Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Chang, Zhao; Wang, Xinrui; Wu, Ying; Lin, Pan; Wang, Rong

doi:10.1002/hbm.26087

Cited by 17 publications

(9 citation statements)

References 94 publications

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“…Compared to classical graph theory, the NSP-based method had superior performance in predicting SANS/SAPS scores and detecting network alterations, reflecting the advantages of our method based on hierarchical modules in brain FC networks. This result is highly consistent with a series of our works wherein the NSP-based method is more powerful in linking the brain to diverse cognitive abilities 32 , task performance 31 , stress conditions 67 , ADHD symptoms 33 , and bipolar disorder symptoms 68 . All these findings demonstrated that the NSP-based features detected across multiple levels are promising biomarkers for schizophrenia and other brain disorders.…”

Section: Discussionsupporting

confidence: 90%

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

2023

Self Cite

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Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.

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Section: Discussionsupporting

confidence: 90%

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

2023

Self Cite

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“…Compared to classical graph theory, the NSP-based method had superior performance in predicting SANS and SAPS scores, reflecting the advantages of our method based on hierarchical modules in brain FC networks. This result is highly consistent with a series of our works wherein the NSP-based method is more powerful in linking the brain to diverse cognitive abilities 32 , task performance 31 , stress conditions 78 , ADHD symptoms 33 and bipolar disorder symptoms 79 . All these findings demonstrated that the NSP-based features detected across multiple levels are promising biomarkers for schizophrenia and other brain disorders.…”

Section: Biomarkers For Positive and Negative Symptomssupporting

confidence: 90%

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Wang¹,

Zhao²,

Wang

2022

Preprint

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Schizophrenia is a severe psychotic disorder clinically characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional connectivity networks in schizophrenia patients and healthy controls, and we studied the dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable global integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Attention systems were associated with hallucinations, and default-mode network (DMN) and control systems were related to avolition. In a machine-learning model, NSP-based features outperformed classical graph measures at predicting positive and negative symptom scores. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms on brain networks was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.

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“…This reduction implies abnormal information transfer within or between functional networks, ultimately suggesting abnormal local segregation and global integration of functional brain networks. And this finding has also been observed in many neurodegenerative and psychiatric disorders 46 – 48 . The connectivity pattern of state 4 is characterized by a modular connection, primarily evidenced by a clear positive connection between DMN and VN, while a distinct negative connection exists between DMN and AN.…”

Section: Discussionsupporting

confidence: 67%

Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients

Wang,

Shu,

Cai

et al. 2024

Sci Rep

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To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

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Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Cited by 17 publications

References 94 publications

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients

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