{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:02:04Z","timestamp":1788786124901,"version":"build-2803163510"},"reference-count":57,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LTGY23H180015"],"award-info":[{"award-number":["LTGY23H180015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.<\/jats:p>","DOI":"10.3390\/brainsci13091282","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T02:14:50Z","timestamp":1693793690000},"page":"1282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6040-0108","authenticated-orcid":false,"given":"Xuchen","family":"Qi","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China"},{"name":"Department of Neurosurgery, Shaoxing People\u2019s Hospital, Shaoxing 312000, 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"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, X., Ren, L., Xue, X., Yu, N., Liu, P., Shen, W., Zhou, H., Wang, B., Zhou, J., and Yang, S. (2023). The Comorbidity of Depression and Anxiety Symptoms in Tinnitus Sufferers: A Network Analysis. Brain Sci., 13.","DOI":"10.3390\/brainsci13040583"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/hbm.25096","article-title":"Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group","volume":"43","author":"Zugman","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1002\/hbm.24902","article-title":"Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder","volume":"41","author":"Cui","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1002\/hbm.23113","article-title":"Differential alterations of resting-state functional connectivity in generalized anxiety disorder and panic disorder","volume":"37","author":"Cui","year":"2016","journal-title":"Hum. Brain Mapp."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jad.2018.02.012","article-title":"Generalized anxiety disorder in urban China: Prevalence, awareness, and disease burden","volume":"234","author":"Yu","year":"2018","journal-title":"J. Affect. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Siwek, M., Gorostowicz, A., Chrobak, A.A., Gerlich, A., Krupa, A.J., Juryk, A., and Dudek, D. (2023). TED-Trazodone Efficacy in Depression: A Naturalistic Study on the Efficacy of Trazodone in an Extended-Release Formulation Compared to SSRIs in Patients with a Depressive Episode-Preliminary Report. Brain Sci., 13.","DOI":"10.3390\/brainsci13010086"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1093\/ije\/dyt175","article-title":"Preventing the onset of major depressive disorder: A meta-analytic review of psychological interventions","volume":"43","author":"Buntrock","year":"2014","journal-title":"Int. J. Epidemiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105163","DOI":"10.1016\/j.neubiorev.2023.105163","article-title":"Fear-induced bradycardia in mental disorders: Foundations, current advances, future perspectives","volume":"149","author":"Battaglia","year":"2023","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"505","DOI":"10.17219\/acem\/165944","article-title":"Preclinical modeling in depression and anxiety: Current challenges and future research directions","volume":"32","author":"Tanaka","year":"2023","journal-title":"Adv. Clin. Exp. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cao, J., Garro, E.M., and Zhao, Y. (2022). EEG\/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning. Sensors, 22.","DOI":"10.3390\/s22197623"},{"key":"ref_11","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_12","first-page":"44","article-title":"Random Forest Model for Labor Induction in Pregnant Women with Hypertensive Disorders Using a Cervical Double Balloon","volume":"29","author":"Huang","year":"2023","journal-title":"Altern. Ther. Health Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.medengphy.2014.12.005","article-title":"Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation: Acoustic versus contact microphone","volume":"37","author":"Verikas","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.biopsycho.2008.04.005","article-title":"Worry, generalized anxiety disorder, and emotion: Evidence from the EEG gamma band","volume":"79","author":"Oathes","year":"2008","journal-title":"Biol. Psychol."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.nicl.2016.11.009","article-title":"Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder","volume":"13","author":"Xing","year":"2017","journal-title":"Neuroimage Clin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1002\/hbm.25994","article-title":"Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram","volume":"43","author":"Shan","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108319","DOI":"10.1016\/j.biopsycho.2022.108319","article-title":"Effect of trait anxiety on cognitive flexibility: Evidence from event-related potentials and resting-state EEG","volume":"170","author":"Du","year":"2022","journal-title":"Biol. Psychol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107506","DOI":"10.1016\/j.neuropsychologia.2020.107506","article-title":"Emotion recognition with convolutional neural network and EEG-based EFDMs","volume":"146","author":"Wang","year":"2020","journal-title":"Neuropsychologia"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.12998\/wjcc.v11.i9.1903","article-title":"Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression: A review","volume":"11","author":"Xie","year":"2023","journal-title":"World J. Clin. Cases"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Rong, W., Zhao, L., He, Z., Yang, Q., Sun, J., and Liu, G. (2022). EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features. Sensors, 22.","DOI":"10.3390\/s22145252"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1007\/s00415-021-10718-z","article-title":"An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features","volume":"269","author":"Wang","year":"2022","journal-title":"J. Neurol."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1016\/j.clinph.2018.09.028","article-title":"Resting frontal EEG asymmetry in adolescents with major depression: Impact of disease state and comorbid anxiety disorder","volume":"129","author":"Feldmann","year":"2018","journal-title":"Clin. Neurophysiol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahorsu, D.K., Chung, K., Wong, H.H., Yiu, M.G.C., Mok, Y.F., Lei, K.S., and Tsang, H.W.H. (2020). The Effects of Major Depressive Disorder on the Sequential Organization of Information Processing Stages: An Event-Related Potential Study. Brain Sci., 10.","DOI":"10.3390\/brainsci10120935"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1055\/s-0042-1756163","article-title":"A Home-Based Approach to Auditory Brainstem Response Measurement: Proof-of-Concept and Practical Guidelines","volume":"43","author":"Parker","year":"2022","journal-title":"Semin. Hear."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mikola, A., Sarkela, M.O., Walsh, T.S., and Lipping, T. (2019, January 23\u201327). Power Spectrum and Cross Power Spectral Density Based EEG Correlates of Intensive Care Delirium. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering In Medicine and Biology Society (embc), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857254"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108302","DOI":"10.1016\/j.biopsycho.2022.108302","article-title":"Reduced electrocortical responses to pleasant pictures in depression: A brief report on time-domain and time-frequency delta analyses","volume":"170","author":"Brush","year":"2022","journal-title":"Biol. Psychol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"095007","DOI":"10.1088\/1361-6579\/abb144","article-title":"Depression evaluation based on prefrontal EEG signals in resting state using fuzzy measure entropy","volume":"41","author":"Chen","year":"2020","journal-title":"Physiol. Meas."},{"key":"ref_30","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_31","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_32","first-page":"S125","article-title":"Optimal Methodology for the Identification of Resting-State EEG Correlates of Non-Clinical Depression and Anxiety","volume":"59","author":"Barry","year":"2022","journal-title":"Psychophysiology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ismail, L., Karwowski, W., Farahani, F.V., Rahman, M., Alhujailli, A., Fernandez-Sumano, R., and Hancock, P.A. (2022). Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach. Brain Sci., 12.","DOI":"10.3390\/brainsci12111575"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tabarelli, D., Brancaccio, A., Zrenner, C., and Belardinelli, P. (2022). Functional Connectivity States of Alpha Rhythm Sources in the Human Cortex at Rest: Implications for Real-Time Brain State Dependent EEG-TMS. Brain Sci., 12.","DOI":"10.3390\/brainsci12030348"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Ksibi, A., Zakariah, M., Menzli, L.J., Saidani, O., Almuqren, L., and Hanafieh, R.A.M. (2023). Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101779"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106741","DOI":"10.1016\/j.compbiomed.2023.106741","article-title":"Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review","volume":"159","author":"Yasin","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"913890","DOI":"10.3389\/fpsyt.2022.913890","article-title":"Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers","volume":"13","author":"Kim","year":"2022","journal-title":"Front. Psychiatry"},{"key":"ref_39","doi-asserted-by":"crossref","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."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1174005","DOI":"10.3389\/fnins.2023.1174005","article-title":"Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG","volume":"17","author":"Zhong","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2582478","DOI":"10.1155\/2016\/2582478","article-title":"Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke","volume":"2016","author":"Liu","year":"2016","journal-title":"Comput. Math. Method Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"046034","DOI":"10.1088\/1741-2552\/ac84ac","article-title":"Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer\u2019s disease","volume":"19","author":"Cao","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.neuroimage.2013.02.008","article-title":"A note on the phase locking value and its properties","volume":"74","author":"Aydore","year":"2013","journal-title":"Neuroimage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1002\/hbm.20346","article-title":"Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources","volume":"28","author":"Stam","year":"2007","journal-title":"Hum. Brain Mapp."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, J., Xu, Y., Tian, J., Li, H., Jiao, W., Sun, Y., and Li, G. (2022). Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model. Entropy, 24.","DOI":"10.3390\/e24121715"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/0167-8760(90)90020-E","article-title":"Event-related desynchronization, ERD-mapping and hemispheric differences for words and numbers","volume":"8","author":"Klimesch","year":"1990","journal-title":"Int. J. Psychophysiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.neuroscience.2005.10.029","article-title":"Grouping of brain rhythms in corticothalamic systems","volume":"137","author":"Steriade","year":"2006","journal-title":"Neuroscience"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"192","DOI":"10.3389\/fnins.2020.00192","article-title":"A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography","volume":"14","author":"Li","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"922207","DOI":"10.3389\/fneur.2022.922207","article-title":"Altered functional connectivity in first-episode and recurrent depression: A resting-state functional magnetic resonance imaging study","volume":"13","author":"Sun","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"191","DOI":"10.3389\/fnins.2020.00191","article-title":"Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach","volume":"14","author":"Yan","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_51","first-page":"574","article-title":"Study on the EEG functional brain connectivity characteristics of college students with depression","volume":"74","author":"Liang","year":"2022","journal-title":"Sheng Li Xue Bao"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"539","DOI":"10.3389\/fnhum.2018.00539","article-title":"Decreased Intrinsic Functional Connectivity in First-Episode, Drug-Naive Adolescents With Generalized Anxiety Disorder","volume":"12","author":"Yang","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102016","DOI":"10.1016\/j.nicl.2019.102016","article-title":"Systematic review and meta-analyses of neural structural and functional differences in generalized anxiety disorder and healthy controls using magnetic resonance imaging","volume":"24","author":"Kolesar","year":"2019","journal-title":"Neuroimage Clin."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e00633","DOI":"10.1002\/brb3.633","article-title":"Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study","volume":"7","author":"Hilbert","year":"2017","journal-title":"Brain Behav."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"114534","DOI":"10.1016\/j.psychres.2022.114534","article-title":"Predictive utility of symptom measures in classifying anxiety and depression: A machine-learning approach","volume":"312","author":"Liu","year":"2022","journal-title":"Psychiatry Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1038\/s41598-021-81368-4","article-title":"Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence","volume":"11","author":"Nemesure","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111582","DOI":"10.1016\/j.pscychresns.2022.111582","article-title":"Depression signal correlation identification from different EEG channels based on CNN feature extraction","volume":"328","author":"Wang","year":"2023","journal-title":"Psychiatry Res. Neuroimaging"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/13\/9\/1282\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:45:54Z","timestamp":1760129154000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/13\/9\/1282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":57,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["brainsci13091282"],"URL":"https:\/\/doi.org\/10.3390\/brainsci13091282","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,4]]}}}