Abstract
BackgroundTransient 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.ObjectiveThe purpose of this study was to investigate whether dynamic alterations in resting-state local metrics could differentiate patients with TIA from healthy controls (HCs) using the support-vector machine (SVM) classification method.MethodsBy analyzing resting-state functional MRI (rs-fMRI) data from 48 patients with and 41 demographically matched 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.ResultsWe found that TIA was associated with disruptions in dynamic local intrinsic brain activities. Compared with HCs, the patients with TIA 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 the dynamic local metrics exhibited an accuracy of 80.90%, sensitivity of 77.08%, specificity of 85.37%, precision of 86.05%, and area under curve of 0.8501 for distinguishing the patients from HCs.ConclusionOur findings may provide important evidence for understanding the neuropathology underlying TIA and strong support for the hypothesis that these local metrics have potential value in clinical diagnosis.
Highlights
Transient ischemic attack (TIA) is a transient neurological dysfunction triggered by focal brain, medulla spinalis, or retinal ischemia, known as “small stroke” (Easton et al, 2009)
By using the support-vector machine (SVM) classification method, we further examined whether these dynamic local abnormalities could differentiate patients from healthy controls (HCs)
The performance of the classifier achieved an accuracy of 80.90%, sensitivity of 77.08%, specificity of 85.37%, precision of 86.05%, and area under curve (AUC) of 0.8501 for TIA vs. HCs
Summary
Transient ischemic attack (TIA) is a transient neurological dysfunction triggered by focal brain, medulla spinalis, or retinal ischemia, known as “small stroke” (Easton et al, 2009). Preventing the social harm caused by stroke is very important (On et al, 2021). Given up to 80% of strokes after TIA are preventable (Coutts, 2017), accurate diagnosis of TIA is valuable and meaningful from the perspective of offering the greatest opportunity for the early intervention of stroke. The transient symptoms make the diagnosis of TIA hard and difficult. Transient ischemic attack (TIA) is known as “small stroke.”. The diagnosis of TIA is currently difficult due to the transient symptoms. Objective and reliable biomarkers are urgently needed in clinical practice
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