{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:01:53Z","timestamp":1775804513537,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The neuroimaging biomarkers for anxiety diagnosis remain poorly understood. This study aims to identify cortical regions whose hemodynamic patterns, derived from resting-state functional near-infrared spectroscopy (fNIRS) combined with machine learning, may serve as potential biomarkers to assist in the assessment of anxiety cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The final sample comprised ninety participants in emerging adulthood: 50 anxiety patients and 40 healthy controls (HC). After demographic data collection and Generalized Anxiety Disorder-7 (GAD-7) administration, all participants underwent a 1-minute baseline measurement and a 5-minute resting-state fNIRS recording. Following a stratified 70:30 train-test split, all feature selection procedures were performed using the training set. Model tuning and stability were assessed by five-fold stratified cross-validation within the training set, and final performance was evaluated on the independent test set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The machine-learning GBDT model utilized 13 distinct features and exhibited enhanced efficacy in discriminating between patients with anxiety and healthy controls compared with the RF classification model typically employed for anxiety identification (AUC: 0.900 vs. 0.832, sensitivity = 0.921, and specificity = 0.709). SHAP-based interpretability analysis revealed that oxyhemoglobin (HbO) fluctuations in the prefrontal regions, particularly the dorsolateral\/middle frontal\/orbital middle frontal gyrus and sensorimotor-visual areas (precentral\/middle occipital gyrus), emerged as potential predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The study provides a new perspective for the development of anxiety diagnosis tools and contributes candidate biomarkers for anxiety disorder identification and intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1722529","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:06:53Z","timestamp":1775801213000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying neuroimaging biomarkers for anxiety in emerging adults using machine learning and functional near-infrared spectroscopy during resting-state"],"prefix":"10.3389","volume":"17","author":[{"given":"Sibo","family":"Huang","sequence":"first","affiliation":[{"name":"School of Intelligent Medicine and Technology (Big Data Research Center), Hainan Medical University","place":["Haikou, Hainan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benguo","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Intelligent Medicine and Technology (Big Data Research Center), Hainan Medical University","place":["Haikou, Hainan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Liang","sequence":"additional","affiliation":[{"name":"The First Clinical College, Hainan Medical University","place":["Haikou, Hainan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.31887\/DCNS.2017.19.2\/bbandelow","article-title":"Treatment of anxiety disorders","volume":"19","author":"Bandelow","year":"2017","journal-title":"Dial Clin Neurosci"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/s2215-0366(21)00395-3","article-title":"Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"9","year":"2022","journal-title":"Lancet Psychiatry"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2023.101331","article-title":"Burden of noncommunicable diseases among children and adolescents aged 10\u201324 years in China, 1990-2019: A population-based study","volume":"4","author":"Zhang","year":"2023","journal-title":"Cell Rep Med"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(20)30752-2","article-title":"Global burden of 87 risk factors in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"396","year":"2020","journal-title":"Lancet (Londo Engl)"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2020.110207","article-title":"Prevalence of anxiety in the Covid-19 pandemic: An updated meta-analysis of community-based studies","volume":"109","author":"Santab\u00e1rbara","year":"2021","journal-title":"Prog Neuro-Psychophar Biol Psychiatry"},{"key":"B6","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s11920-016-0739-5","article-title":"Anxiety disorders and cardiovascular disease","volume":"18","author":"Celano","year":"2016","journal-title":"Curr Psychiatry Rep"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1017\/s0033291720005358","article-title":"Increases in depression and anxiety symptoms in adolescents and young adults during the Covid-19 pandemic","volume":"52","author":"Hawes","year":"2022","journal-title":"Psychol Med"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2021.775762","article-title":"Poor separation of clinical symptom profiles by DSM-5 disorder criteria","volume":"12","author":"Newson","year":"2021","journal-title":"Front Psychiatry"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2020.00015","article-title":"Improving diagnosis of depression with XGBoost machine learning model and a large biomarkers Dutch dataset (N = 11,081)","volume":"3","author":"Sharma","year":"2020","journal-title":"Front Big Data"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1111\/cns.13048","article-title":"Machine learning in major depression: From classification to treatment outcome prediction","volume":"24","author":"Gao","year":"2018","journal-title":"CNS Neurosci Ther"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.03.049","article-title":"A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application","volume":"63","author":"Ferrari","year":"2012","journal-title":"NeuroImage"},{"key":"B12","doi-asserted-by":"publisher","first-page":"4880","DOI":"10.1038\/s41467-023-40666-3","article-title":"Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex","volume":"14","author":"Bramson","year":"2023","journal-title":"Nat Commun"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3940(93)90181-j","article-title":"Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults","volume":"154","author":"Villringer","year":"1993","journal-title":"Neurosci Lett"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.03.067","article-title":"Application of functional near-infrared spectroscopy in psychiatry","author":"Ehlis","year":"2014","journal-title":"NeuroImage"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2017.07.004","article-title":"The impact of machine learning techniques in the study of bipolar disorder: A systematic review","volume":"80","author":"Librenza-Garcia","year":"2017","journal-title":"Neurosci Biobehav Rev"},{"key":"B16","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.jpsychires.2022.01.026","article-title":"Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data","volume":"147","author":"Ho","year":"2022","journal-title":"J Psychiatr Res"},{"key":"B17","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1109\/tnsre.2021.3078460","article-title":"Detection of emotional sensitivity using fNIRS based dynamic functional connectivity","volume":"29","author":"Tang","year":"2021","journal-title":"IEEE Trans Neural Syst Rehabil Eng: A Publ IEEE Eng Med Biol Soc"},{"key":"B18","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.166.10.1092","article-title":"A brief measure for assessing generalized anxiety disorder: The GAD-7","volume":"166","author":"Spitzer","year":"2006","journal-title":"Arch Internal Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/rbme.2019.2944351","article-title":"A newcomer's guide to functional near infrared spectroscopy experiments","volume":"13","author":"Almajidy","year":"2020","journal-title":"IEEE Rev BioMed Eng"},{"key":"B21","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1038\/s41398-020-01088-7","article-title":"Intrinsic organization of cortical networks predicts state anxiety: An functional near-infrared spectroscopy (fNIRS) study","volume":"10","author":"Duan","year":"2020","journal-title":"Transl Psychiatry"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/s0006-3223(02)01550-0","article-title":"Non-invasive neuroimaging using near-infrared light","volume":"52","author":"Strangman","year":"2002","journal-title":"Biol Psychiatry"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.11.2.025001","article-title":"Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches","volume":"11","author":"Huang","year":"2024","journal-title":"Neurophotonics"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/33\/12\/008","article-title":"Estimation of optical pathlength through tissue from direct time of flight measurement","volume":"33","author":"Delpy","year":"1988","journal-title":"Phys Med Biol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2023.102409","article-title":"Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: A prospective multicenter cohort study","volume":"68","author":"Hu","year":"2024","journal-title":"EClinicalMedicine"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1196\/annals.1401.006","article-title":"The role of the orbitofrontal cortex in anxiety disorders","volume":"1121","author":"Milad","year":"2007","journal-title":"Ann N Y Acad Sci"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2019.00465","article-title":"The altered somatic brain network in state anxiety","volume":"10","author":"Li","year":"2019","journal-title":"Front Psychiatry"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-021-01109-z","article-title":"The prefrontal cortex, pathological anxiety, and anxiety disorders","volume":"47","author":"Kenwood","year":"2022","journal-title":"Neuropsychopharmacology"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/4459867","article-title":"Functional near-infrared spectroscopy-based computer-aided diagnosis of major depressive disorder using convolutional neural network with a new channel embedding layer considering inter-hemispheric asymmetry in prefrontal hemodynamic responses","volume":"2024","author":"Lee","year":"2024","journal-title":"Depression Anxiety"},{"key":"B30","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jad.2024.08.082","article-title":"Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task","volume":"365","author":"Mao","year":"2024","journal-title":"J Affect Disord"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/s1474-4422(23)00447-7","article-title":"European intersocietal recommendations for the biomarker-based diagnosis of neurocognitive disorders","volume":"23","author":"Frisoni","year":"2024","journal-title":"Lancet Neurol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.jad.2025.01.019","article-title":"Prediction of health anxiety using resting-state functional near-infrared spectroscopy and machine learning","volume":"374","author":"Shi","year":"2025","journal-title":"J Affect Disord"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120499","article-title":"Prediction of anxious depression using multimodal neuroimaging and machine learning","volume":"285","author":"Zhou","year":"2024","journal-title":"NeuroImage"},{"key":"B34","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1093\/schbul\/sbn135","article-title":"Psychiatric comorbidities and schizophrenia","volume":"35","author":"Buckley","year":"2009","journal-title":"Schizophr Bull"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2025.104382","article-title":"Functional near-infrared spectroscopy (fNIRS) in patients with major depressive disorder, generalized anxiety disorder and their comorbidity: Comparison with healthy controls","volume":"105","author":"Shen","year":"2025","journal-title":"Asian J Psychiatry"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104027","article-title":"Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches","volume":"79","author":"Li","year":"2022","journal-title":"EBioMedicine"}],"container-title":["Frontiers in Psychiatry"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2026.1722529\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:06:54Z","timestamp":1775801214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2026.1722529\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":36,"alternative-id":["10.3389\/fpsyt.2026.1722529"],"URL":"https:\/\/doi.org\/10.3389\/fpsyt.2026.1722529","relation":{},"ISSN":["1664-0640"],"issn-type":[{"value":"1664-0640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"article-number":"1722529"}}