Volume 14, Issue 3 (May & Jun 2023)
BCN 2023, 14(3): 397-410 |
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Keyvanfard F, Schmid A, Nasiraei-Moghaddam A. Functional Connectivity Alterations of Within and Between Networks in Schizophrenia: A Retrospective Study. BCN 2023; 14 (3) :397-410
URL: http://bcn.iums.ac.ir/article-1-2385-en.html
URL: http://bcn.iums.ac.ir/article-1-2385-en.html
1- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
2- Department of Health Sciences and Technology (D-HEST), ETH Zürich, Zürich, Switzerland.
3- Department of Biomedical Engineering, Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
2- Department of Health Sciences and Technology (D-HEST), ETH Zürich, Zürich, Switzerland.
3- Department of Biomedical Engineering, Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Abstract:
Introduction: Schizophrenia (SZ) is a chronic brain disorder characterized by diverse cognitive dysfunctions due to abnormal brain connectivity. Evaluating these connectivity alterations between and within such networks (intra- and inter-connectivity) may improve the understanding of disrupted information processing patterns in SZ patients.
Methods: Resting-state fMRI analysis was performed on 24 SZ patients and 27 matched healthy controls. A functional connectivity matrix was constructed for each participant based on 129 gray matter regions. All regions were classified into eight distinct functional networks. Afterward, all functional connections were segregated into inter- and intra-network connections considering the eight networks. The Mean values of connectivity weights and nodal strength were examined for within- and between-network connections in SZ patients and healthy controls.
Results: This analysis revealed that the within-network connections in the somatomotor (SM) network significantly reduced (P<0.001) in SZ patients. Additionally, intra-network connections within the visual and the ventral attention (VA) networks were significantly lower (P<0.01) in the SZ group. Moreover, disrupted intra-network connectivity was detected between the following network pairs: The visual-limbic, the somatomotor-limbic, the dorsal attention-limbic, and the ventral attention-dorsal attention system.
Conclusion: The results showed an extensive reduction in functional connectivity strength for SZ patients, with a particularly significant decrease in intra-network connections when compared to the inter-networks. These findings can impact the understanding of the important dysregulated connections that are implicated in the incidence of schizophrenia.
Methods: Resting-state fMRI analysis was performed on 24 SZ patients and 27 matched healthy controls. A functional connectivity matrix was constructed for each participant based on 129 gray matter regions. All regions were classified into eight distinct functional networks. Afterward, all functional connections were segregated into inter- and intra-network connections considering the eight networks. The Mean values of connectivity weights and nodal strength were examined for within- and between-network connections in SZ patients and healthy controls.
Results: This analysis revealed that the within-network connections in the somatomotor (SM) network significantly reduced (P<0.001) in SZ patients. Additionally, intra-network connections within the visual and the ventral attention (VA) networks were significantly lower (P<0.01) in the SZ group. Moreover, disrupted intra-network connectivity was detected between the following network pairs: The visual-limbic, the somatomotor-limbic, the dorsal attention-limbic, and the ventral attention-dorsal attention system.
Conclusion: The results showed an extensive reduction in functional connectivity strength for SZ patients, with a particularly significant decrease in intra-network connections when compared to the inter-networks. These findings can impact the understanding of the important dysregulated connections that are implicated in the incidence of schizophrenia.
Keywords: Schizophrenia, Cognitive dysfunction, fMRI, Resting state networks, Inter-network connectivity
Type of Study: Original |
Subject:
Clinical Neuroscience
Received: 2022/01/10 | Accepted: 2022/02/27 | Published: 2023/05/8
Received: 2022/01/10 | Accepted: 2022/02/27 | Published: 2023/05/8
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