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Multivariate Pattern Analysis/Evidence
Method evidence record

Multivariate Pattern Analysis

Multivariate Pattern Analysis (MVPA) is a machine learning approach to fMRI that decodes cognitive states, stimuli, or behavior from whole-brain spatial patterns of neural activity. Pioneered by Haxby and colleagues in 2001, MVPA treats fMRI as a classification problem: can a trained decoder predict what a person is perceiving or thinking based solely on their brain activity pattern?

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Multivariate Pattern Analysis (MVPA)
Taxonomic method record · process-pipeline / neuroimaging
  • Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. · DOI 10.1016/j.tics.2006.07.005
  • Haxby, J. V., Gobbini, M. I., Furey, M. L., et al. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430. · DOI 10.1126/science.1063736
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyGraph Brain Network Analysismachine-suggested · Relational suggestion, not evidence.Same method familyRepresentational Similarity Analysismachine-suggested · Relational suggestion, not evidence.Same method familyVoxel-Based Morphometrymachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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