AUTHOR=Jia Yingxin , Jariwala Namasvi , Hinkley Leighton B. N. , Nagarajan Srikantan , Subramaniam Karuna TITLE=Abnormal resting-state functional connectivity underlies cognitive and clinical symptoms in patients with schizophrenia JOURNAL=Frontiers in Human Neuroscience VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1077923 DOI=10.3389/fnhum.2023.1077923 ISSN=1662-5161 ABSTRACT=Introduction

The cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity.

Methods

We capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC).

Results

We found that SZ showed significant global disrupted functional connectivity in delta-theta (2–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition.

Discussion

The multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.