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功能磁共振成像中的多元空间特征选择。

Multivariate spatial feature selection in fMRI.

机构信息

Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, Hanover, NH 03755, USA.

出版信息

Soc Cogn Affect Neurosci. 2021 Aug 5;16(8):795-806. doi: 10.1093/scan/nsab010.

Abstract

Multivariate neuroimaging analyses constitute a powerful class of techniques to identify psychological representations. However, not all psychological processes are represented at the same spatial scale throughout the brain. This heterogeneity is apparent when comparing hierarchically organized local representations of perceptual processes to flexible transmodal representations of more abstract cognitive processes such as social and affective operations. An open question is how the spatial scale of analytic approaches interacts with the spatial scale of the representations under investigation. In this article, we describe how multivariate analyses can be viewed as existing on a spatial spectrum, anchored by searchlights used to identify locally distributed patterns of information on one end, whole brain approach used to identify diffuse neural representations at the other and region-based approaches in between. We describe how these distinctions are an important and often overlooked analytic consideration and provide heuristics to compare these different techniques to choose based on the analyst's inferential goals.

摘要

多变量神经影像学分析是一种强大的技术,可以识别心理表现。然而,并非所有的心理过程在大脑中都以相同的空间尺度表现出来。当比较知觉过程的层次化局部表现与更抽象的认知过程(如社会和情感操作)的灵活跨模态表现时,这种异质性是显而易见的。一个悬而未决的问题是分析方法的空间尺度如何与所研究的表现的空间尺度相互作用。在本文中,我们描述了如何将多元分析视为存在于一个空间频谱上,一端是用于识别局部分布信息的搜索灯,另一端是用于识别弥散神经表现的全脑方法,而基于区域的方法则处于两者之间。我们描述了这些区别是一个重要且经常被忽视的分析考虑因素,并提供了启发式方法来比较这些不同的技术,以便根据分析师的推理目标进行选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/997f/8343556/cd993513bba3/nsab010f1.jpg

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