Large scale brain network mental workload engagement in schizophrenia
Date
2022
Authors
Duffy, John R., author
Thomas, Michael L., advisor
Rojas, Don, committee member
Blanchard, Nathanial, committee member
Tompkins, Sara Anne, committee member
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Abstract
Objective: Cognitive deficits in patients diagnosed with schizophrenia are a core feature of the disorder. There are currently no treatments for these cognitive deficits. Our aim is to examine and compare patterns of increased versus decreased activity in the central executive network (CEN), salience network (SN), and default mode network (DMN) between healthy controls (HC) and patients diagnosed with schizophrenia (SZ) as well as to explore the influence of task load on these networks between HC and SZ. Method: Analyses focused on a secondary dataset comprising Blood Oxygen-Level Dependent (BOLD) data collected from 25 HC and 27 SZ who completed a working memory (WM) task (N-back) with 5 load conditions while undergoing functional magnetic resonance imaging (fMRI). Region of interest (ROI) data were analyzed using linear mixed-effects models. Dynamic causal modeling (DCM) was used in an exploratory analysis to examine working memory load input to these networks. Results: Group activation differences were found in the posterior salience network (pSN), default mode network (DMN), dorsal default mode network (dDMN), and ventral default mode network (vDMN) showing greater activity for SZ. Specifically, pSN, SMN, dDMN, and vDMN all showed increased activity in SZ compared to HC. The curve of brain activity was consistent between HC and SZ with the exception of the vDMN, where HC show greater activation at modest mental workload (quadratic curve) and SZ showed greater brain activation at lower mental workload (linear). In the CEN, there were no group differences, and the response curve was the same for both groups. In DCM analysis, working memory load acted as an input on different networks between HC and SZ. Conclusions: These group differences demonstrate network difference between HC and SZ and could show value in treatments targeting cognitive deficits in SZ from a large-scale brain network connectivity perspective. Future studies are needed to confirm these results with higher sample size in order to examine potential subtleties of interactions between these networks.
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Subject
dynamic causal modeling
psychology
brain network
schizophrenia
neuroscience