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Foundational Issues in Human Brain Mapping$
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Stephen Jose Hanson and Martin Bunzl

Print publication date: 2010

Print ISBN-13: 9780262014021

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262014021.001.0001

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Multivariate Pattern Analysis of fMRI Data: High-Dimensional Spaces for Neural and Cognitive Representations

Multivariate Pattern Analysis of fMRI Data: High-Dimensional Spaces for Neural and Cognitive Representations

Chapter:
(p.55) 5 Multivariate Pattern Analysis of fMRI Data: High-Dimensional Spaces for Neural and Cognitive Representations
Source:
Foundational Issues in Human Brain Mapping
Author(s):

James V. Haxby

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262014021.003.0006

This chapter focuses on the approaches to functional magnetic resonance imaging (fMRI) data analysis. It sketches the difference between the univariate analysis of fMRI data based on a general linear model and the multivariate pattern (MVP) analysis based on machine learning pattern classifiers. The comparison is based on assumptions about the functional architecture of the brain. The chapter further presents a detailed discussion on the MVP analysis, which is more sensitive than a conventional analysis and can also quantify the similarity of patterns of response. It also provides information on pattern classification of fMRI data and the model-based prediction.

Keywords:   fMRI, multivariate pattern, model-based prediction, pattern classification, univariate analysis, general linear model

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