Literature DB >> 18985145

Modeling group fMRI data.

Jeanette A Mumford1, Russell A Poldrack.   

Abstract

The analysis of group fMRI data requires a statistical model known as the mixed effects model. This article motivates the need for a mixed effects model and outlines the different stages of the mixed model used to analyze group fMRI data. Different modeling options and their impact on analysis results are also described.

Mesh：

Year:  2007        PMID： 18985145      PMCID： PMC2569805          DOI： 10.1093/scan/nsm019

Source DB:  PubMed          Journal:  Soc Cogn Affect Neurosci        ISSN： 1749-5016            Impact factor:   3.436


  12 in total

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3.  Temporal autocorrelation in univariate linear modeling of FMRI data.

Authors:  M W Woolrich; B D Ripley; M Brady; S M Smith
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4.  Classical and Bayesian inference in neuroimaging: applications.

Authors:  K J Friston; D E Glaser; R N A Henson; S Kiebel; C Phillips; J Ashburner
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5.  Classical and Bayesian inference in neuroimaging: theory.

Authors:  K J Friston; W Penny; C Phillips; S Kiebel; G Hinton; J Ashburner
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6.  General multilevel linear modeling for group analysis in FMRI.

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7.  Diagnosis and exploration of massively univariate neuroimaging models.

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Review 8.  Controlling the familywise error rate in functional neuroimaging: a comparative review.

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9.  Multilevel linear modelling for FMRI group analysis using Bayesian inference.

Authors:  Mark W Woolrich; Timothy E J Behrens; Christian F Beckmann; Mark Jenkinson; Stephen M Smith
Journal:  Neuroimage       Date:  2004-04       Impact factor: 6.556

Review 10.  Modeling and inference of multisubject fMRI data.

Authors:  Jeanette A Mumford; Thomas Nichols
Journal:  IEEE Eng Med Biol Mag       Date:  2006 Mar-Apr
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  23 in total

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7.  Bayesian network models in brain functional connectivity analysis.

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9.  Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

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10.  Separating lexical-semantic access from other mnemonic processes in picture-name verification.

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