{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:27:41Z","timestamp":1784543261138,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The study was supported by budgetary funding for basic scientific research at the Institute of Neurosciences and Medicine","award":["No. 122042700001-9"],"award-info":[{"award-number":["No. 122042700001-9"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Processing electroencephalogram (EEG) data using neural networks is becoming increasingly important in modern medicine. This study introduces the development of a neural network method using a combination of psychological questionnaire data and spectral characteristics of resting-state EEG. The data were collected from 71 individuals: 42 healthy and 29 with major depressive disorder (MDD). We evaluated four classes of algorithms\u2014traditional machine learning, deep learning (LSTM), ablation analysis, and feature importance analysis\u2014for two primary tasks: binary classification (healthy vs. MDD) and regression for predicting Beck Depression Inventory scores (BDI). Our results demonstrate that the superiority of a given method is task-dependent. For regression, an LSTM network applied to delta-rhythm EEG data achieved a breakthrough performance of R2 = 0.742 (MAE = 6.114), representing an 86% improvement over traditional Ridge regression. Ablation studies identified delta and alpha rhythms as the most informative neurophysiological biomarkers. Furthermore, feature importance analysis revealed a triad of dominant psychometric predictors: ruminative thinking (31.2%), age (27.9%), and hostility (18.5%), which collectively accounted for 75.2% of the feature importance in predicting severity. LSTM on spectral EEG data provides a superior quantitative assessment of depression severity, while Logistic Regression on psychometric or EEG data offers a highly reliable tool for screening and confirmatory diagnosis. This methodology provides a robust, objective framework for MDD diagnosis that is independent of a patient\u2019s subjective self-assessment, thus facilitating enhanced clinical decision-making and personalized treatment monitoring.<\/jats:p>","DOI":"10.3390\/bioengineering12111251","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T10:24:58Z","timestamp":1763375098000},"page":"1251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7961-5531","authenticated-orcid":false,"given":"Igor","family":"Kozulin","sequence":"first","affiliation":[{"name":"Faculty of Information Technologies and Mechanical-Mathematical Faculty, Novosibirsk State University, 630090 Novosibirsk, Russia"},{"name":"Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekaterina","family":"Merkulova","sequence":"additional","affiliation":[{"name":"Scientific Research Institute of Neurosciences and Medicine, 630117 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasiliy","family":"Savostyanov","sequence":"additional","affiliation":[{"name":"Scientific Research Institute of Neurosciences and Medicine, 630117 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1349-586X","authenticated-orcid":false,"given":"Haonan","family":"Shi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Mechanical-Mathematical Faculty, Novosibirsk State University, 630090 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Mechanical-Mathematical Faculty, Novosibirsk State University, 630090 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2841-3280","authenticated-orcid":false,"given":"Andrey","family":"Bocharov","sequence":"additional","affiliation":[{"name":"Scientific Research Institute of Neurosciences and Medicine, 630117 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3514-2901","authenticated-orcid":false,"given":"Alexander","family":"Savostyanov","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Mechanical-Mathematical Faculty, Novosibirsk State University, 630090 Novosibirsk, Russia"},{"name":"Scientific Research Institute of Neurosciences and Medicine, 630117 Novosibirsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1002\/wps.20550","article-title":"Digital phenotyping: A global tool for psychiatry","volume":"17","author":"Insel","year":"2018","journal-title":"World Psychiatry"},{"key":"ref_2","first-page":"14","article-title":"Brain disorders? 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