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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.
Cognitive remediation (CR) training has emerged as a promising approach to improving cognitive deficits in schizophrenia and related psychosis. The limited availability of psychological services for psychosis is a major barrier to accessing this intervention however. This study investigated the effectiveness of a low support, remotely accessible, computerised working memory (WM) training programme in patients with psychosis.
Ninety patients were enrolled into a single blind randomised controlled trial of CR. Effectiveness of the intervention was assessed in terms of neuropsychological performance, social and occupational function, and functional MRI 2 weeks post-intervention, with neuropsychological and social function again assessed 3–6 months post-treatment.
Patients who completed the intervention showed significant gains in both neuropsychological function (measured using both untrained WM and episodic task performance, and a measure of performance IQ), and social function at both 2-week follow-up and 3–6-month follow-up timepoints. Furthermore, patients who completed MRI scanning showed improved resting state functional connectivity relative to patients in the placebo condition.
CR training has already been shown to improve cognitive and social function in patient with psychosis. This study demonstrates that, at least for some chronic but stable outpatients, a low support treatment was associated with gains that were comparable with those reported for CR delivered entirely on a 1:1 basis. We conclude that CR has potential to be delivered even in services in which psychological supports for patients with psychosis are limited.
Social environmental stress, including childhood abuse and deprivation, is associated with increased rates of psychiatric disorders such as schizophrenia and depression. However, the neural mechanisms mediating risk are not completely understood. Functional magnetic resonance imaging (MRI) studies have reported effects of social environmental stress on a variety of brain regions, but interpretation of results is complicated by the variety of environmental risk factors examined and different methods employed.
We examined brain regions consistently showing differences in blood oxygen level-dependent (BOLD) response in individuals exposed to higher levels of environmental stress by performing a coordinate-based meta-analysis on 54 functional MRI studies using activation likelihood estimation (ALE), including an overall sample of 3044 participants. We performed separate ALE analyses on studies examining adults (mean age ⩾18 years) and children/adolescents (mean age <18 years) and a contrast analysis comparing the two types of study.
Across both adult and children/adolescent studies, ALE meta-analysis revealed several clusters in which differences in BOLD response were associated with social environmental stress across multiple studies. These clusters incorporated several brain regions, among which the right amygdala was most frequently implicated.
These findings suggest that a variety of social environmental stressors is associated with differences in the BOLD response of specific brain regions such as the right amygdala in both children/adolescents and adults. What remains unknown is whether these environmental stressors have differential effects on treatment response in these brain regions.
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