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Exploring low‐ and high‐order functional connectivity in chronic ankle instability through resting‐state fMRI

支持向量机 静息状态功能磁共振成像 功能磁共振成像 脚踝 相关性 计算机科学 默认模式网络 功能连接 人工智能 模式识别(心理学) 机器学习 皮尔逊积矩相关系数 交叉验证 物理医学与康复 医学 心理学 神经科学 数学 统计 病理 几何学
作者
Yajie Li,Zhifeng Wang,Yang Yang,Yan Deng,Yiyuan Shen,Xu Wang,Weiwei Wang,Hanqiu Liu
出处
期刊:Medical Physics [Wiley]
标识
DOI:10.1002/mp.17474
摘要

Abstract Background The functional connectivity (FC) has emerged as a valuable tool for comprehending the cerebral operational mechanism. Understanding the FC changes in patients with chronic ankle instability (CAI) helps reveal the underlying central nervous system mechanisms of the disease and provides clues for developing personalized treatment plans. Objectives To explore differences between low‐ and high‐order FC in patients with CAI and healthy controls, as well as the correlation among the feature connections and clinical data. Methods In our study, we recruited 40 patients with CAI and 42 healthy individuals who had not experienced ankle injuries. All participants underwent clinical assessments of ankle joints, collected the number of ankle sprains within the past 6 months, and performed resting‐state functional magnetic resonance imaging (rs‐fMRI) scans. Pearson correlation and matrix variate normal distribution (MVND) were used to construct low‐order and high‐order FC networks, respectively. Feature selections between groups were performed by two‐sample t ‐tests, and a multi‐kernel support vector machine (MK‐SVM) was subsequently applied to combine the multiple connection patterns for the classification. Using leave‐one‐out cross‐validation (LOOCV) to assess classification performance and identify the consensus connections contributing most to classification. Results FC was reduced in certain brain regions of CAI patients. More consensus connections were recognized in low‐order FC network than in high‐order FC network. The highest classification accuracy of 91.30% was achieved by combining three connection patterns. The most discriminating functional connections were primarily centered on the default mode network and spanned the visual network, sensorimotor network, ventral attention network, and central executive network. In addition, FC strength in the left cingulate and paracingulate gyrus (DCG.L) and right superior temporal gyrus (STG.R) was negatively correlated with the number of ankle sprains in the past 6 months in all FC networks ( p < 0.05). Conclusions Abnormalities in connectivity in patients with CAI were observed in both low‐ and high‐order FC networks. The adaptive changes in the brain related to CAI may extend beyond the sensorimotor networks, primarily involving higher‐level default mode networks associated with attention. Moreover, the FC strength between DCG.L and STG.R may predict the risk of ankle re‐sprains and help clinicians develop personalized treatment plans.
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