Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity

Overall pattern: Copa and colleagues report that baseline FC in distinct RSNs predicted clinical response to psilocybin at different post‑treatment latencies. Visual networks predicted early symptom improvement, fronto‑parietal networks predicted early changes around two weeks, and salience network FC predicted sustained response at later follow‑up. Primary numerical results: For the primary visual network (PV) and extrastriate visual network (ESV) the classifiers attained high AUCs for early outcomes: PV at week 2 AUC = 0.93 ± 0.08 (p = 0.035) and PV at week 5 AUC = 0.95 ± 0.06 (p = 0.021); ESV at week 1 AUC = 0.90 ± 0.09 (p = 0.034), ESV at week 2 AUC = 0.92 ± 0.09 (p = 0.028), and ESV at week 3 AUC = 0.90 ± 0.09 (p = 0.04). The DMN and executive control (EXC) networks predicted response at week 2 (DMN AUC = 0.883 ± 0.10, p = 0.041; EXC AUC = 0.90 ± 0.09, p = 0.04). Long‑term outcome at week 24 was predicted by salience network FC (SAL AUC = 0.89 ± 0.10, p = 0.047). Feature anatomy and directionality: Feature importance analyses showed that responders tended to have greater fronto‑parietal FC relative to non‑responders. The visual network predictors were driven by long‑range occipital‑to‑frontal connections, strong interhemispheric occipital links and occipito‑temporal and occipito‑parietal connections (especially for the ESV). DMN‑related predictive features involved frontal and frontal‑occipital links (predominantly left frontal). EXC importance mirrored the visual networks' distribution. SAL features that predicted long‑term response were concentrated in within‑frontal, fronto‑limbic and insular‑limbic ROI pairs, mainly intrahemispheric. Generalizability and combined sample: Applying the classifier to the independent 22‑subject dataset yielded AUCs above chance for some RSNs (salience reached values near 0.7) but p‑values did not meet conventional significance thresholds, which the authors attribute to low sample sizes and between‑dataset heterogeneity. Analyses on the combined 38‑subject sample again identified significant predictive accuracy in a subset of networks (notably ESV and EXC) and predominantly for early post‑treatment weeks (for ESV at weeks 0, 1 and 3, and for EXC at week 0). Feature importance patterns overlapped substantially between the original and combined analyses.

Interpretation of findings: The authors interpret these results as evidence that baseline resting‑state FC contains signals predictive of response to psilocybin treatment for depression, with different networks predicting improvement at different latencies. Early improvements (within about five weeks) were most strongly predicted by FC of primary and extrastriate visual networks, characterised by increased long‑range connections between occipital and frontal regions as well as intra‑occipital coupling. Two‑week improvements were associated with higher baseline FC in DMN and executive control networks, while salience network FC predicted sustained improvement at 24 weeks. Relation to prior work and possible mechanisms: Copa and colleagues relate the visual network findings to prior reports that visual FC can predict response to other antidepressant treatments (for example electroconvulsive therapy) and to known sensory and visual alterations produced acutely by psychedelics. They suggest a plausible mediating pathway whereby baseline visual‑frontal coupling predisposes individuals to particular subjective acute experiences (e.g. mystical‑type or emotional breakthrough experiences) that have been linked with positive outcomes after psilocybin. DMN involvement is discussed in the context of rumination; altered DMN activity is implicated in depressive rumination and reductions in DMN connectivity during the acute psychedelic state may relate to therapeutic effects, although the timing here (prediction at two weeks) does not imply a direct acute DMN action. The salience network result is framed as potentially indexing network properties that relate to longer‑term clinical course and may overlap with mechanisms implicated in placebo responses. Limitations highlighted by the authors: The investigators emphasise several limitations. Sample sizes were small, reducing statistical power and limiting the robustness of generalizability tests; classifier p‑values did not survive correction for multiple comparisons in some analyses. Heterogeneity between datasets and lack of standardised acquisition procedures complicates cross‑dataset comparisons. Crucially, both datasets lacked a placebo control arm, so some predictive features could reflect expectancy or placebo effects rather than psilocybin‑specific mechanisms. Short scanning durations in both datasets are noted as an additional constraint. The authors also acknowledge practical limitations of neuroimaging as a predictive tool in real‑world settings, including susceptibility to artefacts, between‑scanner differences and cost. Implications and next steps: The study is presented as a proof‑of‑concept that baseline neurophysiological measures can help predict psilocybin treatment outcome. The authors recommend larger samples, inclusion of placebo or active control conditions, standardised imaging protocols and multimodal approaches combining neuroimaging, behavioural, subjective and genetic markers to improve prediction and to better disentangle placebo from drug‑specific effects. They also propose further investigation of whether baseline FC predicts the acute subjective effects of psychedelics and whether those acute effects mediate longer‑term antidepressant benefit.