The Impact of Preprocessing Pipeline Choice in Univariate and Multivariate Analyses of PET Data
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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The Impact of Preprocessing Pipeline Choice in Univariate and Multivariate Analyses of PET Data. / Nørgaard, Martin; Greve, Douglas N.; Svarer, Claus; Strother, Stephen C.; Knudsen, Gitte M.; Ganz, Melanie.
2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018. IEEE, 2018. 8423962.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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 -
ID: 203671902