{"doi":"10.3389/fpsyt.2023.1202049","title":"Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG","abstract":"Background: Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment. Methods: In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders. Results: Psychiatric disorders were identified using rsEEG with high accuracy ranged from 78.6 to 91.3% in patient vs. controls two-class classification, and 68.2% in four-class classification. The control-to-schizophrenia trajectory interpretated by the model was consistent with the disease severity in clinical observation. Conclusion: The MsRNN demonstrated a capability in extracting discriminative rsEEG biomarkers for psychiatric disorder classification, indicating its potential to facilitate our understanding of psychiatric disorders and monitoring interventions.","journal":"Frontiers in Psychiatry","year":2023,"id":330240,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9516,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":966332,"name":"Linzhen Yu","orcid":null,"position":1,"is_corresponding":false},{"id":1053887,"name":"Dandan Liu","orcid":"0000-0002-2090-9217","position":2,"is_corresponding":false},{"id":227750,"name":"Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you","orcid":"0000-0001-6837-5966","position":3,"is_corresponding":false},{"id":227761,"name":"Vince D. Calhoun","orcid":"0000-0001-9058-0747","position":4,"is_corresponding":false},{"id":1053888,"name":"Zheng Lin","orcid":"0000-0002-8432-1658","position":5,"is_corresponding":false},{"id":651315,"name":"Weizheng Yan","orcid":"0000-0002-0885-5631","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:09:10.120703Z","pmid":"37441141","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}