"55323","25","archive","13881",,,"disk0/00/05/53/23","2016-05-16 17:01:17","2017-06-02 09:42:01","2016-05-16 17:01:17","article",,,"show",,"/style/images/fileicons/application_pdf.png;/55323/1/BrooksEtAl%20-%20AGAT%20Manuscript%20Accepted.pdf",,,"","","","","","","","","","",,,,"19973","1496396433","0","Brooks","Joseph L","","","j.l.brooks@kent.ac.uk","http://orcid.org/0000-0002-5364-3611","",,,,,"","","","Data-driven region-of-interest selection without inflating Type I error rate",,"pub","BF","13600/1","restricted",,,"EEG, ERP, region-of-interest, statistics, bias",,"Emailed author to let them know Sherpa says 12 month embargo is required, but cannot be set currently as item is not published. 17/09/16.","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.","2016-12-20","published",,"Psychophysiology","54","1","Wiley",,"100-113",,,,,,"10.1111/psyp.12682",,,,,,"TRUE",,"0048-5772",,,,,,"","","http://dx.doi.org/10.1111/psyp.12682","","",,,,,"",,,,,"","",,,,,"TRUE","archive",,,,,"","",,,,,,,,,,,,,,,,,,"1469-8986",,,,,,,"2016-05-04","paid_kent","APC217","","","","","318","12","AB","2016-05-04","2016-12-20","2016-07-07",,"AM",,,,,,,,,,"","",,,,,,,,"","","","","",,"",
"55323",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Zoumpoulaki","Alexia","","","az61@kent.ac.uk",,,,,,,,,,,,,"HA","25000/1",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"55323",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Bowman","Howard","","","h.bowman@kent.ac.uk",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,