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RELIEF:一个结构化的多变量方法来消除隐藏的扫描器间效应

Rongqian Zhang¹, Lindsay D Oliver², Aristotle N Voineskos^2,3

¹Department of Statistical Sciences, University of Toronto, Toronto, Canada.

Imaging neuroscience (Cambridge, Mass.)

|September 18, 2023

概括

研究人员开发了RELIEF,这是一种新的多变量方法,用于消除神经成像数据中的扫描器间偏差. 这种方法增强了数据协调,提高了多地点研究的可靠性和统计能力.

科学领域:

神经成像是一种神经成像.
生物统计学生物统计学
数据科学数据科学数据科学

背景情况:

来自多个站点的神经成像数据的结合增加了概括性,但引入了扫描器间的偏见.
现有的单变量协调方法可能会过度简化复杂的扫描仪特定变量.
可靠的多站点数据集成对于强大的科学发现至关重要.

研究的目的:

引入RELIEF (通过因子化去除潜在的扫描器间效应),一种新的多变量协调方法.
通过估计和消除显式和隐式扫描效应来解决现有方法的局限性.
为纠正神经成像中扫描器间偏差提供一种新的方法方向.

主要方法:

RELIEF采用了相互连接的矩阵的同时尺寸缩小和因数分解.
该方法应用于来自SPINS研究的扩散张力成像 (DTI) 数据.
为了验证该方法的性能,进行了广泛的模拟研究.

主要成果:

在神经成像数据中,RELIEF有效地减轻了扫描器间偏差.
该方法成功地保留了生物相关的关联.
与现有的协调技术相比,RELIEF表现出更高的性能,增加了统计能力.

结论:

RELIEF提供了一种强大的新方法来协调多站点神经成像数据.
该方法提高了数据可靠性和研究的统计能力.
RELIEF是公开提供作为一个R包,用于更广泛的科学用途.

关键词:

帮助救济帮助救济批量效应批量效应批量效应协差异性异质性的异质性缩小尺寸缩小尺寸的方法扫描器之间的偏差神经成像是一种神经成像.

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