The Impact of Preprocessing Pipeline Choice in Univariate and Multivariate Analyses of PET Data

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It has long been recognized that the data preprocessing chain is a critical part of a neuroimaging experiment. In this work we evaluate the impact of preprocessing choices in univariate and multivariate analyses of Positron Emission Tomography (PET) data. Thirty healthy participants were scanned twice in a High-Resolution Research Tomography PET scanner with the serotonin transporter (5-HTT) radioligand [11 C]DASB. Binding potentials (BPND) from 14 brain regions are quantified with 384 different preprocessing choices. A univariate paired t-Test is applied to each region and for each preprocessing choice, and corrected for multiple comparisons using FDR within each pipeline. Additionally, a multivariate Linear Discriminant Analysis (LDA) model is used to discriminate test and retest BPND, and the model performance is evaluated using a repeated cross-validation framework with permutations. The univariate analysis revealed several significant differences in 5-HTT BPND across brain regions, depending on the preprocessing choice. The classification accuracy of the multivariate LDA model varied from 37% to 70% depending on the choice of preprocessing, and could reasonably be modeled with a normal distribution centered at 51% accuracy. In spite of correcting for multiple comparisons, the univariate model with varying preprocessing choices is more likely to generate false-positive results compared to a simple multivariate analysis model evaluated with cross-validation and permutations.

Original languageEnglish
Title of host publication2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
Number of pages4
Publication date2018
Article number8423962
ISBN (Print)9781538668597
Publication statusPublished - 2018
Event2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 - Singapore, Singapore
Duration: 12 Jun 201814 Jun 2018


Conference2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

ID: 203671902