Functional Connectivity Predicting Transdiagnostic Treatment Outcomes in Internalizing Psychopathologies

Predicting treatment outcomes for internalizing psychopathologies (IPs), such as depression and anxiety, holds promise for advancing precision medicine. The extent to which whole-brain functional connectivity (FC) can predict treatment responses for patients with IPs across different therapeutic modalities remains unclear.

To examine whether pretreatment FC patterns predict multidimensional treatment outcomes in patients with IPs and whether predictive performance generalizes across diagnoses and treatment modalities.

This prognostic study analyzed baseline neuroimaging and clinical data from patients with IPs enrolled in 1 of 2 randomized clinical trials (conducted from December 2013 to February 2018 and September 2017 to December 2020). Data analysis for predictive modeling was conducted from September 2024 through March 2025.

Participants were randomized to receive 12 weeks of cognitive-behavioral therapy (CBT), selective-serotonin reuptake inhibitor (SSRI) treatment, or supportive therapy (ST).

A regularized canonical correlation analysis model was trained with pretreatment FC patterns. The ability of the model to predict multidimensional treatment outcomes spanning depression, anxiety, worry, rumination, and emotion regulation was tested. The predictive model was evaluated across diagnostic categories and treatment modalities.

In 181 patients with IPs (mean [SD] age, 27.7 [9.2] years; 127 women [71%] and 52 men [29%]) randomized to receive CBT (n = 89), SSRI treatment (n = 46), or ST (n = 46), baseline whole-brain connectivity robustly predicted multidimensional symptom changes. Predictions were significant at the individual level (r = 0.37, P = .009, permutation test), across diagnoses (r = 0.24, P = .02) and across treatment modalities (ST: r = 0.28, P = .02; SSRI treatment: r = 0.39, P = .006; and CBT: r = 0.32, P = .003). Connections significantly contributing to the FC variate were distributed across the brain, but especially within the default mode network and the dorsal and ventral attention networks. Predictive performance decreased in models incorporating fewer neural systems or clinical outcome dimensions.

In this prognostic study assessing predictive models of 181 patients with IPs, whole-brain FC reliably predicted multidimensional treatment outcomes across diagnoses and treatment modalities. These results suggest an association between neural connectivity patterns within specific neural networks and clinical improvements induced by varying treatment modalities, thereby advancing efforts toward personalized treatment approaches in psychiatry.