AUTHOR=Shi Shanshan , Qie Shuyan , Wang Hujun , Wang Jie , Liu Tiejun 

TITLE=Recombination of the right cerebral cortex in patients with left side USN after stroke: fNIRS evidence from resting state

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1178087

DOI=10.3389/fneur.2023.1178087

ISSN=1664-2295

ABSTRACT=Objective: Unilateral spatial neglect (USN) is impaired contralesional stimulus detection, response, or action, causing functional disability. After a stroke, the right hemisphere experiences USN more noticeably, severely, and persistently than the left. However, few studies using fNIRS have been reported in cases of USN. The study aimed to confirm weaker RSFC in USN and investigate potential inherent features in hemodynamic fluctuations that may be associated with USN. Furthermore, these features were combined into a mathematical model for more accurate classification.
Methods: Thirty-three stroke patients with right-sided brain damage were chosen, of whom 12 had non-USN after-stroke, and 21 had USN after-stroke (the USN group). Graph theory was used to evaluate the hemodynamic signals of the brain's right cerebral cortex during rest. Furthermore, a support vector machine model was built to categorize the subjects into two groups based on the chosen network properties.
Results: First, mean functional connectivity was lower in the USN group (0.745±0.239) than in the non-USN group (0.843±0.254) (t =-4.300, p < 0.001). Second, compared with the non-USN group, USN patients had a larger clustering coefficient (C) (t =3.145, p < 0.001), local efficiency (LE) (t =3.189, p < 0.001), and smaller global efficiency (GE) (t =3.047, p < 0.001). Notably, there were differences in characteristic path length (L) and small worldness (σ) values between the two groups at certain thresholds, mainly as higher L (t =3.074, p < 0.001) and lower small worldness (σ) values (t =2.998, p < 0.001) in USN patients compared with non-USN patients. Lastly, the classification accuracy of the SVM model based on AUC aC (t=-2.259, p=0.031) and AUC aLE (t=-2.063, p=0.048) was 85%, the sensitivity was 75%, and the specificity was 89%.
Conclusions: The functional network architecture of the right cerebral cortex exhibits significant topological alterations in individuals with USN following stroke, and the sensitivity index based on the small-world property AUC may be utilized to identify these patients accurately.