in two randomized clinical trials.
  • A regularized canonical correlation analysis model was trained using pretreatment FC patterns.
  • The model's ability to predict outcomes (depression, anxiety, worry, rumination, emotion regulation) was tested across diagnoses and treatment types (CBT, SSRI, ST).

Main Results:

  • Baseline whole-brain FC robustly predicted multidimensional symptom changes in patients with IPs.
  • Predictions were significant at the individual level (r=0.37), across diagnoses (r=0.24), and across treatment modalities (ST: r=0.28; SSRI: r=0.39; CBT: r=0.32).
  • Key predictive connections were found within the default mode network and attention networks; performance decreased with fewer neural systems or outcome dimensions.

Conclusions:

  • Whole-brain FC reliably predicts treatment outcomes for IPs across diagnoses and treatment types.
  • Neural connectivity patterns are associated with clinical improvements from various psychiatric treatments.
  • These findings support the development of personalized treatment approaches in psychiatry based on neurobiological markers.