doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one’s intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.

Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient response to treatment. The approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.

IMPORTANCE: Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. OBJECTIVE: To characterize individualized brain functional connectivity (FC) and determine whether it can define signatures of antidepressant and placebo treatment in MDD. DESIGN, SETTING, AND PARTICIPANTS: The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Recruitment started from July 29, 2011, to December 15, 2015. A sample of 296 subjects was randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) at 4 clinical sites. Individualized FC was quantified by removing the common components from the raw baseline FC to train regression-based connectivity predictive models. The data analysis was performed from January 7, 2022 to August 24, 2022. INTERVENTIONS: The MDD patients received either antidepressant sertraline or placebo for 8 weeks. MAIN OUTCOMES AND MEASURES: Treatment response was measured as pre- minus post-treatment change in total score of the 17-item Hamilton Depression Rating Scale (HAMD17). RESULTS: With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by the 10\times10-fold cross-validation (Wilcoxon signed-rank test result of R2 difference; sertraline: windividualized vs raw = 2.57, pindividualized vs raw = 0.014; placebo: windividualized vs raw = 3.02, pindividualized vs raw = 0.006). For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex (MTC) and right insula. Lower FC between the right temporal pole and right insula predicted a better response to sertraline. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex (STC). Lower FC between the right anterior cingulate cortex and left posterior cingulate cortex (PCC) indicated a better placebo response. The right prefrontal lobe was critical for predicting responses to both treatment arms. CONCLUSIONS AND RELEVANCE: Our findings demonstrated that individualization of FC metrics through removal of common FC components enhanced the prediction performance compared to the raw FC. The proposed individualized FC predictive modeling framework was highly adaptable to precise diagnosis and prognosis of other mental disorders. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment. Clinical Trial Registration ID #: NCT01407094.