Literature DB >> 15193618

An empirical investigation into the number of subjects required for an event-related fMRI study.

Kevin Murphy1, Hugh Garavan.   

Abstract

Optimising the number of subjects required for an event-related functional imaging study is critical for ensuring sufficient statistical power. We report an empirical investigation of this issue by employing a resampling approach to the data of 58 subjects drawn from four previous GO/NOGO studies. Using voxelwise measures and setting the activation map from the complete sample to be a "gold standard", analyses revealed the statistical power to be surprisingly low at typical sample sizes (n = 20). However, voxels that were significantly active from smaller samples tended to be true positives, that is, they were typically active in the gold standard map and correlated well with the gold standard activation measure. The numerous false negatives that resulted from the lower SNR of the smaller samples drove the poor statistical power of those samples. Splitting the sample into two groups provided a test of the reproducibility of activation maps that was assessed using an alternative measure that quantified the distances between centres-of-mass of activated areas. These analyses revealed that although the voxelwise overlap may be poor, the locations of activated areas provide some optimism for studies with typical sample sizes. With n = 20 in each of two groups, it was found that the centres-of-mass for 80% of activated areas fell within 25 mm of each other. The reported analyses, by quantifying the spatial reproducibility for various sample sizes performing a typical event-related cognitive task, thus provide an empirical measure of the disparity to be expected in comparing activation maps.

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Mesh:

Year:  2004        PMID: 15193618     DOI: 10.1016/j.neuroimage.2004.02.005

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  46 in total

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Review 2.  [Methodological principles for optimising functional MR experiments].

Authors:  T Wüstenberg; F L Giesel; H Strasburger
Journal:  Radiologe       Date:  2005-02       Impact factor: 0.635

3.  How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration.

Authors:  Kevin Murphy; Jerzy Bodurka; Peter A Bandettini
Journal:  Neuroimage       Date:  2006-11-22       Impact factor: 6.556

4.  Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control.

Authors:  Katya Rubia; Anna B Smith; James Woolley; Chiara Nosarti; Isobel Heyman; Eric Taylor; Mick Brammer
Journal:  Hum Brain Mapp       Date:  2006-12       Impact factor: 5.038

5.  Cerebral control of the bladder in normal and urge-incontinent women.

Authors:  Derek Griffiths; Stasa D Tadic; Werner Schaefer; Neil M Resnick
Journal:  Neuroimage       Date:  2007-05-18       Impact factor: 6.556

6.  Linear age-correlated functional development of right inferior fronto-striato-cerebellar networks during response inhibition and anterior cingulate during error-related processes.

Authors:  Katya Rubia; Anna B Smith; Eric Taylor; Michael Brammer
Journal:  Hum Brain Mapp       Date:  2007-11       Impact factor: 5.038

7.  Group analysis and the subject factor in functional magnetic resonance imaging: analysis of fifty right-handed healthy subjects in a semantic language task.

Authors:  Mohamed L Seghier; François Lazeyras; Alan J Pegna; Jean-Marie Annoni; Asaid Khateb
Journal:  Hum Brain Mapp       Date:  2008-04       Impact factor: 5.038

8.  Power and sample size calculation for neuroimaging studies by non-central random field theory.

Authors:  Satoru Hayasaka; Ann M Peiffer; Christina E Hugenschmidt; Paul J Laurienti
Journal:  Neuroimage       Date:  2007-06-18       Impact factor: 6.556

9.  Dynamic and static contributions of the cerebrovasculature to the resting-state BOLD signal.

Authors:  Sungho Tak; Danny J J Wang; Jonathan R Polimeni; Lirong Yan; J Jean Chen
Journal:  Neuroimage       Date:  2013-10-04       Impact factor: 6.556

10.  Connectivity Analysis is Essential to Understand Neurological Disorders.

Authors:  James B Rowe
Journal:  Front Syst Neurosci       Date:  2010-09-17
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