Multivariate spatial feature selection in fMRI
Author(s) -
Eshin Jolly,
Luke J. Chang
Publication year - 2021
Publication title -
social cognitive and affective neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.229
H-Index - 103
eISSN - 1749-5024
pISSN - 1749-5016
DOI - 10.1093/scan/nsab010
Subject(s) - heuristics , multivariate statistics , psychology , cognitive psychology , cognition , perception , selection (genetic algorithm) , scale (ratio) , neuroimaging , artificial intelligence , multivariate analysis , computer science , machine learning , cartography , neuroscience , geography , operating system , psychiatry
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.
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