林李泽强
(2022-10-31 23:29):
#paper doi：arxiv.org/abs/2210.09217 Statistical learning methods for neuroimaging data analysis with applications
这是一篇尚未发布得预印本，作者是具有统计学背景的研究人员。
在这篇文章中，作者从统计学的角度全面回顾了从神经成像技术到大规模神经成像研究再到统计学习方法中的统计问题。
文中有三个主要的内容:(1)从统计学视角看待和综述影像处理方法；(2)介绍了当前最前沿的几个神经成像数据集；(3)从统计学视角介绍了9类影像数据的统计方法。
这篇文章从统计学的角度讲述神经成像领域的问题，适合具有数理背景的作为领域入门读物，当然也适合其他背景的研究人员站在统计学角度看待神经成像数据分析中的问题。
arXiv,
2022.
Statistical learning methods for neuroimaging data analysis with applications
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Abstract:
The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate the four common themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four common themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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