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Data-driven region-of-interest selection without inflating Type I error rate

Brooks, Joseph L, Zoumpoulaki, Alexia, Bowman, Howard (2016) Data-driven region-of-interest selection without inflating Type I error rate. Psychophysiology, 54 (1). pp. 100-113. ISSN 0048-5772. E-ISSN 1469-8986. (doi:10.1111/psyp.12682) (KAR id:55323)


In event-related potentials (ERP) and other large multi-dimensional neuroscience datasets, researchers often select regions-of-interest (ROIs) for analysis. The method of ROI selection can critically affect the conclusions of a study by causing the researcher to miss effects in the data or to detect spurious effects. In practice, to avoid inflating Type I error rate (i.e., false positives), ROIs are often based on a priori hypotheses or independent information. However, this can be insensitive to experiment-specific variations in effect location (e.g. latency shifts) reducing power to detect effects. Data-driven ROI selection, in contrast, is non-independent and uses the data under analysis to determine ROI positions. Therefore, it has potential to select ROIs based on experiment-specific information and increase power for detecting effects. However, data driven methods have been criticized because they can substantially inflate Type I error rate. Here we demonstrate, using simulations of simple ERP experiments, that data-driven ROI selection can indeed be more powerful than a priori hypotheses or independent information. Furthermore, we show that data-driven ROI selection using the aggregate-grand-average from trials (AGAT), despite being based on the data at hand, can be safely used for ROI selection under many circumstances. However, when there is a noise difference between conditions, using the AGAT can inflate Type 1 error and should be avoided. We identify critical assumptions for use of the AGAT and provide a basis for researchers to use, and reviewers to assess, data-driven methods of ROI localization in ERP and other studies.

Item Type: Article
DOI/Identification number: 10.1111/psyp.12682
Uncontrolled keywords: EEG, ERP, region-of-interest, statistics, bias
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HA Statistics
Divisions: Divisions > Division of Human and Social Sciences > School of Psychology
Depositing User: Joe Brooks
Date Deposited: 16 May 2016 17:01 UTC
Last Modified: 04 Mar 2024 17:11 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Brooks, Joseph L.

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Zoumpoulaki, Alexia.

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Bowman, Howard.

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