{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T22:27:07Z","timestamp":1790807227165,"version":"4.1.0"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jinhua Key Science and Technology Program Projects","award":["2023-3-154"],"award-info":[{"award-number":["2023-3-154"]}]},{"name":"Jinhua Key Science and Technology Program Projects","award":["2023-3-157"],"award-info":[{"award-number":["2023-3-157"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Depressive disorder (DD) and generalized anxiety disorder (GAD), two prominent mental health conditions, are commonly diagnosed using subjective methods such as scales and interviews. Previous research indicated that machine learning (ML) can enhance our understanding of their underlying mechanisms. This study seeks to investigate the mechanisms of DD, GAD, and healthy controls (HC) while constructing a diagnostic framework for triple classifications. Specifically, the experiment involved collecting electroencephalogram (EEG) signals from 42 DD patients, 45 GAD patients, and 38 HC adults. The Phase Lag Index (PLI) was employed to quantify brain functional connectivity and analyze differences in functional connectivity among three groups. This study also explored the impact of time window feature computations on classification performance, including the XGBoost, CatBoost, LightGBM, and ensemble models. In order to enhance classification performance, a feature optimization algorithm based on Autogluon-Tabular was proposed. The results indicate that a 12 s time window provides optimal classification performance for the three groups, achieving the highest accuracy of 97.33% with the ensemble model. The analysis further reveals a significant reorganization of the brain, with the most pronounced changes observed in the frontal lobe and beta rhythm. These findings support the hypothesis of abnormal brain functional connectivity in DD and GAD, contributing valuable insights into the neural mechanisms underlying DD and GAD.<\/jats:p>","DOI":"10.3390\/brainsci14030245","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T06:07:53Z","timestamp":1709273273000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Exploring Abnormal Brain Functional Connectivity in Healthy Adults, Depressive Disorder, and Generalized Anxiety Disorder through EEG Signals: A Machine Learning Approach for Triple Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiaqi","family":"Fang","sequence":"first","affiliation":[{"name":"College of Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1957-471X","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1496-1745","authenticated-orcid":false,"given":"Wanxiu","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guibin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixia","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Second Hospital of Jinhua, Jinhua 321016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youdong","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Luo","sequence":"additional","affiliation":[{"name":"The Second Hospital of Jinhua, Jinhua 321016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","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":"ref_2","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1001\/jamapsychiatry.2017.0056","article-title":"Cross-sectional Comparison of the Epidemiology of DSM-5 Generalized Anxiety Disorder across the Globe","volume":"74","author":"Ruscio","year":"2017","journal-title":"JAMA Psychiatry"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1016\/S0140-6736(21)02143-7","article-title":"Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic","volume":"398","author":"Santomauro","year":"2021","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1176\/ajp.2007.164.6.942","article-title":"The worldwide prevalence of ADHD: A systematic review and metaregression analysis","volume":"164","author":"Polanczyk","year":"2007","journal-title":"Am. J. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jad.2023.01.126","article-title":"Alterations in EEG functional connectivity in individuals with depression: A systematic review","volume":"328","author":"Miljevic","year":"2023","journal-title":"J. Affect. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"American Psychiatric Publishing, Inc. (2013). Diagnostic and Statistical Manual of Mental Disorders: DSM-5\u2122, American Psychiatric Publishing, Inc.. [5th ed.].","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1037\/a0034733","article-title":"Comorbidity of Anxiety and Depression in Children and Adolescents: 20 Years After","volume":"140","author":"Cummings","year":"2014","journal-title":"Psychol. Bull."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"521","DOI":"10.3389\/fnhum.2018.00521","article-title":"EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies","volume":"12","author":"Newson","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12666","DOI":"10.1007\/s10489-022-04159-y","article-title":"Benchmarks for machine learning in depression discrimination using electroencephalography signals","volume":"53","author":"Seal","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"VII","DOI":"10.1682\/JRRD.2010.02.0017","article-title":"Functional imaging and related techniques: An introduction for rehabilitation researchers","volume":"47","author":"Crosson","year":"2010","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, G., Zhong, H., Wang, J., Yang, Y., Li, H., Wang, S., Sun, Y., and Qi, X. (2023). Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression. Brain Sci., 13.","DOI":"10.3390\/brainsci13030384"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qi, X., Xu, W., and Li, G. (2023). Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder. Brain Sci., 13.","DOI":"10.3390\/brainsci13091282"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qi, X., Fang, J., Sun, Y., Xu, W., and Li, G. (2023). Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder. Diagnostics, 13.","DOI":"10.3390\/diagnostics13071292"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11571-014-9325-x","article-title":"Power spectral density and coherence analysis of Alzheimer\u2019s EEG","volume":"9","author":"Wang","year":"2015","journal-title":"Cogn. Neurodyn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"078704","DOI":"10.7498\/aps.63.078704","article-title":"Multiscale permutation entropy analysis of electroencephalogram","volume":"63","author":"Yao","year":"2014","journal-title":"Acta Phys. Sin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jneumeth.2012.04.007","article-title":"Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index","volume":"207","author":"Peraza","year":"2012","journal-title":"J. Neurosci. Methods"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1074587","DOI":"10.3389\/fnhum.2022.1074587","article-title":"Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder","volume":"16","author":"Wang","year":"2022","journal-title":"Front. Hum. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jad.2019.07.026","article-title":"The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms","volume":"257","author":"Wang","year":"2019","journal-title":"J. Affect. Disord."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/1087054712460087","article-title":"A Decade of EEG Theta\/Beta Ratio Research in ADHD: A Meta-Analysis","volume":"17","author":"Arns","year":"2013","journal-title":"J. Atten. Disord."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"056014","DOI":"10.1088\/1741-2552\/aacdbb","article-title":"Altered electroencephalography functional connectivity in depression during the emotional face-word Stroop task","volume":"15","author":"Guo","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shen, Z., Li, G., Fang, J., Zhong, H., Wang, J., Sun, Y., and Shen, X. (2022). Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework. Sensors, 22.","DOI":"10.3390\/s22145420"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1109\/TNSRE.2022.3181690","article-title":"Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals","volume":"30","author":"Sun","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jpsychires.2022.03.027","article-title":"Polysomnographic identification of anxiety and depression using deep learning","volume":"150","author":"Thakre","year":"2022","journal-title":"J. Psychiatr. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1049\/htl.2016.0096","article-title":"Predicting anxiety and depression in elderly patients using machine learning technology","volume":"4","author":"Sau","year":"2017","journal-title":"Health Technol. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iyortsuun, N.K., Kim, S.H., Jhon, M., Yang, H.J., and Pant, S. (2023). A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare, 11.","DOI":"10.3390\/healthcare11030285"},{"key":"ref_26","first-page":"3149","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_28","first-page":"6639","article-title":"CatBoost: Unbiased boosting with categorical features","volume":"31","author":"Prokhorenkova","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/THMS.2023.3275626","article-title":"Emotion Recognition through Combining EEG and EOG over Relevant Channels with Optimal Windowing","volume":"53","author":"Cai","year":"2023","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.jad.2022.01.105","article-title":"Consistency checks to improve measurement with the Hamilton Rating Scale for Depression (HAM-D)","volume":"302","author":"Rabinowitz","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref_31","unstructured":"Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A. (2020). Autogluon-tabular: Robust and accurate automl for structured data. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104249","DOI":"10.1016\/j.catena.2019.104249","article-title":"Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment","volume":"186","author":"Sameen","year":"2020","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ouyang, D., Yuan, Y., Li, G., and Guo, Z. (2022). The Effect of Time Window Length on EEG-Based Emotion Recognition. Sensors, 22.","DOI":"10.3390\/s22134939"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TAFFC.2018.2854168","article-title":"Empirical Evidence Relating EEG Signal Duration to Emotion Classification Performance","volume":"12","author":"Pereira","year":"2021","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","article-title":"EEG-Based Emotion Recognition in Music Listening","volume":"57","author":"Lin","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yu, X., Li, Z., Zang, Z., and Liu, Y. (2023). Real-Time EEG-Based Emotion Recognition. Sensors, 23.","DOI":"10.3390\/s23187853"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhuang, N., Zeng, Y., Tong, L., Zhang, C., Zhang, H., and Yan, B. (2017). Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain. BioMed Res. Int., 2017.","DOI":"10.1155\/2017\/8317357"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3389\/fnbot.2019.00037","article-title":"SAE plus LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG","volume":"13","author":"Xing","year":"2019","journal-title":"Front. Neurorobot."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"607","DOI":"10.17219\/acem\/166476","article-title":"Advances in EEG-based functional connectivity approaches to the study of the central nervous system in health and disease","volume":"32","author":"Battaglia","year":"2023","journal-title":"Adv. Clin. Exp. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e01867","DOI":"10.7554\/eLife.01867","article-title":"Fast transient networks in spontaneous human brain activity","volume":"3","author":"Baker","year":"2014","journal-title":"eLife"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/S1474-4422(13)70264-3","article-title":"Connectivity-based approaches in stroke and recovery of function","volume":"13","author":"Grefkes","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yasin, S., Othmani, A., Raza, I., and Hussain, S.A. (2023). Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review. Comput. Biol. Med., 159.","DOI":"10.1016\/j.compbiomed.2023.106741"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xie, Y., Yang, B., Lu, X., Zheng, M., Fan, C., Bi, X., Zhou, S., and Li, Y. (2020, January 20\u201324). Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176471"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1038\/s41386-021-01101-7","article-title":"Prefrontal cortex and depression","volume":"47","author":"Pizzagalli","year":"2022","journal-title":"Neuropsychopharmacology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1016\/j.tics.2023.05.006","article-title":"Feature Review Functional connectomics in depression: Insights into therapies","volume":"27","author":"Chai","year":"2023","journal-title":"Trends Cogn. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.bpsgos.2021.11.004","article-title":"Differential Impact of Anxious Misery Psychopathology on Multiple Representations of the Functional Connectome","volume":"2","author":"Seok","year":"2022","journal-title":"Biol. Psychiatry Glob. Open Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1159\/000356964","article-title":"Functional Connectivity and Temporal Variability of Brain Connections in Adults with Attention Deficit\/Hyperactivity Disorder and Bipolar Disorder","volume":"69","author":"Barttfeld","year":"2014","journal-title":"Neuropsychobiology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/RBME.2020.2969915","article-title":"A Review on Machine Learning for EEG Signal Processing in Bioengineering","volume":"14","author":"Hosseini","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/14\/3\/245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:07:56Z","timestamp":1760105276000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/14\/3\/245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["brainsci14030245"],"URL":"https:\/\/doi.org\/10.3390\/brainsci14030245","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,1]]}}}