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Brain Science Advances 2023, 9(2): 78-94 https://doi.org/10.26599/BSA.2023.9050007
Research Article | Open Access | Issue | Published: 05 June 2023

The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

Show Author's Information Yan Niu1,  Nan Zhang2,  Mengni Zhou3,  Lan Yang1,  Jie Sun1,  Xueting Cheng1,  Yanan Li1,  Lefan Guo1,  Jie Xiang1,  Bin Wang1( )
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan
Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangzhou, China
Keywords:
functional entropy, network complexity, schizophrenia, bipolar disorder, resting-state fMRI
Cite this article:
Niu Y, Zhang N, Zhou M, et al. The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients. Brain Science Advances, 2023, 9(2): 78-94. https://doi.org/10.26599/BSA.2023.9050007
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Abstract Full text About this article

Schizophrenia (SZ) and bipolar disorder (BD) are two of the most frequent mental disorders. These disorders exhibit similar psychotic symptoms, making diagnosis challenging and leading to misdiagnosis. Yet, the network complexity changes driving spontaneous brain activity in SZ and BD patients are still unknown. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE was utilized in this study to examine the network complexity of the resting-state fMRI data of SZ and BD patients at three levels, including global, modules, and nodes. At three levels, the FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intra-module level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. Finally, we classified using the FE features and attained an accuracy of 66.7% (BD vs. SZ vs. NC) and an accuracy of 75.0% (SZ vs. BD). These findings proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness.


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About this article

The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

Show Author's information Yan Niu1, Nan Zhang2, Mengni Zhou3, Lan Yang1, Jie Sun1, Xueting Cheng1, Yanan Li1, Lefan Guo1, Jie Xiang1, Bin Wang1( )
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan
Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangzhou, China

Abstract

Schizophrenia (SZ) and bipolar disorder (BD) are two of the most frequent mental disorders. These disorders exhibit similar psychotic symptoms, making diagnosis challenging and leading to misdiagnosis. Yet, the network complexity changes driving spontaneous brain activity in SZ and BD patients are still unknown. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE was utilized in this study to examine the network complexity of the resting-state fMRI data of SZ and BD patients at three levels, including global, modules, and nodes. At three levels, the FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intra-module level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. Finally, we classified using the FE features and attained an accuracy of 66.7% (BD vs. SZ vs. NC) and an accuracy of 75.0% (SZ vs. BD). These findings proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness.

Keywords: functional entropy, network complexity, schizophrenia, bipolar disorder, resting-state fMRI

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Publication history

Received: 28 November 2022
Revised: 09 February 2023
Accepted: 01 March 2023
Published: 05 June 2023
Issue date: June 2023

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© The authors 2023.

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