ABSTRACT

PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiersβ€”Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)β€”were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifier trained on hybrid features achieved strong discrimination (𝑅𝑂𝐢-π΄π‘ˆπΆ = 0.909) under subject-aware CV5; at the default threshold, 𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 was moderate and 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited 𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 despite high 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦. These findings highlight the importance of temporal information in the fNIRS signal and support the potential of machine learning combined with portable neuroimaging for postCOVID-19 identification. This approach may contribute to the development of non-invasive diagnostic tools to support individualized treatment and longitudinal monitoring of patients with persistent neurological symptoms.