{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:16:57Z","timestamp":1791389817700,"version":"4.3.3"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"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>Background<\/jats:title>\n                    <jats:p>Adolescent depression diagnosis currently relies primarily on subjective self-report questionnaires, with a notable lack of objective neurobiological biomarkers. This study aimed to compare the diagnostic performance of traditional psychological scales with a newly developed Multi-view Adaptive Graph Convolutional Network (MVA-GCN) based on resting-state electroencephalography (EEG), and to exploratorily examine the associations between MVA-GCN-derived brain network features and psychological resilience in adolescents.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Resting-state EEG data were collected from 44 adolescents with major depressive disorder (MDD) and 30 healthy controls (HCs). The MVA-GCN model integrated three parallel connectivity views\u2014phase-locking value (PLV), Pearson correlation coefficient (PCC), and phase lag index (PLI)\u2014via an adaptive fusion mechanism. We compared the classification performance of questionnaire-based machine learning models against the MVA-GCN, and further examined the correlations between model-highlighted network features and clinical symptom measures, including depression, anxiety, loneliness, rumination, and resilience, with age and sex included as covariates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Questionnaire-based machine learning models achieved a mean classification accuracy of 86.43%, whereas the MVA-GCN attained 99.84% accuracy in the present dataset. Occipital and fronto-central regions contributed most to the model\u2019s predictions. Increased gamma-band functional connectivity and reduced alpha-band power were identified as potential electrophysiological correlates of group differences. Exploratory correlational analyses at the nominal level (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05, uncorrected) suggested potential group-specific patterns in brain\u2013resilience associations. A\n                      <jats:italic>post-hoc<\/jats:italic>\n                      deviation index analysis showed that, within the MDD group, greater deviation from the healthy reference brain\u2013resilience association pattern was significantly correlated with more severe rumination, self-rated depression, clinician-rated depression severity, loneliness, and self-rated anxiety (all FDR\n                      <jats:italic>q<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this exploratory sample, the MVA-GCN demonstrated promising proof-of-concept discriminative capability for adolescent depression compared with traditional self-report scales. However, the brain\u2013resilience association findings were not statistically significant after correction for multiple comparisons and should be interpreted with caution. These results suggest that MVA-GCN-derived network features warrant further investigation in larger, independent cohorts, but do not yet support their use as a clinically validated diagnostic tool.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1908969","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:24:42Z","timestamp":1785471882000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Adolescent depression recognition and symptom prediction based on multi-view EEG network: an exploratory study"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanli","family":"Zhao","sequence":"first","affiliation":[{"name":"Psychiatric Research Center, Capital Medical University Affiliated Beijing Huilongguan Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Chen","sequence":"additional","affiliation":[{"name":"Psychiatric Research Center, Capital Medical University Affiliated Beijing Huilongguan Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiao","family":"Ma","sequence":"additional","affiliation":[{"name":"Laboratory of Brain Science and Intelligent Information Processing, School of Computer Science and Technology, Taiyuan Normal University","place":["Jinzhong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyue","family":"Li","sequence":"additional","affiliation":[{"name":"Laboratory of Brain Science and Intelligent Information Processing, School of Computer Science and Technology, Taiyuan Normal University","place":["Jinzhong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"Laboratory of Brain Science and Intelligent Information Processing, School of Computer Science and Technology, Taiyuan Normal University","place":["Jinzhong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Wei","sequence":"additional","affiliation":[{"name":"Laboratory of Brain Science and Intelligent Information Processing, School of Computer Science and Technology, Taiyuan Normal University","place":["Jinzhong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Song","sequence":"additional","affiliation":[{"name":"Psychiatric Research Center, Capital Medical University Affiliated Beijing Huilongguan Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"Psychiatric Research Center, Capital Medical University Affiliated Beijing Huilongguan Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Xin","sequence":"additional","affiliation":[{"name":"Psychiatric Research Center, Capital Medical University Affiliated Beijing Huilongguan Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guimei","family":"Yin","sequence":"additional","affiliation":[{"name":"Laboratory of Brain Science and Intelligent Information Processing, School of Computer Science and Technology, Taiyuan Normal University","place":["Jinzhong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuping","family":"Tan","sequence":"additional","affiliation":[{"name":"Psychiatric Research Center, Capital Medical University Affiliated Beijing Huilongguan Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"16065","DOI":"10.1038\/nrdp.2016.65","article-title":"Major depressive disorder","volume":"2","author":"Otte","year":"2016","journal-title":"Nat Rev Dis Primers"},{"key":"B2","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1111\/bjc.12333","article-title":"Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis","volume":"61","author":"Shorey","year":"2022","journal-title":"Br J Clin Psychol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1038\/nrn2513","article-title":"Why do many psychiatric disorders emerge during adolescence","volume":"9","author":"Paus","year":"2008","journal-title":"Nat Rev Neurosci"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1016\/j.cub.2017.04.051","article-title":"Modular segregation of structural brain networks supports the development of executive function in youth","volume":"27","author":"Baum","year":"2017","journal-title":"Curr Biol.: CB."},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1038\/13158","article-title":"Brain development during childhood and adolescence: A longitudinal MRI study","volume":"2","author":"Giedd","year":"1999","journal-title":"Nat Neurosci"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0402680101","article-title":"Dynamic mapping of human cortical development during childhood through early adulthood","volume":"101","author":"Gogtay","year":"2004","journal-title":"PNAS"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/nrn2353","article-title":"The social brain in adolescence","volume":"9","author":"Blakemore","year":"2008","journal-title":"Nat Rev Neurosci"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.3446-12.2013","article-title":"A developmental shift from positive to negative connectivity in human amygdala-prefrontal circuitry","volume":"33","author":"Gee","year":"2013","journal-title":"J Neurosci.: Off J Soc For. Neurosci"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2016.00040","article-title":"Fusiform gyrus dysfunction is associated with perceptual processing efficiency to emotional faces in adolescent depression: A model-based approach","volume":"7","author":"Ho","year":"2016","journal-title":"Front Psychol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1038\/npp.2017.103","article-title":"Inflexible functional connectivity of the dorsal anterior cingulate cortex in adolescent major depressive disorder","volume":"42","author":"Ho","year":"2017","journal-title":"Neuropsychopharmacology"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2011.02.003","article-title":"Default-mode and task-positive network activity in major depressive disorder: Implications for adaptive and maladaptive rumination","volume":"70","author":"Hamilton","year":"2011","journal-title":"Biol Psychiatry"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1001\/jamapsychiatry.2015.0071","article-title":"Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity","volume":"72","author":"Kaiser","year":"2015","journal-title":"JAMA Psychiatry"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07805-2","article-title":"Frontostriatal salience network expansion in individuals in depression","volume":"633","author":"Lynch","year":"2024","journal-title":"Nature"},{"key":"B14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neubiorev.2019.07.021","article-title":"Depression biomarkers using non-invasive EEG: A review","volume":"105","author":"de Aguiar Neto","year":"2019","journal-title":"Neurosci Biobehav Rev"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015596416791","article-title":"Changes in the EEG rhythms in endogenous depressive disorders and the effect of pharmacotherapy","volume":"28","author":"Omel\u2019chenko","year":"2002","journal-title":"Fiziol. Cheloveka."},{"key":"B16","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/s0531-5131(02)00168-1","article-title":"EEG topography and tomography in diagnosis and treatment of mental disorders: Evidence for a key-lock principle","volume":"24","author":"Saletu","year":"2002","journal-title":"Methods Findings. Exp Clin Pharmacol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-023-02394-4","article-title":"Deep brain stimulation for refractory major depressive disorder: a comprehensive review","volume":"29","author":"Johnson","year":"2024","journal-title":"Mol Psychiatry"},{"key":"B18","article-title":"Adversarial graph augmentation to improve graph contrastive learning","volume-title":"Neural Information Processing Systems","author":"Suresh","year":"2021"},{"key":"B19","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neunet.2020.08.009","article-title":"Emotional EEG classification using connectivity features and convolutional neural networks","volume":"132","author":"Moon","year":"2020","journal-title":"Neural Networks.: Off J Int Neural Network. Soc"},{"key":"B20","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM52615.2021.9669572","article-title":"Identification of depression with a semi-supervised GCN based on EEG data","author":"Wang","year":"2021"},{"key":"B21","article-title":"Intelligent diagnosis of depression by integrating genetic algorithm and graph neural network","volume":"29","author":"Dan","year":"2024","journal-title":"J Image. Graphics"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2025024","article-title":"Multimodal depression detection based on an attention graph convolution and transformer","volume":"22","author":"Jia","year":"2025","journal-title":"Math. Biosci Eng"},{"key":"B23","doi-asserted-by":"publisher","first-page":"28400","DOI":"10.1038\/s41598-024-79981-0","article-title":"An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection","volume":"14","author":"Xing","year":"2024","journal-title":"Sci Rep"},{"key":"B24","volume-title":"Diagnostic and Statistical Manual of Mental Disorders (5th Ed.)","year":"2013"},{"key":"B25","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-79948-3_2011","article-title":"Structured clinical interview for DSM-IV (SCID-I\/SCID-II)","volume-title":"Encyclopedia of Clinical Neuropsychology","author":"Gorgens","year":"2011"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1111\/j.2044-8260.1967.tb00530.x","article-title":"Development of a rating scale for primary depressive illness","volume":"6","author":"Hamilton","year":"1967","journal-title":"Br J Soc Clin Psychol"},{"key":"B27","volume-title":"ECDEU Assessment Manual for Psychopharmacology","author":"Guy","year":"1976"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1111\/j.2044-8341.1959.tb00467.x","article-title":"The assessment of anxiety states by rating","volume":"32","author":"Hamilton","year":"1959","journal-title":"Br J Med Psychol"},{"key":"B29","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1111\/j.1469-7610.1987.tb00651.x","article-title":"Clinical evaluation of a self-rating scale for depressive disorder in childhood (Depression Self-Rating Scale)","volume":"28","author":"Birleson","year":"1987","journal-title":"J Child Psychol Psychiatry"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-7967(98)00034-5","article-title":"A measure of anxiety symptoms among children","volume":"36","author":"Spence","year":"1998","journal-title":"Behav Res Ther"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1007\/s10578-015-0615-2","article-title":"Reliability and validity of the Chinese version of Spence Children\u2019s Anxiety Scale","volume":"10","author":"Wang","year":"2002","journal-title":"Chin J Clin Psychol"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.39.3.472","article-title":"The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence","volume":"39","author":"Russell","year":"1980","journal-title":"J Pers Soc Psychol"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023910315561","article-title":"Rumination reconsidered: A psychometric analysis","volume":"27","author":"Treynor","year":"2003","journal-title":"Cogn Ther Res"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.3724\/sp.j.1041.2008.00902","article-title":"Development and psychometric validity of the Resilience Scale for Chinese Adolescents","volume":"40","author":"Hu","year":"2008","journal-title":"Acta Psychol. Sin"},{"key":"B35","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.brainres.2012.01.052","article-title":"Conflict monitoring and resolution: are two languages better than one? Evidence from reaction time and event-related brain potentials","volume":"1446","author":"Kousaie","year":"2012","journal-title":"Brain Res"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2013.00267MNE","article-title":"MEG and EEG data analysis with MNE-Python","volume":"7","author":"Gramfort","year":"2013","journal-title":"Front Neurosci"},{"key":"B37","unstructured":"MNE \u2014 MNE 1.12.0 Documentation"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1162\/089976699300016719","article-title":"Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources","volume":"11","author":"Lee","year":"1999","journal-title":"Neural Comput"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017","journal-title":"2017 IEEE Int Conf Comput Vision (ICCV)."},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1038\/nrn2575","article-title":"Complex brain networks: Graph theoretical analysis of structural and functional systems","volume":"10","author":"Bullmore","year":"2009","journal-title":"Nat Rev Neurosci"},{"key":"B41","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1038\/s41583-024-00846-6","article-title":"Structure\u2013function coupling in macroscale human brain networks","volume":"25","author":"Fotiadis","year":"2024","journal-title":"Nat Rev Neurosci"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1080\/03610918.2013.809097","article-title":"Efficient stratified testing procedure for a false discovery rate","volume":"44","author":"Han","year":"2015","journal-title":"Commun Stat- Simul. Comput"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2025.3527888","article-title":"Multiview graph learning with consensus graph","volume":"11","author":"Karaaslanli","year":"2025","journal-title":"IEEE Trans Signal Inf. Proc. Networks"},{"key":"B44","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.neuroimage.2013.04.010","article-title":"The contribution of electrophysiology to functional connectivity mapping","volume":"80","author":"Sch\u00f6lvinck","year":"2013","journal-title":"NeuroImage. Mapp Connectome."},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.12.066","article-title":"Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity","volume":"91","author":"Brookes","year":"2014","journal-title":"NeuroImage"},{"key":"B46","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C","article-title":"Measuring phase synchrony in brain signals","volume":"8","author":"Lachaux","year":"1999","journal-title":"Hum Brain Mapp."},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2008.919072","article-title":"On the importance of the Pearson correlation coefficient in noise reduction","volume":"16","author":"Benesty","year":"2008","journal-title":"IEEE Trans Audio. Speech. Lang. Proc."},{"key":"B48","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10916-019-1345-y","article-title":"Automated depression detection using deep representation and sequence learning with EEG signals","volume":"43","author":"Ay","year":"2019","journal-title":"J Med Syst"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105690","article-title":"Minimal EEG channel selection for depression detection with connectivity features during sleep","volume":"147","author":"Zhang","year":"2022","journal-title":"Comput Biol Med"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2026.04.030","article-title":"Systematic review of machine learning and deep learning models for EEG-based detection of depression","volume":"199","author":"Luna-Guevara","year":"2026","journal-title":"J Psychiatr Res"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpsc.2017.07.001","article-title":"Electroencephalography source functional connectivity reveals abnormal high-frequency communication among large-scale functional networks in depression","volume":"3","author":"Whitton","year":"2018","journal-title":"Biol Psychiatry Cogn Neurosci Neuroimaging"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1900801116","article-title":"Childhood trauma history is linked to abnormal brain connectivity in major depression","volume":"116","author":"Yu","year":"2019","journal-title":"PNAS"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202102076","article-title":"Loneliness and the social brain: How perceived social isolation impairs human interactions","volume":"8","author":"Lieberz","year":"2021","journal-title":"Adv Sci"},{"key":"B54","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1038\/s42003-025-08429-6","article-title":"Visuoaffective day residue in hypnagogia involves sequential bihemispheric interactions between cortical, subcortical, and cerebellar structures","volume":"8","author":"Souza","year":"2025","journal-title":"Commun Biol"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2423560122","article-title":"The brain computes dynamic facial movements for emotion categorization using a third pathway","volume":"122","author":"Yan","year":"2025","journal-title":"PNAS"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065717500137","article-title":"From structure to activity: Using centrality measures to predict neuronal activity","volume":"28","author":"Fletcher","year":"2018","journal-title":"Int J Neural Syst"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1097\/RLU.0000000000005227","article-title":"Tumors affect the metabolic connectivity of the human brain measured by 18 F-FDG PET","volume":"49","author":"Pasquini","year":"2024","journal-title":"Clin Nucl Med"},{"key":"B58","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1093\/brain\/awaa001","article-title":"Brain structural covariance networks in obsessive-compulsive disorder: A graph analysis from the ENIGMA Consortium","volume":"143","author":"Yun","year":"2020","journal-title":"Brain"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropharm.2012.04.021","article-title":"The neurobiology of the EEG biomarker as a predictor of treatment response in depression","volume":"63","author":"Baskaran","year":"2012","journal-title":"Neuropharmacology"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2019.09.039","article-title":"Effects of vortioxetine and escitalopram on electroencephalographic recordings - A randomized, crossover trial in healthy males","volume":"424","author":"Nissen","year":"2020","journal-title":"Neuroscience"},{"key":"B61","article-title":"No unbiased estimator of the variance of K-fold cross-validation","volume":"5","author":"Bengio","year":"2004","journal-title":"J Mach Learn Res"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017197","article-title":"Approximate statistical tests for comparing supervised classification learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput"}],"container-title":["Frontiers in Psychiatry"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2026.1908969\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:24:45Z","timestamp":1785471885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2026.1908969\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":62,"alternative-id":["10.3389\/fpsyt.2026.1908969"],"URL":"https:\/\/doi.org\/10.3389\/fpsyt.2026.1908969","relation":{},"ISSN":["1664-0640"],"issn-type":[{"value":"1664-0640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]},"article-number":"1908969"}}