利用人工智能在脑核磁共振成像上推断神经认知。

Frontiers in neuroimaging Pub Date : 2024-11-27 eCollection Date: 2024-01-01 DOI:10.3389/fnimg.2024.1455436
Mohammad Arafat Hussain, Patricia Ellen Grant, Yangming Ou
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引用次数: 0

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。一个核心的谜团是MRI对神经认知的个体差异及其在智力上的表现的解释。在过去的40年里,人们对这个长达一个世纪的谜团的研究取得了巨大的进展,但样本量和人口水平的研究限制了对个体水平的解释。最近大数据和人工智能的兴起提供了新的机会。然而,必须仔细考虑数据来源、协调、研究设计和解释。本文旨在总结过去的工作,讨论未来的机遇和挑战,并促进人工智能推断人类神经认知的进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inferring neurocognition using artificial intelligence on brain MRIs.

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.

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