A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis

Abstract

Objective: Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker.

Methods: In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design.

Results: The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems.

Conclusions: This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Keywords: Biomarkers; Connectome; Neuroimaging; Schizophrenia Spectrum and Other Psychotic Disorders.

FIGURE 1.. Flowchart of the data processing pipeline^a

^aBy combining cross-paradigm connectivity and connectome-based predictive modeling, the predictive model was trained and tested in the discovery sample. The generalizability of the model was further examined in the validation sample.

FIGURE 2.. Trajectories of psychosis scores across treatment^a

^aThe slopes for both samples were highly significant. The black lines and red lines indicate individual trajectories and group trajectories, respectively.

FIGURE 3.. The connectome-based features and their prediction performance in the studied samples^a

^aPanel A shows the final features (five positive and nine negative) selected from the 100 repetitions of cross-validated connectome-based predictive modeling in the discovery sample. The positive and negative features are marked in red and blue, respectively. In panel B, the upper graphs show associations between the selected features and symptom changes in the discovery sample, and the lower graphs show median prediction performance (correlations between the predicted and observed slopes) in the discovery sample, which was significant with permutations. In panel C, the upper graphs show associations between the selected features and symptom changes in the validation sample, and the lower graphs show prediction performance in the validation sample, which remained significant with permutations. The green dashed lines in the histograms in panels B and C indicate the position of the observed slopes.