Tag: brain network

Altered Effective Connectivity of Resting-State Networks by Tai Chi Chuan in Chronic Fatigue Syndrome Patients: A Multivariate Granger Causality Study

Abstract:

Numerous evidence has shown that patients with chronic fatigue syndrome (CFS) have changes in resting brain functional connectivity, but there is no study on the brain network effect of Tai Chi Chuan intervention in CFS. To explore the influence of Tai Chi Chuan exercise on the causal relationship between brain functional networks in patients with CFS, 21 patients with CFS and 19 healthy controls were recruited for resting-state functional magnetic resonance imaging (rs-fMRI) scanning and 36-item Short-Form Health Survey (SF-36) scale assessment before and after 1month-long training in Tai Chi Chuan.

We extracted the resting brain networks using the independent component analysis (ICA) method, analyzed the changes of FC in these networks, conducted Granger causality analysis (GCA) on it, and analyzed the correlation between the difference causality value and the SF-36 scale. Compared to the healthy control group, the SF-36 scale scores of patients with CFS were lower at baseline. Meanwhile, the causal relationship between sensorimotor network (SMN) and default mode network (DMN) was weakened. ‘

The above abnormalities could be improved by Tai Chi Chuan training for 1 month. In addition, the correlation analyses showed that the causal relationship between SMN and DMN was positively correlated with the scores of Role Physical (RP) and Bodily Pain (BP) in CFS patients, and the change of causal relationship between SMN and DMN before and after training was positively correlated with the change of BP score.

The findings suggest that Tai Chi Chuan is helpful to improve the quality of life for patients with CFS. The change of Granger causality between SMN and DMN may be a readout parameter of CFS. Tai Chi Chuan may promote the functional plasticity of brain networks in patients with CFS by regulating the information transmission between them.

Source: Li Y, Wu K, Hu X, Xu T, Li Z, Zhang Y, Li K. Altered Effective Connectivity of Resting-State Networks by Tai Chi Chuan in Chronic Fatigue Syndrome Patients: A Multivariate Granger Causality Study. Front Neurol. 2022 Jun 3;13:858833. doi: 10.3389/fneur.2022.858833. PMID: 35720086; PMCID: PMC9203735. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203735/ (Full text)

Altered Structural Brain Networks Related to Adrenergic/Muscarinic Receptor Autoantibodies in Chronic Fatigue Syndrome

Abstract:

Background and purpose: Recent studies suggest that the autoantibodies against adrenergic/muscarinic receptors might be one of the causes and potential markers of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The purpose of this study was to investigate the structural network changes related to autoantibody titers against adrenergic/muscarinic receptors in ME/CFS by performing a single-subject gray matter similarity-based structural network analysis.

Methods: We prospectively examined 89 consecutive right-handed ME/CFS patients who underwent both brain MRI including 3D T1-wighted images and a blood analysis of autoantibodies titers against β1 adrenergic receptor (β1 AdR-Ab), β2 AdR-Ab, M3 acetylcholine receptor (M3 AchR-Ab), and M4 AchR-Ab. Single-subject gray matter similarity-based structural networks were extracted from segmented gray matter images for each patient. We calculated local network properties (betweenness centrality, clustering coefficient, and characteristic path length) and global network properties (normalized path length λ, normalized clustering coefficient γ, and small-world network value δ). We investigated the correlations between the autoantibody titers and regional gray matter/white matter volumes, the local network properties, and the global network properties.

Results: Betweenness centrality showed a significant positive correlation with β1-AdR-Ab in the right dorsolateral prefrontal cortex. The characteristic path length showed a significant negative correlation with β2-AdR-Ab in the right precentral gyrus. There were no significant correlations between the antibody titers and the regional gray matter/white matter volumes, and the global network properties.

Conclusions: Our findings suggest that β1 AdR-Ab and β2 AdR-Ab are potential markers of ME/CFS.

Source: Fujii H, Sato W, Kimura Y, et al. Altered Structural Brain Networks Related to Adrenergic/Muscarinic Receptor Autoantibodies in Chronic Fatigue Syndrome [published online ahead of print, 2020 Jul 1]. J Neuroimaging. 2020;10.1111/jon.12751. doi:10.1111/jon.12751 https://pubmed.ncbi.nlm.nih.gov/32609410/

Decreased connectivity and increased BOLD complexity in the default mode network in individuals with chronic fatigue syndrome

Abstract:

The chronic fatigue syndrome / myalgic encephalomyelitis (CFS) is a debilitating disease with unknown pathophysiology and no diagnostic test. This study investigated the default mode network (DMN) in order to understand the pathophysiology of CFS and to identify potential biomarkers.

Using functional MRI (fMRI) collected from 72 subjects (45 CFS and 27 controls) with a temporal resolution of 0.798s, we evaluated the default mode network using static functional connectivity (FC), dynamic functional connectivity (DFC) and DFC complexity, blood oxygenation level dependent (BOLD) activation maps and complexity of activity. General linear model (GLM) univariate analysis was used for inter group comparison to account for age and gender differences. Hierarchical regression analysis was used to test whether fMRI measures could be used to explain variances of health scores.

BOLD signals in the posterior cingulate cortex (PCC), the driving hub in the DMN, were more complex in CFS in both resting state and task (P < 0.05). The FCs between medial prefrontal cortex (mPFC) and both inferior parietal lobules (IPLs) were weaker (P < 0.05) during resting state, while during task mPFC – left IPL and mPFC – PCC were weaker (P < 0.05). The DFCs between the DMN hubs were more complex in CFS (P < 0.05) during task. Each of these differences accounted for 7 – 11% variability of health scores. This study showed that DMN activity is more complex and less coordinated in CFS, suggesting brain network analysis could be potential used as a diagnostic biomarker for CFS.

Source: Shan ZY, Finegan K, Bhuta S, Ireland T, Staines DR, Marshall-Gradisnik SM, Barnden LR. Decreased connectivity and increased BOLD complexity in the default mode network in individuals with chronic fatigue syndrome. Brain Connect. 2017 Nov 20. doi: 10.1089/brain.2017.0549. [Epub ahead of print] https://www.ncbi.nlm.nih.gov/pubmed/29152994