Machine Learning and Brain Imaging: Opportunities and Challenges.

Creator(s)

Martin P Paulus
Rayus Kuplicki
Hung-Wen Yeh, Children's Mercy HospitalFollow

Document Type

Response or Comment

Publication Date

10-2019

Identifier

DOI: 10.1016/j.tins.2019.07.007

Abstract

Machine learning approaches may provide ways to link brain activation patterns to behavior at an individual-subject level. Using a comparative performance analysis, Jollans and colleagues (Neuroimage, 2019) highlight in a recent paper key considerations when applying machine learning algorithms to neuroimaging data.

Journal Title

Trends in neurosciences

Volume

42

Issue

10

First Page

659

Last Page

661

MeSH Keywords

Algorithms; Brain; Machine Learning; Neuroimaging

Keywords

machine learning; neuroimaging; prediction

Comments

Comment on

• Quantifying performance of machine learning methods for neuroimaging data. Jollans L, Boyle R, Artiges E, Banaschewski T, Desrivières S, Grigis A, Martinot JL, Paus T, Smolka MN, Walter H, Schumann G, Garavan H, Whelan R.Neuroimage. 2019 Oct 1;199:351-365. doi: 10.1016/j.neuroimage.2019.05.082. Epub 2019 Jun 5.PMID: 31173905 Free PMC article.

Grant support

• R01 MH101453/MH/NIMH NIH HHS/United States
• U01 DA041089/DA/NIDA NIH HHS/United States
• P20 GM121312/GM/NIGMS NIH HHS/United States

Recommended Citation

Paulus MP, Kuplicki R, Yeh HW. Machine Learning and Brain Imaging: Opportunities and Challenges. Trends Neurosci. 2019;42(10):659-661. doi:10.1016/j.tins.2019.07.007