The Predictive Value of Dynamic Intrinsic Local Metrics in Transient Ischemic Attack - Ma, Huibin; Huang, Guofeng; Li, Mengting; Han, Yu; Sun, Jiawei; Zhan, Linlin; Wang, Qianqian; Jia, Xize; Han, Xiujie; Li, Huayun; Song, Yulin; Lv, Yating (Frontiers in Aging Neuroscience, 2022)
Background: Transient ischemic attack (TIA) is known as ‘small stroke’. However, the diagnosis of TIA is currently difficult due to the transient symptoms. Therefore, objective and reliable biomarkers are urgently needed in clinical practice. Objective: The purpose of the current study was to investigate whether dynamic alterations of resting-state local metrics could differentiate patients with transient ischemic attack (TIA) from healthy controls (HCs) using Support Vector Machine (SVM) classification method. Methods: By analyzing resting-state fMRI data from 48 TIA patients and 41 demographically matched healthy controls (HCs), we compared the group-differences in three dynamic local metrics: dynamic amplitude of low-frequency fluctuation (d-ALFF), dynamic fractional amplitude of low-frequency fluctuation (d-fALFF) and dynamic regional homogeneity (d-ReHo). Furthermore, we selected the observed alterations in three dynamic local metrics as classification features to distinguish patients with TIA from HCs through SVM classifier. Results: We found that TIA was associated with disruptions in dynamic local intrinsic brain activity. Compared with HCs, the TIA patients exhibited increased d-fALFF d-fALFF and d-ReHo in vermis, right calcarine, right middle temporal gyrus, opercular part of right inferior frontal gyrus, left calcarine, left occipital, and left temporal and cerebellum. These alternations in dynamic local metrics exhibited an accuracy of 80.90% and area under curve of 0. 8501 for distinguishing the patients from HCs. Conclusions: Our findings may provide important evidence for understanding neuropathology underlying TIA and strong support for the hypothesis that these local metrics have potential value in clinical diagnosis.