{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T01:33:27Z","timestamp":1761528807155,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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 classifiers\u2014Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)\u2014were 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 (ROC-AUC = 0.909) under subject-aware CV5; at the default threshold, Sensitivity was moderate and Specificity was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited Sensitivity despite high Specificity. 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.<\/jats:p>","DOI":"10.3390\/make7040129","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:51:42Z","timestamp":1761526302000},"page":"129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring New Horizons: fNIRS and Machine Learning in Understanding PostCOVID-19"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3669-2638","authenticated-orcid":false,"given":"Antony","family":"Morales-Cervantes","sequence":"first","affiliation":[{"name":"Department of Postgraduate Studies and Research, TecNM\u2014Instituto Tecnol\u00f3gico de Morelia, Av. Tecnol\u00f3gico 1500, Morelia 58120, Michoac\u00e1n, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1367-8622","authenticated-orcid":false,"given":"Victor","family":"Herrera","sequence":"additional","affiliation":[{"name":"Coordinaci\u00f3n para la Innovaci\u00f3n y Aplicaci\u00f3n de la Ciencia y la Tecnolog\u00eda\u2014CIACYT, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona 550, San Luis Potos\u00ed 78120, San Luis Potos\u00ed, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0093-7752","authenticated-orcid":false,"given":"Blanca Nohem\u00ed","family":"Zamora-Mendoza","sequence":"additional","affiliation":[{"name":"Laboratorio de Salud Total, Centro de Investigaci\u00f3n Aplicada en Ambiente y Salud\u2014CIACYT, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona 550, San Luis Potos\u00ed 78120, San Luis Potos\u00ed, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2263-6280","authenticated-orcid":false,"given":"Rogelio","family":"Flores-Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Laboratorio de Salud Total, Centro de Investigaci\u00f3n Aplicada en Ambiente y Salud\u2014CIACYT, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona 550, San Luis Potos\u00ed 78120, San Luis Potos\u00ed, Mexico"},{"name":"SECIHTI\u2014Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona 550, San Luis Potos\u00ed 78120, San Luis Potos\u00ed, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9772-4282","authenticated-orcid":false,"given":"Aaron A.","family":"L\u00f3pez-Cano","sequence":"additional","affiliation":[{"name":"Coordinaci\u00f3n para la Innovaci\u00f3n y Aplicaci\u00f3n de la Ciencia y la Tecnolog\u00eda\u2014CIACYT, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona 550, San Luis Potos\u00ed 78120, San Luis Potos\u00ed, Mexico"},{"name":"Faculty of Science, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Av. Chapultepec 1570, San Luis Potos\u00ed 78295, San Luis Potos\u00ed, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2313-2810","authenticated-orcid":false,"given":"Edgar","family":"Guevara","sequence":"additional","affiliation":[{"name":"Coordinaci\u00f3n para la Innovaci\u00f3n y Aplicaci\u00f3n de la Ciencia y la Tecnolog\u00eda\u2014CIACYT, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona 550, San Luis Potos\u00ed 78120, San Luis Potos\u00ed, Mexico"},{"name":"Faculty of Science, Universidad Aut\u00f3noma de San Luis Potos\u00ed, Av. Chapultepec 1570, San Luis Potos\u00ed 78295, San Luis Potos\u00ed, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, September 28). Post COVID-19 Condition. 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