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Dopamine-Induced Dysconnectivity Between Salience Network and Auditory Cortex in Subjects With Psychotic-like Experiences: A Randomized Double-Blind Placebo-Controlled Study.

ABSTRACT: Dopamine is involved in the pathophysiology of schizophrenia. Disrupted salience processing by the salience network (SN) may be a central link between dysregulated dopamine function and psychotic symptoms. However, dopaminergic influence on the SN and its presumed influence on psychotic and subpsychotic symptoms or psychotic-like experiences in healthy individuals remain unclear. Therefore, we investigated dopamine-induced changes in functional connectivity of the right anterior insula (rAI), a central SN hub, and their association with psychotic-like experiences. We enrolled 54 healthy, right-handed male subjects in a randomized, double-blind, cross-sectional placebo-controlled experiment. Psychotic-like experiences were assessed using the revised Exceptional Experiences Questionnaire (PAGE-R). They then received either placebo (n = 32) or 200 mg L-DOPA (n = 33), a dopamine precursor, orally and underwent resting-state functional magnetic resonance imaging. In a seed-to-voxel approach, we analyzed dopamine-induced changes in functional connectivity of the rAI and assessed the relationship between functional connectivity changes and PAGE-R score. L-DOPA reduced functional connectivity between the rAI and the left auditory cortex planum polare. In the placebo group, we found a strong negative correlation between PAGE-R score and rAI to planum polare functional connectivity; in the L-DOPA group, there was a strong positive correlation between PAGE-R score and functional connectivity between rAI and planum polare. The PAGE-R score explained about 30% of the functional connectivity variation between rAI and planum polare in the two groups. Our findings suggest that psychotic-like experiences are associated with dopamine-induced disruption of auditory input to the SN, which may lead to aberrant attribution of salience.

SUBMITTER: Rossler J

PROVIDER: S-EPMC7147573 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundProgress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.MethodsWe leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.ResultsAlthough we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.ConclusionsConnectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Dataset Information

Dopamine-Induced Dysconnectivity Between Salience Network and Auditory Cortex in Subjects With Psychotic-like Experiences: A Randomized Double-Blind Placebo-Controlled Study.

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