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在脑成像遗传学中的统计和机器学习分析:对方法的审查

Connor L Cheek^1,2, Peggy Lindner^3,4, Elena L Grigorenko^3,5,6,7

¹Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA. connor.cheek@times.uh.edu.

Behavior genetics

|February 9, 2024

概括

脑成像遗传分析整合了脑部扫描和遗传数据,以了解大脑功能和疾病. 本综述涵盖了从单变量到深度学习的方法,强调了这个复杂的研究领域的挑战和未来方向.

科学领域:

神经科学是一个神经科学.
遗传学遗传学是一个
计算生物学计算生物学

背景情况:

脑成像遗传分析结合了神经成像和基因组数据,以阐明大脑功能和疾病.
了解大脑疾病的遗传基础对于开发有效治疗方法至关重要.
多式联网数据集中的高维度和噪声带来了重大的分析挑战.

研究的目的:

审查大脑成像遗传分析方法的演变.
突出各种分析方法的优缺点.
讨论当前的挑战和该领域的未来前景.

主要方法:

审查从大规模单变量分析到深度学习的方法.
对高维,多模式数据集的数据融合技术的分析.
在综合分析中评估统计信号噪声比挑战.

主要成果:

从早期的单变量方法到先进的深度学习方法的进展已被记录下来.
确定了每个方法阶段的主要优势和局限性.
该评论综合了脑成像遗传分析的发展轨迹.

结论:

脑成像遗传分析对理解大脑疾病有很大的潜力.
克服数据维度和噪声仍然是一个关键的挑战.
未来的研究应该专注于先进的计算方法和综合分析策略.

关键词:

大脑成像遗传研究研究大脑成像.大脑成像基因组学深度学习是一种深度学习.机器学习机器学习方法论方法论方法论统计分析统计分析

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