{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T06:44:08Z","timestamp":1792046648806,"version":"4.4.0"},"reference-count":35,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"name":"National Recovery and Resilience Plan for Romania","award":["760246\/28.12.2023\/28.12.2023"],"award-info":[{"award-number":["760246\/28.12.2023\/28.12.2023"]}]},{"name":"National Recovery and Resilience Plan for Romania","award":["PNRR-III-C9-2023-I8-CF103\/31.07.2023"],"award-info":[{"award-number":["PNRR-III-C9-2023-I8-CF103\/31.07.2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2027,3]]},"abstract":"<jats:p>Mental illness is becoming increasingly common in everyday life, affecting a growing number of people. Functional Magnetic Resonance Imaging (fMRI) is an effective technique for detecting mental disorders. Resting-state fMRI (rs-fMRI) analyses spontaneous low-frequency oscillations in the Blood Oxygen Level Dependent signal to examine the functional architecture of the brain. fMRI and rs-fMRI data processing contribute to mapping neural activity in specific brain regions, enabling accurate localization of cognitive functions and improving our understanding of Functional Connectivity and interactions among brain regions. This paper focuses on the use of Machine Learning and Deep Learning techniques for fMRI analysis to identify brain features that may be associated with Bipolar Disorder (BD) and Alzheimer\u2019s Disease (AD). The first step in the proposed approach is the preprocessing of fMRI and rs-fMRI data using two distinct methods: the first combines Statistical Parametric Mapping and the CONN toolbox for improved results, and the second focuses on the MELODIC software. The proposed approach begins with fMRI and rs-fMRI preprocessing using two pipelines: one integrating Statistical Parametric Mapping (SPM) with the CONN toolbox and another based on the MELODIC framework. The aim is to evaluate how preprocessing choices affect functional connectivity representations and classification performance when combined with classical Machine Learning (ML) methods, namely Support Vector Machines and Random Forests, and the Deep Learning (DL) model AlexNet. Given the altered brain connectivity patterns observed in Bipolar Disorder and Alzheimer\u2019s Disease, these methods are employed to assess preprocessing-learning interactions across both disorders. Experimental results emphasize that Connbox-SPM preprocessing combined with AlexNet yields the best results for both mental disorders, with Bipolar Disorder reporting 78.2% accuracy and AD achieving 82.4% accuracy.<\/jats:p>","DOI":"10.1142\/s0129065727500158","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T06:19:01Z","timestamp":1785478741000},"source":"Crossref","is-referenced-by-count":0,"title":["Neuroimaging Biomarkers for Brain Disorder Diagnosis Using Advanced Learning Techniques"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7122-0884","authenticated-orcid":false,"given":"Iulia-Andreea","family":"Ion","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Babes-Bolyai University, Mihail Kogalniceanu nr. 1 Cluj-Napoca 400084, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-1298","authenticated-orcid":false,"given":"Camelia","family":"Chira","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Babes-Bolyai University, Mihail Kogalniceanu nr. 1 Cluj-Napoca 400084, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"S0129065727500158BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(15)00241-X"},{"key":"S0129065727500158BIB002","doi-asserted-by":"publisher","DOI":"10.1186\/s40345-019-0160-1"},{"key":"S0129065727500158BIB003","doi-asserted-by":"publisher","DOI":"10.1177\/155005940503600303"},{"key":"S0129065727500158BIB004","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-2005-7301"},{"key":"S0129065727500158BIB005","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2025-0062"},{"key":"S0129065727500158BIB006","volume-title":"Introduction to Neuroimaging Analysis","author":"Jenkinson M.","year":"2018"},{"key":"S0129065727500158BIB007","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500023"},{"key":"S0129065727500158BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-819410-2.00024-2"},{"key":"S0129065727500158BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108351"},{"key":"S0129065727500158BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.pscychresns.2026.112146"},{"key":"S0129065727500158BIB011","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2016-0029"},{"key":"S0129065727500158BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.pscychresns.2026.112151"},{"key":"S0129065727500158BIB013","first-page":"648","volume-title":"Proc. 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