2019
Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation
BARTOŇ, Marek, Radek MAREČEK, Lenka KRAJČOVIČOVÁ, Tomáš SLAVÍČEK, Tomáš KAŠPÁREK et. al.Základní údaje
Originální název
Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation
Autoři
BARTOŇ, Marek (203 Česká republika, domácí), Radek MAREČEK (203 Česká republika, domácí), Lenka KRAJČOVIČOVÁ (703 Slovensko, domácí), Tomáš SLAVÍČEK (203 Česká republika, domácí), Tomáš KAŠPÁREK (203 Česká republika, domácí), Petra HOLŠTAJN ZEMÁNKOVÁ (203 Česká republika, domácí), Pavel ŘÍHA (203 Česká republika, domácí) a Michal MIKL (203 Česká republika, garant, domácí)
Vydání
Human Brain Mapping, Wiley-Liss, 2019, 1065-9471
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
30103 Neurosciences
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 4.421
Kód RIV
RIV/00216224:14740/19:00107257
Organizační jednotka
Středoevropský technologický institut
UT WoS
000459470400006
Klíčová slova anglicky
cerebrospinal fluid; filtering; fMRI; functional connectivity; nuisance regression; principal component analysis; psychophysiological interactions; RETROICOR; white matter
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 24. 10. 2024 09:40, Mgr. Adéla Pešková
Anotace
V originále
This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.
Návaznosti
EF16_013/0001775, projekt VaV |
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GA14-33143S, projekt VaV |
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LM2015062, projekt VaV |
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