Mohammad Arafat Hussain
*
Patricia Ellen Grant
Yangming Ou
*The final, formatted version of the article will be published soon.
REVIEW article
Front. Neuroimaging
Sec. Neuroimaging for Cognitive Neuroscience
Volume 3 - 2024 |
doi: 10.3389/fnimg.2024.1455436
Inferring Neurocognition Using Artificial Intelligence on Brain MRIs
Provisionally accepted- Boston Children's Hospital, Harvard Medical School, Boston, United States
Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.
Keywords: neurocognition, artificial intelligence, brain MRI, Intelligence, P-FIT model
Received: 26 Jun 2024; Accepted: 07 Nov 2024.
Copyright: © 2024 Hussain, Grant and Ou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Mohammad Arafat Hussain, Boston Children's Hospital, Harvard Medical School, Boston, United States
Yangming Ou, Boston Children's Hospital, Harvard Medical School, Boston, United States
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