Leveraging unsupervised machine learning to map neural mechanisms of psychopathology: a review and user guide
Richier, Corey J.
Loading…
Permalink
https://hdl.handle.net/2142/113211
Description
Title
Leveraging unsupervised machine learning to map neural mechanisms of psychopathology: a review and user guide
Author(s)
Richier, Corey J.
Issue Date
2021-07-20
Director of Research (if dissertation) or Advisor (if thesis)
Heller, Wendy
Koyejo, Sanmi
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine learning
psychopathology
neuroimaging
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
