神经影像学
数据科学
虚假关系
情感(语言学)
转录组
计算机科学
心理学
神经科学
计算生物学
生物
机器学习
基因
基因表达
沟通
生物化学
作者
Aurina Arnatkevičiūtė,Ross D. Markello,Ben Fulcher,Bratislav Mišić,Alex Fornito
标识
DOI:10.1016/j.biopsych.2022.10.016
摘要
Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.
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