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Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study

BACKGROUND: Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain. METHODS: We examined the functional degree distribution o...

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Autores principales: Wang, Feiwen, Xi, Chang, Liu, Zhening, Deng, Mengjie, Zhang, Wen, Cao, Hengyi, Yang, Jie, Palaniyappan, Lena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: CMA Impact Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524478/
https://www.ncbi.nlm.nih.gov/pubmed/36167413
http://dx.doi.org/10.1503/jpn.220053
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author Wang, Feiwen
Xi, Chang
Liu, Zhening
Deng, Mengjie
Zhang, Wen
Cao, Hengyi
Yang, Jie
Palaniyappan, Lena
author_facet Wang, Feiwen
Xi, Chang
Liu, Zhening
Deng, Mengjie
Zhang, Wen
Cao, Hengyi
Yang, Jie
Palaniyappan, Lena
author_sort Wang, Feiwen
collection PubMed
description BACKGROUND: Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain. METHODS: We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set. RESULTS: Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the “2-back versus 0-back” contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%. LIMITATIONS: We used a limited n-back paradigm that precluded the examination of higher working-memory loads. CONCLUSION: Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia.
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spelling pubmed-95244782022-10-14 Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study Wang, Feiwen Xi, Chang Liu, Zhening Deng, Mengjie Zhang, Wen Cao, Hengyi Yang, Jie Palaniyappan, Lena J Psychiatry Neurosci Research Paper BACKGROUND: Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain. METHODS: We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set. RESULTS: Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the “2-back versus 0-back” contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%. LIMITATIONS: We used a limited n-back paradigm that precluded the examination of higher working-memory loads. CONCLUSION: Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia. CMA Impact Inc. 2022-09-27 /pmc/articles/PMC9524478/ /pubmed/36167413 http://dx.doi.org/10.1503/jpn.220053 Text en © 2022 CMA Impact Inc. or its licensors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY-NC-ND 4.0) licence, which permits use, distribution and reproduction in any medium, provided that the original publication is properly cited, the use is noncommercial (i.e., research or educational use), and no modifications or adaptations are made. See: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Research Paper
Wang, Feiwen
Xi, Chang
Liu, Zhening
Deng, Mengjie
Zhang, Wen
Cao, Hengyi
Yang, Jie
Palaniyappan, Lena
Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
title Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
title_full Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
title_fullStr Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
title_full_unstemmed Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
title_short Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
title_sort load-dependent inverted u–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional mri study
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524478/
https://www.ncbi.nlm.nih.gov/pubmed/36167413
http://dx.doi.org/10.1503/jpn.220053
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