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利用人工智能在脑部磁共振成像上推断神经认知情况。

Inferring neurocognition using artificial intelligence on brain MRIs.

作者信息

Hussain Mohammad Arafat, Grant Patricia Ellen, Ou Yangming

机构信息

Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.

Department of Radiology, Harvard Medical School, Boston, MA, United States.

出版信息

Front Neuroimaging. 2024 Nov 27;3:1455436. doi: 10.3389/fnimg.2024.1455436. eCollection 2024.

DOI:10.3389/fnimg.2024.1455436
PMID:39664769
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11631947/
Abstract

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.

摘要

脑磁共振成像（MRI）为研究人类神经认知的神经解剖学支持提供了一个独特的视角。一个核心谜团是MRI对神经认知个体差异及其在智力方面表现的解释。在过去的四十年里，在研究这个长达一个世纪的谜团方面取得了巨大进展，但样本量和群体水平的研究限制了在个体层面的解释。最近大数据和人工智能的兴起提供了新的机遇。然而，必须仔细考虑数据来源、数据协调、研究设计和解释。本综述旨在总结过去的工作，讨论新出现的机遇和挑战，并促进对人工智能推断人类神经认知的进一步研究。

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