Disentangling local functional connectivity and its variability as a biomarker for predicting dysfunction in patients with diffuse axonal injury

• Published in: Chinese journal of academic radiology Vol. 3; no. 2; pp. 115 - 123
• Main Authors: Zhou, Fuqing, Kuang, Hongmei, Li, Jian, Zhan, Jie, Gong, Honghan
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.06.2020
• Subjects: Imaging; Medicine; Medicine & Public Health; Original Article; Radiology; Diffuse axonal injury; Dynamic functional connectivity; Regional homogeneity
• ISSN: 2520-8985; 2520-8993
• DOI: 10.1007/s42058-020-00036-0

Purpose To investigates and better understand local functional connectivity and its variability, and explore their predictive values of patients with diffuse axonal injury (DAI). Methods We prospectively enrolled 24 DAI patients and well-matched healthy controls ( n  = 26) receiving resting-state functional magnetic resonance imaging. We first assessed static and dynamic regional homogeneity (ReHo); then, a k-means clustering algorithm were performed to cluster ReHo states; finally, the prediction of clinical scores of the altered static and dynamic ReHo values was conducted using a general linear model. Results Compared with healthy subjects, the DAI patients showed distributed alteration in static and dynamic ReHo in several regions, and their alteration commonly located in the left cerebellum posterior lobe ( P  < 0.01 and Gaussian random field correction at P  < 0.05). Importantly, a relationship was observed in disease-specific differences in static or dynamic ReHo and the clinical assessment: the static ReHo values of the left inferior parietal lobule predicted Glasgow Coma Scale scores ( ρ  = 0.462, P  = 0.010). Notably, the results suggested that DAI patients make different transitions to some states (states 1 and 2) compared to healthy controls, but these dynamic transitions were not predictive of clinical associations with ReHo variability. Conclusion Our findings suggested alterations in both static and dynamic ReHo (less temporal variability) in several regions in the DAI patients. The static ReHo values of the left inferior parietal lobule could be useful as a novel predictive indicator to develop objective neuromarker for future artificial intelligence analyses.