How Machine Learning is Powering Neuroimaging to Improve Brain Health.
Brain health
Clinical translational neuroimaging
Deep learning
EEG
MRI
Machine learning
PET
Transcranial magnetic stimulation
Journal
Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
accepted:
07
02
2022
pubmed:
30
3
2022
medline:
26
10
2022
entrez:
29
3
2022
Statut:
ppublish
Résumé
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
Identifiants
pubmed: 35347570
doi: 10.1007/s12021-022-09572-9
pii: 10.1007/s12021-022-09572-9
pmc: PMC9515245
mid: NIHMS1805522
doi:
Types de publication
Journal Article
Review
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
943-964Subventions
Organisme : NIMH NIH HHS
ID : K23 MH121657
Pays : United States
Organisme : NIMH NIH HHS
ID : L30 MH127717
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102190
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS107291
Pays : United States
Informations de copyright
© 2022. The Author(s).
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