{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:28:09Z","timestamp":1775219289605,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T00:00:00Z","timestamp":1654560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brazil (CAPES)","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brazil (CAPES)","award":["APQ-01565-18"],"award-info":[{"award-number":["APQ-01565-18"]}]},{"DOI":"10.13039\/501100004901","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais (FAPEMIG)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100004901","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004901","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais (FAPEMIG)","doi-asserted-by":"publisher","award":["APQ-01565-18"],"award-info":[{"award-number":["APQ-01565-18"]}],"id":[{"id":"10.13039\/501100004901","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: \u2018autism\u2019, \u2018autism spectrum\u2019, and \u2018non-ASD\u2019. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.<\/jats:p>","DOI":"10.3390\/a15060195","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T09:59:38Z","timestamp":1654768778000},"page":"195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3076-8657","authenticated-orcid":false,"given":"Igor D.","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Institute of Systems Engineering and Information Technology, Federal University of Itajub\u00e1, Itajub\u00e1 37500-903, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5028-2243","authenticated-orcid":false,"given":"Emerson A.","family":"de Carvalho","sequence":"additional","affiliation":[{"name":"Institute of Systems Engineering and Information Technology, Federal University of Itajub\u00e1, Itajub\u00e1 37500-903, Brazil"},{"name":"IFSULDEMINAS, Computer Department, Machado 37750-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4447-1229","authenticated-orcid":false,"given":"Caio P.","family":"Santana","sequence":"additional","affiliation":[{"name":"Institute of Systems Engineering and Information Technology, Federal University of Itajub\u00e1, Itajub\u00e1 37500-903, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8280-2813","authenticated-orcid":false,"given":"Guilherme S.","family":"Bastos","sequence":"additional","affiliation":[{"name":"Institute of Systems Engineering and Information Technology, Federal University of Itajub\u00e1, Itajub\u00e1 37500-903, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2014). 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