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Schizophr Bull. 2019 Mar 7;45(2):415-424. doi: 10.1093/schbul/sby039.

Data-Driven Analysis of Functional Connectivity Reveals a Potential Auditory Verbal Hallucination Network.

Author information

1
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
2
Department of Psychiatry, Yale School of Medicine, New Haven, CT.
3
Department of Neurosurgery, Yale School of Medicine, New Haven, CT.

Abstract

Schizophrenia is a severe global health problem, with over half of such patients experiencing auditory verbal hallucinations (AVHs). A better understanding of the neural correlates differentiating patients experiencing AVHs from patients not experiencing AVHs and healthy controls may identify targets that lead to better treatment strategies for AVHs. Employing 2 data-driven, voxel-based measure of functional connectivity, we studied 46 patients with schizophrenia or schizoaffective disorder (28 experiencing AVHs and 18 not experiencing AVHs). Twenty healthy controls matched for age, gender, ethnicity, education level, handedness, and estimated verbal intelligence were included for comparison. The intrinsic connectivity distribution (ICD) was used to model each voxel's connectivity to the rest of the brain using a Weibull distribution. To investigate lateralization of connectivity, we used cross-hemisphere ICD, a method that separates the contribution of each hemisphere to interrogate connectivity laterality. Patients with AVHs compared with patients without AVHs exhibited significantly decreased whole-brain connectivity in the medial prefrontal cortex and posterior cingulate cortex, less lateralized connectivity in left putamen, and more lateralized connectivity in left interior frontal gyrus. Correlations with Auditory Hallucination Rating Scale (AHRS) and post hoc seed connectivity analyses revealed significantly altered network connectivity. Using the results from all analyses comparing the patient groups and correlations with AHRS, we identified a potential AVH network, consisting of 25 nodes, showing substantial overlap with the default mode network and language processing networks. This network as a whole, instead of individual nodes, may represent actionable targets for interventions.

KEYWORDS:

computational psychiatry; default mode network; functional magnetic resonance imaging; language; schizophrenia

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