BARTOŇ, Marek, Radek MAREČEK, Ivan REKTOR, Pavel FILIP, Eva JANOUŠOVÁ and Michal MIKL. Sensitivity of PPI analysis to differences in noise reduction strategies. Journal of Neuroscience Methods. Amsterdam: Elsevier Science Ltd, 2015, vol. 253, September, p. 218-232. ISSN 0165-0270. doi:10.1016/j.jneumeth.2015.06.021.
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Basic information
Original name Sensitivity of PPI analysis to differences in noise reduction strategies
Authors BARTOŇ, Marek (203 Czech Republic, guarantor, belonging to the institution), Radek MAREČEK (203 Czech Republic, belonging to the institution), Ivan REKTOR (203 Czech Republic, belonging to the institution), Pavel FILIP (703 Slovakia, belonging to the institution), Eva JANOUŠOVÁ (203 Czech Republic, belonging to the institution) and Michal MIKL (203 Czech Republic, belonging to the institution).
Edition Journal of Neuroscience Methods, Amsterdam, Elsevier Science Ltd, 2015, 0165-0270.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 30000 3. Medical and Health Sciences
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 2.053
RIV identification code RIV/00216224:14740/15:00080949
Organization unit Central European Institute of Technology
UT WoS 000360867400022
Keywords in English BOLD; Filtering; FMRI; Noise; Psychophysiological interactions; RETROICOR
Tags EL OK, podil, rivok
Tags International impact, Reviewed
Changed by Changed by: Martina Prášilová, učo 342282. Changed: 8. 12. 2015 18:43.
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.
ED1.1.00/02.0068, research and development projectName: CEITEC - central european institute of technology
GA14-33143S, research and development projectName: Vliv fyziologických procesů na reliabilitu a časovou proměnlivost konektivity v lidském mozku měřené pomocí fMRI
Investor: Czech Science Foundation
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