Verbal working memory and functional large-scale networks in schizophrenia

TY - JOUR

T1 - Verbal working memory and functional large-scale networks in schizophrenia

AU - Dauvermann, Maria R

AU - Moorhead, Thomas Wj

AU - Watson, Andrew R

AU - Duff, Barbara

AU - Romaniuk, Liana

AU - Hall, Jeremy

AU - Roberts, Neil

AU - Lee, Graham L

AU - Hughes, Zoë A

AU - Brandon, Nicholas J

AU - Whitcher, Brandon

AU - Blackwood, Douglas Hr

AU - McIntosh, Andrew M

AU - Lawrie, Stephen M

N1 - Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

PY - 2017/12/30

Y1 - 2017/12/30

N2 - The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia.

AB - The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia.

KW - Adult

KW - Bayes Theorem

KW - Case-Control Studies

KW - Female

KW - Functional Neuroimaging

KW - Humans

KW - Magnetic Resonance Imaging

KW - Male

KW - Memory, Short-Term

KW - Middle Aged

KW - Neural Pathways/diagnostic imaging

KW - Nonlinear Dynamics

KW - Prefrontal Cortex/diagnostic imaging

KW - Schizophrenia/diagnostic imaging

KW - Schizophrenic Psychology

KW - Substantia Nigra/diagnostic imaging

KW - Ventral Tegmental Area/diagnostic imaging

KW - Young Adult

U2 - 10.1016/j.pscychresns.2017.10.004

DO - 10.1016/j.pscychresns.2017.10.004

M3 - Article

C2 - 29111478

SN - 0925-4927

VL - 270

SP - 86

EP - 96

JO - Psychiatry Research. Neuroimaging

JF - Psychiatry Research. Neuroimaging

ER -