Previous studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient.

Here we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology.

Connectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions.

These results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the ‘dysconnectivity hypothesis’ of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.

Cannabis and its main psychoactive ingredient δ-9-tetrahydrocannibidiol (THC) can induce transient psychotic symptoms in healthy individuals and exacerbate them in those with established psychosis. However, not everyone experience these effects, suggesting that certain individuals are particularly susceptible. The neural basis of this sensitivity to the psychotomimetic effects of THC is unclear.

We investigated whether individuals who are sensitive to the psychotomimetic effects of THC (TP) under experimental conditions would show differential hippocampal activation compared with those who are not (NP). We studied 36 healthy males under identical conditions under the influence of placebo or THC (10 mg) given orally, on two separate occasions, in a pseudo-randomized, double-blind, repeated measures, within-subject, cross-over design, using psychopathological assessments and functional MRI while they performed a verbal learning task. They were classified into those who experienced transient psychotic symptoms (TP; n = 14) following THC administration and those who did not (NP; n = 22).

Under placebo conditions, there was significantly greater engagement of the left hippocampus (p < 0.001) in the TP group compared with the NP group during verbal encoding, which survived leave-one-out analysis. The level of hippocampal activation was directly correlated (Spearman's ρ = 0.44, p = 0.008) with the severity of transient psychotic symptoms induced by THC. This difference was not present when we compared two subgroups from the same sample that were defined by sensitivity to anxiogenic effects of THC.

These results suggest that altered hippocampal activation during verbal encoding may serve as a marker of sensitivity to the acute psychotomimetic effects of THC.

Diffusion tensor magnetic resonance imaging studies in schizophrenia to date have been largely inconsistent. This may reflect variation in methodology, and the use of small samples with differing illness duration and medication exposure.

To determine the extent and location of white matter microstructural changes in schizophrenia, using optimised diffusion tensor imaging in a large patient sample, and to consider the effects of illness duration and medication exposure.

Scans from 76 patients with schizophrenia and 76 matched controls were used to compare fractional anisotropy, a measure of white matter microstructural integrity, between the groups.

We found widespread clusters of reduced fractional anisotropy in patients, affecting most major white matter tracts. These reductions did not correlate with illness duration, and there was no difference between age-matched chronically and briefly medicated patients.

The finding of widespread fractional anisotropy reductions in our larger sample of patients with schizophrenia may explain some of the inconsistent findings of previous, smaller studies.

People with prodromal symptoms have a very high risk of developing psychosis.

To use functional magnetic resonance imaging to examine the neurocognitive basis of this vulnerability.

Cross-sectional comparison of regional activation in individuals with an ‘at-risk mental state’ (at-risk group: n=17), patients with first-episode schizophreniform psychosis (psychosis group: n=10) and healthy volunteers (controls: n=15) during an overt verbal fluency task and an N-back working memory task.

A similar pattern of between-group differences in activation was evident across both tasks. Activation in the at-risk group was intermediate relative to that in controls and the psychosis group in the inferior frontal and anterior cingulate cortex during the verbal fluency task and in the inferior frontal, dorsolateral prefrontal and parietal cortex during the N-back task.

The at-risk mental state is associated with abnormalities of regional brain function that are qualitatively similar to, but less severe than, those in patients who have recently presented with psychosis.