{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113211"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113211","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Leveraging unsupervised machine learning to map neural mechanisms of psychopathology: a review and user guide","abstract":"Past and present neuroimaging studies of psychopathology have heavily relied upon general linear models to infer relationships between brain activity and symptoms. However, this work has resulted in little tangible benefit for clinical implementation. In the era of transdiagnostic approaches such as the Research Domain Criteria framework, a subfield of research has emerged which seeks to utilize unsupervised machine learning to map the relationships between neural activity and psychopathology agnostic of existing diagnostic categories. The ability of machine learning algorithms to extract patterns from large amounts of data may hold promise for deriving the underlying patterns that cut across psychiatric disorders. In the present review, we survey neuroimaging studies of psychopathology that have utilized unsupervised learning algorithms. We synthesize findings regarding the structure of psychopathology from results reported by these studies and compare these findings to established taxonomies. Considerations regarding study design and analytical methods are explored, including discussion of key assumptions made in current models. Future directions, as well as suggestions for researchers interested in these methods, are also discussed.","abstract_html":"Past and present neuroimaging studies of psychopathology have heavily relied upon general linear models to infer relationships between brain activity and symptoms. However, this work has resulted in little tangible benefit for clinical implementation. In the era of transdiagnostic approaches such as the Research Domain Criteria framework, a subfield of research has emerged which seeks to utilize unsupervised machine learning to map the relationships between neural activity and psychopathology agnostic of existing diagnostic categories. The ability of machine learning algorithms to extract patterns from large amounts of data may hold promise for deriving the underlying patterns that cut across psychiatric disorders. In the present review, we survey neuroimaging studies of psychopathology that have utilized unsupervised learning algorithms. We synthesize findings regarding the structure of psychopathology from results reported by these studies and compare these findings to established taxonomies. Considerations regarding study design and analytical methods are explored, including discussion of key assumptions made in current models. Future directions, as well as suggestions for researchers interested in these methods, are also discussed.","abstract_has_math":false,"creators":["Richier, Corey J."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Heller, Wendy","Koyejo, Sanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:35:19Z","date_published":"2022-01-12T22:35:19Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Machine learning","psychopathology","neuroimaging"],"languages":["en"],"rights":["Copyright 2021 Corey Richier"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113211","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Heller, Wendy","Koyejo, Sanmi"]},{"key":"dc:creator","label":"Author","values":["Richier, Corey J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:35:19Z","2024-01-12T22:35:30Z","2021-07-20","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning","psychopathology","neuroimaging"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Corey Richier"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113211"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Past and present neuroimaging studies of psychopathology have heavily relied upon general linear models to infer relationships between brain activity and symptoms. 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Future directions, as well as suggestions for researchers interested in these methods, are also discussed.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Corey Richier, accepted the attached license on 2021-07-16 at 09:49.","The student, Corey Richier, submitted this Thesis for approval on 2021-07-16 at 10:01.","This Thesis was approved for publication on 2021-07-20 at 07:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16980 on 2022-01-12 at 12:55:25","Made available in DSpace on 2022-01-12T22:35:19Z (GMT). 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