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

TY - GEN

T1 - The Impact of Preprocessing Pipeline Choice in Univariate and Multivariate Analyses of PET Data

AU - Nørgaard, Martin

AU - Greve, Douglas N.

AU - Svarer, Claus

AU - Strother, Stephen C.

AU - Knudsen, Gitte M.

AU - Ganz, Melanie

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85051565165&partnerID=8YFLogxK

U2 - 10.1109/PRNI.2018.8423962

DO - 10.1109/PRNI.2018.8423962

M3 - Article in proceedings

AN - SCOPUS:85051565165

SN - 9781538668597

BT - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

PB - IEEE

T2 - 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

Y2 - 12 June 2018 through 14 June 2018

ER -