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Multivariate spatial feature selection in fMRI.

E Jolly1, L J Chang1

  • 1Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, Hanover, NH 03755, USA.

Social Cognitive and Affective Neuroscience
|January 27, 2021
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Summary
This summary is machine-generated.

Multivariate neuroimaging analyses can reveal brain representations at different spatial scales. Choosing the right analysis method, like searchlight or whole-brain, is crucial for understanding these brain representations effectively.

Keywords:
biomarkerdecodingfMRIfeature-selectionmultivariatesearchlight

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Brain Representation Analysis

Background:

  • Multivariate neuroimaging techniques are key to identifying psychological representations in the brain.
  • Psychological processes vary in spatial scale, from local perceptual to abstract cognitive operations.
  • The interaction between analytical spatial scale and representational spatial scale remains an open question.

Purpose of the Study:

  • To explore how different spatial scales of multivariate neuroimaging analyses interact with the spatial scales of neural representations.
  • To categorize multivariate analysis approaches along a spatial spectrum.
  • To provide guidance on selecting appropriate analytical techniques based on research goals.

Main Methods:

  • Conceptualizing multivariate neuroimaging analyses on a spatial spectrum.
  • Comparing searchlight (local), whole-brain (diffuse), and region-based (intermediate) approaches.
  • Discussing the implications of spatial scale in neuroimaging analysis.

Main Results:

  • Multivariate analyses exist on a continuum from local (searchlight) to diffuse (whole-brain) spatial scales.
  • Region-based approaches represent an intermediate spatial scale.
  • The choice of analytical spatial scale significantly impacts the identification of neural representations.

Conclusions:

  • The spatial scale of neuroimaging analysis is a critical, often overlooked, factor.
  • Selecting the appropriate analytical spatial scale is essential for accurate interpretation of brain representations.
  • Heuristics are provided to aid researchers in choosing methods aligned with their inferential aims.