Literature DB >> 26158969

Correlations and Multiple Comparisons in Functional Imaging: A Statistical Perspective (Commentary on Vul et al., 2009).

Martin A Lindquist1, Andrew Gelman2.   

Abstract

Vul, Harris, Winkielman, and Pashler (2009), this issue) claim in their article that the correlations reported in fMRI studies are commonly overstated because researchers tend to report only the highest correlations or only those correlations that exceed some threshold. Their article has in a short time given rise to a spirited debate about key statistical issues at the heart of most functional neuroimaging studies. The debate provides a useful opportunity to discuss core statistical issues in neuroimaging and ultimately provides a chance for the field to grow and move forward. This commentary approaches the debate from a fundamentally statistical perspective. We begin by summarizing several of the key points under discussion, followed by our own commentary on these issues from a statistical point of view. We conclude our discussion by contemplating whether it may be time to move beyond the correlation and multiple comparisons framework that is causing so much confusion and instead represent all relevant research questions as parameters in one coherent multilevel model.
© 2009 Association for Psychological Science.

Year:  2009        PMID: 26158969     DOI: 10.1111/j.1745-6924.2009.01130.x

Source DB:  PubMed          Journal:  Perspect Psychol Sci        ISSN: 1745-6916


  16 in total

Review 1.  Everything you never wanted to know about circular analysis, but were afraid to ask.

Authors:  Nikolaus Kriegeskorte; Martin A Lindquist; Thomas E Nichols; Russell A Poldrack; Edward Vul
Journal:  J Cereb Blood Flow Metab       Date:  2010-06-23       Impact factor: 6.200

2.  Estimating and testing variance components in a multi-level GLM.

Authors:  Martin A Lindquist; Julie Spicer; Iris Asllani; Tor D Wager
Journal:  Neuroimage       Date:  2011-07-31       Impact factor: 6.556

3.  The principled control of false positives in neuroimaging.

Authors:  Craig M Bennett; George L Wolford; Michael B Miller
Journal:  Soc Cogn Affect Neurosci       Date:  2009-12       Impact factor: 3.436

4.  Cluster failure or power failure? Evaluating sensitivity in cluster-level inference.

Authors:  Stephanie Noble; Dustin Scheinost; R Todd Constable
Journal:  Neuroimage       Date:  2019-12-15       Impact factor: 6.556

5.  Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators.

Authors:  Amanda F Mejia; Mary Beth Nebel; Haochang Shou; Ciprian M Crainiceanu; James J Pekar; Stewart Mostofsky; Brian Caffo; Martin A Lindquist
Journal:  Neuroimage       Date:  2015-02-28       Impact factor: 6.556

6.  Avoiding non-independence in fMRI data analysis: leave one subject out.

Authors:  Michael Esterman; Benjamin J Tamber-Rosenau; Yu-Chin Chiu; Steven Yantis
Journal:  Neuroimage       Date:  2009-12-16       Impact factor: 6.556

7.  Ironing out the statistical wrinkles in "ten ironic rules".

Authors:  Martin A Lindquist; Brian Caffo; Ciprian Crainiceanu
Journal:  Neuroimage       Date:  2013-04-12       Impact factor: 6.556

8.  Zen and the art of multiple comparisons.

Authors:  Martin A Lindquist; Amanda Mejia
Journal:  Psychosom Med       Date:  2015 Feb-Mar       Impact factor: 4.312

9.  Group-regularized individual prediction: theory and application to pain.

Authors:  Martin A Lindquist; Anjali Krishnan; Marina López-Solà; Marieke Jepma; Choong-Wan Woo; Leonie Koban; Mathieu Roy; Lauren Y Atlas; Liane Schmidt; Luke J Chang; Elizabeth A Reynolds Losin; Hedwig Eisenbarth; Yoni K Ashar; Elizabeth Delk; Tor D Wager
Journal:  Neuroimage       Date:  2015-11-17       Impact factor: 6.556

10.  Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI.

Authors:  Haochang Shou; Ani Eloyan; Mary Beth Nebel; Amanda Mejia; James J Pekar; Stewart Mostofsky; Brian Caffo; Martin A Lindquist; Ciprian M Crainiceanu
Journal:  Neuroimage       Date:  2014-05-29       Impact factor: 6.556

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