神经影像数据分析的统计学习方法及其应用。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI:10.1146/annurev-biodatasci-020722-100353
Hongtu Zhu, Tengfei Li, Bingxin Zhao
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引用次数: 6

摘要

这篇综述的目的是对神经成像数据分析中的统计挑战进行全面的调查,从神经成像技术到大规模神经成像研究和统计学习方法。我们简要回顾了八种流行的神经成像技术及其在神经科学研究和临床翻译中的潜在应用。我们描述了神经成像数据的四个主题,并回顾了在个体水平上处理神经成像数据的主要图像处理分析方法。我们简要回顾了四项大型神经成像相关研究和一个成像基因组学联盟,并讨论了人群水平上神经成像数据分析的四个主题。我们回顾了九种主要的基于人口的统计分析方法及其相关的统计挑战,并介绍了统计方法的最新进展,以应对这些挑战。
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Statistical Learning Methods for Neuroimaging Data Analysis with Applications.

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four 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 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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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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