{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:43:23Z","timestamp":1791420203529,"version":"4.3.3"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076209"],"award-info":[{"award-number":["62076209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated that major depressive disorder subjects exhibit topological brain network changes and different temporal electroencephalography (EEG) characteristics compared to healthy controls. Based on these phenomena, we proposed a novel model, termed as MDD-SSTNet, for detecting major depressive disorder by exploring spectral-spatial-temporal information from resting-state EEG with deep convolutional neural network. Firstly, MDD-SSTNet used the Sinc filter to obtain specific frequency band features from pre-processed EEG data. Secondly, two parallel branches were used to extract temporal and spatial features through convolution and other operations. Finally, the model was trained with a combined loss function of center loss and Binary Cross-Entropy Loss. Using leave-one-subject-out cross-validation on the HUSM dataset and MODMA dataset, the MDD-SSTNet model outperformed six baseline models, achieving average classification accuracies of 93.85% and 65.08%, respectively. These results indicate that MDD-SSTNet could effectively mine spatial-temporal difference information between major depressive disorder subjects and healthy control subjects, and it holds promise to provide an efficient approach for MDD detection with EEG data.<\/jats:p>","DOI":"10.1093\/cercor\/bhae505","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T15:03:45Z","timestamp":1737558225000},"source":"Crossref","is-referenced-by-count":7,"title":["MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network"],"prefix":"10.1093","volume":"35","author":[{"given":"Qiurong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence , Southwest University of Science and Technology, 621010 Mianyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence , Southwest University of Science and Technology, 621010 Mianyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence , Southwest University of Science and Technology, 621010 Mianyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqian","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence , Southwest University of Science and Technology, 621010 Mianyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuzhu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence , Southwest University of Science and Technology, 621010 Mianyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fali","family":"Li","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation , University of Electronic Science and Technology of China, 611731 Chengdu,","place":["China"]},{"name":"Research Unit of NeuroInformation , Chinese Academy of Medical Sciences, 2019RU035, Chengdu,","place":["China"]},{"name":"Department of Electrical and Computer Engineering , Faculty of Science and Technology, University of Macau, 999078, Macau,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Neurology, Sichuan Provincial People\u2019s Hospital , University of Electronic Science and Technology of China, 610072 Chengdu,","place":["China"]},{"name":"Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital , 610072 Chengdu,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangsong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence , Southwest University of Science and Technology, 621010 Mianyang,","place":["China"]},{"name":"School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation , University of Electronic Science and Technology of China, 611731 Chengdu,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"2025021207195199400_ref1","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1159\/000438457","article-title":"A novel depression diagnosis index using nonlinear features in EEG signals","volume":"74","author":"Acharya","year":"2015","journal-title":"Eur Neurol"},{"key":"2025021207195199400_ref2","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","article-title":"Automated EEG-based screening of depression using deep convolutional neural network","volume":"161","author":"Acharya","year":"2018","journal-title":"Comput Methods Prog Biomed"},{"key":"2025021207195199400_ref3","doi-asserted-by":"publisher","first-page":"2390","DOI":"10.1109\/IJCNN.2008.4634130","article-title":"Filter bank common spatial pattern (FBCSP) in brain-computer interface","volume-title":"2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence)","author":"Ang","year":"2008"},{"key":"2025021207195199400_ref4","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":"2025021207195199400_ref5","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.bspc.2016.09.010","article-title":"Single channel EEG analysis for detection of depression","volume":"31","author":"Bachmann","year":"2017","journal-title":"Biomed Signal Process Control"},{"key":"2025021207195199400_ref6","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s10747-005-0042-z","article-title":"Analysis of EEG structural synchrony in adolescents with schizophrenic disorders","volume":"31","author":"Borisov","year":"2005","journal-title":"Hum Physiol"},{"key":"2025021207195199400_ref7","doi-asserted-by":"publisher","first-page":"5238028","DOI":"10.1155\/2018\/5238028","article-title":"A pervasive approach to EEG-based depression detection","volume":"2018","author":"Cai","year":"2018","journal-title":"Complexity"},{"key":"2025021207195199400_ref8","article-title":"MODMA dataset: a multi-modal open dataset for mental-disorder analysis","author":"Cai","year":"2020"},{"key":"2025021207195199400_ref9","doi-asserted-by":"publisher","first-page":"026001","DOI":"10.1088\/1741-2552\/acbb2c","article-title":"Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding","volume":"20","author":"Chen","year":"2023","journal-title":"J Neural Eng"},{"key":"2025021207195199400_ref10","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.neunet.2023.04.045","article-title":"A transformer-based deep neural network model for ssvep classification","volume":"164","author":"Chen","year":"2023","journal-title":"Neural Netw"},{"key":"2025021207195199400_ref11","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: a review","volume":"16","author":"Craik","year":"2019","journal-title":"J Neural Eng"},{"key":"2025021207195199400_ref12","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":"2025021207195199400_ref13","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1159\/000026630","article-title":"Is resting anterior EEG alpha asymmetry a trait marker for depression?","volume":"41","author":"Debener","year":"2000","journal-title":"Neuropsychobiology"},{"key":"2025021207195199400_ref14","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1176\/appi.books.9780890425596","volume-title":"Diagnostic and statistical manual of mental disorders","author":"American Psychiatric Association","year":"2013"},{"key":"2025021207195199400_ref15","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1016\/j.biopsych.2014.12.011","article-title":"Altered structure of dynamic electroencephalogram oscillatory pattern in major depression","volume":"77","author":"Fingelkurts","year":"2015","journal-title":"Biol Psychiatry"},{"key":"2025021207195199400_ref16","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/s41398-018-0239-y","article-title":"Gamma oscillations as a biomarker for major depression: an emerging topic","volume":"8","author":"Fitzgerald","year":"2018","journal-title":"Transl Psychiatry"},{"key":"2025021207195199400_ref17","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.pscychresns.2006.04.006","article-title":"An analysis of functional neuroimaging studies of dorsolateral prefrontal cortical activity in depression","volume":"148","author":"Fitzgerald","year":"2006","journal-title":"Psychiatry Res Neuroimaging"},{"key":"2025021207195199400_ref18","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/ICBAPS.2015.7292237","article-title":"Decrease alpha waves in depression: an electroencephalogram (EEG) study","volume-title":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","author":"Kan","year":"2015"},{"key":"2025021207195199400_ref19","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1001\/archpsyc.55.7.645","article-title":"Cost-effectiveness of treatments for major depression in primary care practice","volume":"55","author":"Lave","year":"1998","journal-title":"Arch Gen Psychiatry"},{"key":"2025021207195199400_ref20","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J Neural Eng"},{"key":"2025021207195199400_ref21","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.euroneuro.2012.10.011","article-title":"Brain, networks, depression, and more","volume":"23","author":"Leistedt","year":"2013","journal-title":"Eur Neuropsychopharmacol"},{"key":"2025021207195199400_ref22","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.cmpb.2016.08.010","article-title":"EEG-based mild depressive detection using feature selection methods and classifiers","volume":"136","author":"Li","year":"2016","journal-title":"Comput Methods Prog Biomed"},{"key":"2025021207195199400_ref23","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1007\/s11517-019-01959-2","article-title":"EEG-based mild depression recognition using convolutional neural network","volume":"57","author":"Li","year":"2019","journal-title":"Med Biol Eng Comput"},{"key":"2025021207195199400_ref24","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TBME.2022.3193277","article-title":"FBMSNet: a filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding","volume":"70","author":"Liu","year":"2022","journal-title":"IEEE Trans Biomed Eng"},{"key":"2025021207195199400_ref25","doi-asserted-by":"publisher","first-page":"630","DOI":"10.3390\/brainsci12050630","article-title":"A depression prediction algorithm based on spatiotemporal feature of EEG signal","volume":"12","author":"Liu","year":"2022","journal-title":"Brain Sci"},{"key":"2025021207195199400_ref26","doi-asserted-by":"publisher","first-page":"056024","DOI":"10.1088\/1741-2552\/acf7f4","article-title":"SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification","volume":"20","author":"Liu","year":"2023","journal-title":"J Neural Eng"},{"key":"2025021207195199400_ref27","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/S0140-6736(09)60879-5","article-title":"Clinical diagnosis of depression in primary care: a meta-analysis","volume":"374","author":"Mitchell","year":"2009","journal-title":"Lancet"},{"key":"2025021207195199400_ref28","doi-asserted-by":"publisher","first-page":"103983","DOI":"10.1016\/j.ijmedinf.2019.103983","article-title":"A deep learning framework for automatic diagnosis of unipolar depression","volume":"132","author":"Mumtaz","year":"2019","journal-title":"Int J Med Inform"},{"key":"2025021207195199400_ref29","doi-asserted-by":"publisher","first-page":"e0171409","DOI":"10.1371\/journal.pone.0171409","article-title":"A wavelet-based technique to predict treatment outcome for major depressive disorder","volume":"12","author":"Mumtaz","year":"2017","journal-title":"PLoS One"},{"key":"2025021207195199400_ref30","volume-title":"Depression and other common mental disorders: global health estimates","author":"World Health Organization","year":"2017"},{"key":"2025021207195199400_ref31","doi-asserted-by":"publisher","first-page":"92630","DOI":"10.1109\/ACCESS.2019.2927121","article-title":"Multivariate pattern analysis of EEG-based functional connectivity: a study on the identification of depression","volume":"7","author":"Peng","year":"2019","journal-title":"IEEE Access"},{"key":"2025021207195199400_ref32","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1109\/SLT.2018.8639585","article-title":"Speaker recognition from raw waveform with SincNet","volume-title":"2018 IEEE Spoken Language Technology Workshop (SLT)","author":"Ravanelli","year":"2018"},{"key":"2025021207195199400_ref33","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s13246-020-00897-w","article-title":"Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals","volume":"43","author":"Saeedi","year":"2020","journal-title":"Phys Eng Sci Med"},{"key":"2025021207195199400_ref34","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum Brain Mapp"},{"key":"2025021207195199400_ref35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3053999","article-title":"DeprNet: a deep convolution neural network framework for detecting depression using EEG","volume":"70","author":"Seal","year":"2021","journal-title":"IEEE Trans Instrum Meas"},{"key":"2025021207195199400_ref36","doi-asserted-by":"publisher","first-page":"102393","DOI":"10.1016\/j.bspc.2020.102393","article-title":"DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression","volume":"66","author":"Sharma","year":"2021","journal-title":"Biomed Signal Process Control"},{"key":"2025021207195199400_ref37","doi-asserted-by":"publisher","first-page":"103612","DOI":"10.1016\/j.bspc.2022.103612","article-title":"LSDD-EEGNet: an efficient end-to-end framework for EEG-based depression detection","volume":"75","author":"Song","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"2025021207195199400_ref38","doi-asserted-by":"publisher","first-page":"110190","DOI":"10.1016\/j.knosys.2022.110190","article-title":"Automated accurate detection of depression using twin Pascal\u2019s triangles lattice pattern with EEG signals","volume":"260","author":"Tasci","year":"2023","journal-title":"Knowl-Based Syst"},{"key":"2025021207195199400_ref39","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2025021207195199400_ref40","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-46478-7_31","article-title":"A discriminative feature learning approach for deep face recognition","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Wen","year":"2016"},{"key":"2025021207195199400_ref41","doi-asserted-by":"publisher","first-page":"41337","DOI":"10.1109\/ACCESS.2023.3270426","article-title":"An end-to-end deep learning model for EEG-based major depressive disorder classification","volume":"11","author":"Xia","year":"2023","journal-title":"IEEE Access"},{"key":"2025021207195199400_ref42","doi-asserted-by":"publisher","first-page":"122356","DOI":"10.1016\/j.eswa.2023.122356","article-title":"A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism","volume":"239","author":"Zhang","year":"2024","journal-title":"Expert Syst Appl"},{"key":"2025021207195199400_ref43","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.jad.2023.10.143","article-title":"Abnormal changes of dynamic topological characteristics in patients with major depressive disorder","volume":"345","author":"Zhou","year":"2024","journal-title":"J Affect Disord"},{"key":"2025021207195199400_ref44","doi-asserted-by":"publisher","first-page":"28196","DOI":"10.1109\/ACCESS.2019.2901950","article-title":"Multimodal mild depression recognition based on EEG-EM synchronization acquisition network","volume":"7","author":"Zhu","year":"2019","journal-title":"IEEE Access"}],"container-title":["Cerebral Cortex"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/cercor\/article-pdf\/35\/2\/bhae505\/61555541\/bhae505.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/cercor\/article-pdf\/35\/2\/bhae505\/61555541\/bhae505.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T07:20:03Z","timestamp":1739344803000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/cercor\/article\/doi\/10.1093\/cercor\/bhae505\/7972440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2,5]]}},"URL":"https:\/\/doi.org\/10.1093\/cercor\/bhae505","relation":{},"ISSN":["1047-3211","1460-2199"],"issn-type":[{"value":"1047-3211","type":"print"},{"value":"1460-2199","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,2]]},"published":{"date-parts":[[2025,1,22]]},"article-number":"bhae505"}}