Literature DB >> 22542665

Identifying FMRI model violations with Lagrange multiplier tests.

Ben Cassidy1, Christopher J Long, Caroline Rae, Victor Solo.   

Abstract

The standard modeling framework in functional magnetic resonance imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialized software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as nonlinearity, nonstationarity and validity of the common double gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples.

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Year:  2012        PMID: 22542665      PMCID: PMC3759682          DOI: 10.1109/TMI.2012.2195327

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  16 in total

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7.  The variability of human, BOLD hemodynamic responses.

Authors:  G K Aguirre; E Zarahn; M D'esposito
Journal:  Neuroimage       Date:  1998-11       Impact factor: 6.556

8.  Linear systems analysis of functional magnetic resonance imaging in human V1.

Authors:  G M Boynton; S A Engel; G H Glover; D J Heeger
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9.  Nonlinear aspects of the BOLD response in functional MRI.

Authors:  A L Vazquez; D C Noll
Journal:  Neuroimage       Date:  1998-02       Impact factor: 6.556

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

Authors:  Martin M Monti
Journal:  Front Hum Neurosci       Date:  2011-03-18       Impact factor: 3.169

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  2 in total

1.  State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI.

Authors:  Victor Solo
Journal:  Neural Comput       Date:  2016-03-04       Impact factor: 2.026

2.  Variational Bayesian causal connectivity analysis for fMRI.

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Journal:  Front Neuroinform       Date:  2014-05-05       Impact factor: 4.081

  2 in total

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