The Predictive Value of Dynamic Intrinsic Local Metrics in Transient Ischemic Attack

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.

Dynamic regional homogeneity alterations and cognitive impairment in patients with moderate and severe obstructive sleep apnea

“…Moreover, with the intensive development of machine learning technology, it will be a general trend for doctors to utilize artificial intelligence to diagnose and manage the health of patients. Moreover, SVM has been extensively applied to various diseases and has achieved good classification performance (Gui et al, 2021;Ma H. et al, 2021). Using an SVM classifier, OSA patients can be distinguished from HCs by dynamic local indicators.…”

“…Sun et al employed SVM to classify individuals with bipolar disorder and unipolar disorder based on dReHo data and achieved an accuracy rate of 91.86% (Sun et al, 2021). Ma H. et al (2021) also used this classifier to distinguish transient ischemic attacks from normal individuals with an accuracy rate of more than 80%. Thus, as SVM has been proposed as an effective tool for clinical diagnosis, we attempted to distinguish OSA patients from healthy controls (HCs) using SVM based on dReHo data.…”

The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach

“…Data were acquired from 51 patients with suspected TIA in the Department of Neurology at the Anshan Changda Hospital, Liaoning, China. Patients with transient neurological symptoms may have a vascular etiology, according to the assessment of clinical psychiatrists [ 11 , 36 ]. Blood pressure, clinical features, symptom duration, and history of diabetes symptoms were assessed for each patient.…”

“…The steps were as follows: (1) the mean ALFF and fALFF values in WM regions showing significant differences between the two groups were used together to serve as features and were normalized from -1 to 1. According to previous studies on support vector machines (SVM), the combination of features of multiple metrics has a better classification effect than using single metric as the feature [36,[72][73][74]. (2) The parametric Q-learning method [75] was used to train the approximate Q value function with the linear model and obtain reward feedback by interacting with the environment to find the optimal Q value function and obtain the final classification.…”

“…The ALFF measures the signal intensity in low-frequency oscillations (LFOs) of local spontaneous neural activity of the brain [ 34 ] and has been proven to exhibit outstanding test-retest reliability [ 35 ]. Previous studies have investigated spontaneous neural activities in the GM and found decreased ALFF in patients with TIA [ 11 , 36 ], providing evidence of brain dysfunction in TIA. Moreover, based on ALFF, fALFF was raised to characterize the relative contribution of a specific LFO to the whole frequency range, effectively reducing physiological noise and suppressing artifacts in nonspecific brain regions [ 35 , 37 ].…”