Abnormal Brain Connectivity in Carpal Tunnel Syndrome Assessed by Graph Theory

Abstract

Introduction Numerous resting-state functional magnetic resonance imaging (fMRI) researches have indicated that large-scale functional and structural remodeling occurs in the whole brain despite an intact sensorimotor network after carpal tunnel syndrome (CTS). Investigators aimed to explore alterations of the global and nodal properties that occur in the whole brain network of patients with CTS based on topographic theory. Methods Standard-compliant fMRI data were collected from 27 patients with CTS in bilateral hands and 19 healthy control subjects in this cross-sectional study. The statistics based on brain networks were calculated the differences between the patients and the healthy. Several topological properties were computed, such as the small-worldness, nodal clustering coefficient, characteristic path length, and degree centrality. Results Compared to those of the healthy controls, the global properties of the CTS group exhibited a decreased characteristic path length. Changes in the local-level properties included a decreased nodal clustering coefficient in 6 separate brain regions and significantly different degree centrality in several brain regions that were related to sensorimotor function and pain. Discussion The study suggested that CTS reinforces global connections and makes their networks more random. The changed nodal properties were affiliated with basal ganglia-thalamo-cortical circuits and the pain matrix. These results provided new insights for improving our understanding of abnormal topological theory in relation to the functional brain networks of CTS patients. Perspective This article presents that the CTS patients’ brain with a higher global efficiency. And the significant alterations in several brain regions which are more related to pain and motor processes. The results provided effective complements to the neural mechanisms underlying CTS.

White matter structural and network topological changes in moyamoya disease with limb paresthesia: A study based on diffusion kurtosis imaging

“…When hemodynamics are improved after revascularization, the microstructure of brain tissue may not easily show changes within a short period of time. On the other hand, the plasticity of brain function can help patients regain functionality neuro-elastically by rerouting functional pathways elsewhere to compensate for permanently damaged neuropathways ( Klein et al, 2018 ; Wang et al, 2020 ; Xing et al, 2021 ). A longer-term follow-up period could facilitate the observation of changes in DKI parameters after an improvement in cerebral hemodynamics and validate our hypothesis.…”

Brain Structural Changes in Carpal Tunnel Syndrome Patients: From the Perspectives of Structural Connectivity and Structural Covariance Network

“…The present study demonstrated changed brain structure from perspectives of structural connectivity and SCN for a group of subjects, providing a supplement to CTS-related brain changes. [7][8][9][10][33][34][35][36] According to definition and clinical implications of these measures, the efficiency of information exchange altered at regional level. [37][38][39][40] Specifically, decreased efficiency was found in pain neuromatrix, including thalamus and insula, and increased efficiency in nonpain-related brain regions including MTG and LOcC among CTS patients.…”

Modulation of Brain Network Topological Properties in Knee Osteoarthritis by Electroacupuncture in Rats

“…All fMRI data were collected by a 7-T horizontal-bore Bruker scanner (Bruker Corporation, Ettlingen, Germany) with a gradient system of 116 mm inner diameter and 400 mT/m maximum gradient strength. 26 , 27 Rats were anesthetized initially with 4% isoflurane and then fixed on the scanner and maintained with 1.5–2% isoflurane in oxygen-enriched air (20% oxygen/80% air) with necessary ventilator support. fMRI images were acquired using a Gradient Echo-Echo Planar Imaging (GRE-EPI) sequence with the following parameters: interleaved scanning order; Field of view = 32 × 32 mm 2 ; matrix size = 64 × 64; slice number = 43; Time of repetition/echo = 3000/20 ms; flip angle = 90°; slice thickness = 0.5 mm;…”