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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.

Titel2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
Antal sider4
ISBN (Trykt)9781538668597
StatusUdgivet - 2018
Begivenhed2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018 - Singapore, Singapore
Varighed: 12 jun. 201814 jun. 2018


Konference2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

ID: 203671902