Moment-to-Moment Brain Signal Variability Reliably Predicts Psychiatric Treatment Outcome

BACKGROUND: Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes. METHODS: A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple taskbased and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes. RESULTS: Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total rACTUAL, PREDICTED = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long. CONCLUSIONS: Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

Automated summary

Task-based brain signal variability has been found to be the most reliable indicator for predicting treatment outcomes of psychiatric disorders, outperforming self-reports and other neural measures. This suggests that moment-to-moment fMRI signal variability may serve as a prognostic indicator for clinical outcomes.