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Cited by 238Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsNeural and Behavioral Psychology Studies

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

The impact of study design on pattern estimation for single-trial multivariate pattern analysis

Mumford, Jeanette A., Davis, Tyler, Poldrack, Russell A.·2014·NeuroImage
DOI 10.1016/j.neuroimage.2014.09.026
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Abstract

A prerequisite for a pattern analysis using functional magnetic resonance imaging (fMRI) data is estimating the patterns from time series data, which then are input into the pattern analysis. Here we focus on how the combination of study design (order and spacing of trials) with pattern estimator impacts the Type I error rate of the subsequent pattern analysis. When Type I errors are inflated, the results are no longer valid, so this work serves as a guide for designing and analyzing MVPA studies with controlled false positive rates. The MVPA strategies examined are pattern classification and similarity, utilizing single trial activation patterns from the same functional run. Primarily focusing on the Least Squares Single and Least Square All pattern estimators, we show that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses. It may seem intuitive that increasing the interstimulus interval (ISI) would alleviate this issue, but remaining weak correlations between activation patterns persist and have a strong influence in pattern similarity analyses. Pattern similarity analyses using only activation patterns estimated from the same functional run of data are susceptible to inflated false positives unless trials are randomly ordered, with a different randomization for each subject. In other cases, where there is any structure to trial order, valid pattern similarity analysis results can only be obtained if similarity computations are restricted to pairs of activation patterns from independent runs. Likewise, for pattern classification, false positives are minimized when the testing and training sets in cross validation do not contain patterns estimated from the same run.

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Metadata

Authors
Mumford, J. A., Davis, T., & Poldrack, R. A.
Affiliations
  • University of Wisconsin–Madison (US)
  • Texas Tech University (US)
  • The University of Texas at Austin (US)
Corresponding author
Mumford, Jeanette A.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2014-12
Volume
103
Pages
130-138
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2014.09.026
OpenAlex
W2015682160
Cited by
237
References
24
Citations by year
2026: 20 · 2025: 36 · 2024: 20 · 2023: 29 · 2022: 25 · 2021: 29 · 2020: 23 · 2019: 15 · 2018: 17 · 2017: 10 · 2016: 8 · 2015: 5
FWCI
4.28
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
multivariate pattern analysis, single-trial analysis, research design, interstimulus interval, type I error rate, false positive rate, pattern similarity, pattern classification, least squares estimation, temporal autocorrelation, cross-validation, functional MRI
MeSH terms
Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Research Design, Multivariate Analysis

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