Verbal working memory and functional large-scale networks in schizophrenia

Maria R. Dauvermann; Thomas WJ Moorhead; Andrew R. Watson; Barbara Duff; Liana Romaniuk; Jeremy Hall; Neil Roberts; Graham L. Lee; Zoë A. Hughes; Nicholas J. Brandon; Brandon Whitcher; Douglas HR Blackwood; Andrew M. McIntosh; Stephen M. Lawrie

doi:10.1016/j.pscychresns.2017.10.004

Verbal working memory and functional large-scale networks in schizophrenia

Maria R. Dauvermann^*, Thomas WJ Moorhead, Andrew R. Watson, Barbara Duff, Liana Romaniuk, Jeremy Hall, Neil Roberts, Graham L. Lee, Zoë A. Hughes, Nicholas J. Brandon, Brandon Whitcher, Douglas HR Blackwood, Andrew M. McIntosh, Stephen M. Lawrie

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

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.

Original language	English
Pages (from-to)	86-96
Number of pages	11
Journal	Psychiatry Research - Neuroimaging
Volume	270
Early online date	23 Oct 2017
DOIs	https://doi.org/10.1016/j.pscychresns.2017.10.004
Publication status	Published - 30 Dec 2017

Bibliographical note

Funding Information:
MD and TM were supported by Dr. Mortimer and Theresa Sackler Foundation throughout the project. No conflicts of interest are declared.

Funding Information:
This work was supported by an award from the Translational Medicine Research Collaboration (NS-EU-166) – a consortium made up of the Universities of Aberdeen, Dundee, Edinburgh and Glasgow, the four associated NHS Health Boards (Grampian, Tayside, Lothian and Greater Glasgow & Clyde), Scottish Enterprise and Pfizer. Pfizer has been involved in the study design of the study, writing of the report and in the decision to submit the article for publication. The investigators also acknowledge the support of National Health Service Research Scotland, through the Scottish Mental Health Research Network ( www.smhrn.org.uk ), who provided assistance with subject recruitment and cognitive assessments. Imaging aspects also received financial support from the Dr. Mortimer and Theresa Sackler Foundation.

Funding Information:
TM, AW and BD received funding from Pfizer through the study. No conflicts of interest are declared.

Publisher Copyright:
© 2017 The Authors

Keywords

Functional large-scale networks
Functional Magnetic Resonance Imaging
Nonlinear Dynamic Causal Modeling
Schizophrenia
Working memory

ASJC Scopus subject areas

Neuroscience (miscellaneous)
Radiology Nuclear Medicine and imaging
Psychiatry and Mental health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.pscychresns.2017.10.004Licence: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)

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Cite this

Dauvermann, M. R., Moorhead, T. WJ., Watson, A. R., Duff, B., Romaniuk, L., Hall, J., Roberts, N., Lee, G. L., Hughes, Z. A., Brandon, N. J., Whitcher, B., Blackwood, D. HR., McIntosh, A. M., & Lawrie, S. M. (2017). Verbal working memory and functional large-scale networks in schizophrenia. Psychiatry Research - Neuroimaging, 270, 86-96. Advance online publication. https://doi.org/10.1016/j.pscychresns.2017.10.004

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abstract = "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.",

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note = "Funding Information: MD and TM were supported by Dr. Mortimer and Theresa Sackler Foundation throughout the project. No conflicts of interest are declared. Funding Information: This work was supported by an award from the Translational Medicine Research Collaboration (NS-EU-166) – a consortium made up of the Universities of Aberdeen, Dundee, Edinburgh and Glasgow, the four associated NHS Health Boards (Grampian, Tayside, Lothian and Greater Glasgow & Clyde), Scottish Enterprise and Pfizer. Pfizer has been involved in the study design of the study, writing of the report and in the decision to submit the article for publication. The investigators also acknowledge the support of National Health Service Research Scotland, through the Scottish Mental Health Research Network ( www.smhrn.org.uk ), who provided assistance with subject recruitment and cognitive assessments. Imaging aspects also received financial support from the Dr. Mortimer and Theresa Sackler Foundation. Funding Information: TM, AW and BD received funding from Pfizer through the study. No conflicts of interest are declared. Publisher Copyright: {\textcopyright} 2017 The Authors",

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AU - Moorhead, Thomas WJ

AU - Watson, Andrew R.

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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 - Funding Information: MD and TM were supported by Dr. Mortimer and Theresa Sackler Foundation throughout the project. No conflicts of interest are declared. Funding Information: This work was supported by an award from the Translational Medicine Research Collaboration (NS-EU-166) – a consortium made up of the Universities of Aberdeen, Dundee, Edinburgh and Glasgow, the four associated NHS Health Boards (Grampian, Tayside, Lothian and Greater Glasgow & Clyde), Scottish Enterprise and Pfizer. Pfizer has been involved in the study design of the study, writing of the report and in the decision to submit the article for publication. The investigators also acknowledge the support of National Health Service Research Scotland, through the Scottish Mental Health Research Network ( www.smhrn.org.uk ), who provided assistance with subject recruitment and cognitive assessments. Imaging aspects also received financial support from the Dr. Mortimer and Theresa Sackler Foundation. Funding Information: TM, AW and BD received funding from Pfizer through the study. No conflicts of interest are declared. Publisher Copyright: © 2017 The Authors

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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.

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Verbal working memory and functional large-scale networks in schizophrenia

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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