IMPORTANCEThere is urgent need to improve the limited prognostic accuracy of clinical instruments to predict psychosis onset in individuals at clinical high risk (CHR) for psychosis. As yet, no reliable biological marker has been established to delineate CHR individuals who will develop psychosis from those who will not.OBJECTIVES To investigate abnormalities in a graph-based gyrification connectome in the early stages of psychosis and to test the accuracy of this systems-based approach to predict a transition to psychosis among CHR individuals. MAIN OUTCOMES AND MEASURESGyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation, and small-worldness. Group differences in network measures were assessed using functional data analysis across a range of network densities. The extremely randomized trees algorithm with repeated 5-fold cross-validation was used to delineate ARMS-T individuals from ARMS-NT individuals. Permutation tests were conducted to assess the significance of classification performance measures. RESULTSThe 4 study groups comprised 161 participants with mean (SD) ages ranging from 24.0 (4.7) to 25.9 (5.7) years. Small-worldness was reduced in the ARMS-T and FEP groups and was associated with decreased integration and increased segregation in both groups (Hedges g range, 0.666-1.050). Using the connectome properties as features, a good classification performance was obtained (accuracy, 90.49%; balanced accuracy, 81.34%; positive predictive value, 84.47%; negative predictive value, 92.18%; sensitivity, 66.11%; specificity, 96.58%; and area under the curve, 88.30%).CONCLUSIONS AND RELEVANCE These findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. These results further suggest that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data to improve individual prediction of a transition to psychosis in CHR individuals.

Identifying robust markers for predicting the onset of psychosis has been a key challenge for early detection research. Persecutory delusions are core symptoms of psychosis, and social cognition is particularly impaired in first-episode psychosis patients and individuals at risk for developing psychosis. Here, we propose new avenues for translation provided by hierarchical Bayesian models of behaviour and neuroimaging data applied in the context of social learning to target persecutory delusions. As it comprises a mechanistic model embedded in neurophysiology, the findings of this approach may shed light onto inference and neurobiological causes of transition to psychosis.

Depressive symptoms in subjects at Clinical High Risk for Psychosis (CHR-P) or at first-episode psychosis (FEP) are often treated with antidepressants. Our cross-sectional study investigated whether brain morphology is altered by antidepressant medication. High-resolution T 1 -weighted structural MRI scans of 33 CHR-P and FEP subjects treated with antidepressants, 102 CHR-P and FEP individuals without antidepressant treatment and 55 controls, were automatically segmented using Freesurfer 6.0. Linear mixed-effects modelling was applied to assess the differences in subcortical volume, surface area and cortical thickness in treated, non-treated and healthy subjects, taking into account converted dosages of antidepressants. Increasing antidepressant dose was associated with larger volume of the pallidum and the putamen, and larger surface of the left inferior temporal gyrus. In a pilot subsample of separately studied subjects of known genomic risk loci, we found that in the right postcentral gyrus, the left paracentral lobule and the precentral gyrus antidepressant dose-associated surface increase depended on polygenic schizophrenia-related-risk score. As the reported regions are linked to the symptoms of psychosis, our findings reflect the possible beneficial effects of antidepressant treatment on an emerging psychosis.

Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie their effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), a novel technique that assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two randomised, placebo-controlled, double-blind, cross-over trials, in which 45 participants were administered 100 μg LSD and placebo in two resting-state fMRI sessions. We compared EC against whole-brain functional connectivity (FC) using classical statistics and machine learning methods. Multivariate analyses of EC parameters revealed predominantly stronger interregional connectivity and reduced self-inhibition under LSD compared to placebo, with the notable exception of weakened interregional connectivity and increased self-inhibition in occipital brain regions as well as subcortical regions. Together, these findings suggests that LSD perturbs the Excitation/Inhibition balance of the brain. Notably, whole-brain EC did not only provide additional mechanistic insight into the effects of LSD on the Excitation/Inhibition balance of the brain, but EC also correlated with global subjective effects of LSD and discriminated experimental conditions in a machine learning-based analysis with high accuracy (91.11%), highlighting the potential of using whole-brain EC to decode or predict subjective effects of LSD in the future.