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@article{1308441,
author = {Bartoň, Marek and Mareček, Radek and Rektor, Ivan and Filip, Pavel and Janoušová, Eva and Mikl, Michal},
article_location = {Amsterdam},
article_number = {September},
doi = {http://dx.doi.org/10.1016/j.jneumeth.2015.06.021},
keywords = {BOLD; Filtering; FMRI; Noise; Psychophysiological interactions; RETROICOR},
language = {eng},
issn = {0165-0270},
journal = {Journal of Neuroscience Methods},
title = {Sensitivity of PPI analysis to differences in noise reduction strategies},
url = {http://ac.els-cdn.com/S0165027015002459/1-s2.0-S0165027015002459-main.pdf?_tid=2b313bc2-4259-11e5-bb7c-00000aab0f26&acdnat=1439538824_bbe2dc8b96cdc663b6ffb54057334360},
volume = {253},
year = {2015}
}
TY - JOUR
ID - 1308441
AU - Bartoň, Marek - Mareček, Radek - Rektor, Ivan - Filip, Pavel - Janoušová, Eva - Mikl, Michal
PY - 2015
TI - Sensitivity of PPI analysis to differences in noise reduction strategies
JF - Journal of Neuroscience Methods
VL - 253
IS - September
SP - 218-232
EP - 218-232
PB - Elsevier Science Ltd
SN - 01650270
KW - BOLD
KW - Filtering
KW - FMRI
KW - Noise
KW - Psychophysiological interactions
KW - RETROICOR
UR - http://ac.els-cdn.com/S0165027015002459/1-s2.0-S0165027015002459-main.pdf?_tid=2b313bc2-4259-11e5-bb7c-00000aab0f26&acdnat=1439538824_bbe2dc8b96cdc663b6ffb54057334360
L2 - http://ac.els-cdn.com/S0165027015002459/1-s2.0-S0165027015002459-main.pdf?_tid=2b313bc2-4259-11e5-bb7c-00000aab0f26&acdnat=1439538824_bbe2dc8b96cdc663b6ffb54057334360
N2 - Background In some fields of fMRI data analysis, using correct methods for dealing with noise is crucial for achieving meaningful results. This paper provides a quantitative assessment of the effects of different preprocessing and noise filtering strategies on psychophysiological interactions (PPI) methods for analyzing fMRI data where noise management has not yet been established. Methods Both real and simulated fMRI data were used to assess these effects. Four regions of interest (ROIs) were chosen for the PPI analysis on the basis of their engagement during two tasks. PPI analysis was performed for 32 different preprocessing and analysis settings, which included data filtering with RETROICOR or no such filtering; different filtering of the ROI “seed” signal with a nuisance data-driven time series; and the involvement of these data-driven time series in the subsequent PPI GLM analysis. The extent of the statistically significant results was quantified at the group level using simple descriptive statistics. Simulated data were generated to assess statistical improvement of different filtering strategies. Results We observed that different approaches for dealing with noise in PPI analysis yield differing results in real data. In simulated data, we found RETROICOR, seed signal filtering and the addition of data-driven covariates to the PPI design matrix significantly improves results. Conclusions We recommend the use of RETROICOR, and data-driven filtering of the whole data, or alternatively, seed signal filtering with data-driven signals and the addition of data-driven covariates to the PPI design matrix.
ER -
BARTOŇ, Marek, Radek MAREČEK, Ivan REKTOR, Pavel FILIP, Eva JANOUŠOVÁ a Michal MIKL. Sensitivity of PPI analysis to differences in noise reduction strategies. \textit{Journal of Neuroscience Methods}. Amsterdam: Elsevier Science Ltd, 2015, roč.~253, September, s.~218-232. ISSN~0165-0270. Dostupné z: https://dx.doi.org/10.1016/j.jneumeth.2015.06.021.
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