Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
14-33143S
Grantová Agentura České Republiky - International
CZ.02.1.01/0.0/0.0/16_013/0001775
Ministerstvo Školství, Mládeže a Tělovýchovy and European Regional Development Fund - International
LM2015062
Ministerstvo Školství, Mládeže a Tělovýchovy - International
PubMed
30403309
PubMed Central
PMC6865642
DOI
10.1002/hbm.24433
Knihovny.cz E-zdroje
- Klíčová slova
- RETROICOR, cerebrospinal fluid, fMRI, filtering, functional connectivity, nuisance regression, principal component analysis, psychophysiological interactions, white matter,
- MeSH
- artefakty * MeSH
- bílá hmota MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mapování mozku metody MeSH
- mozek diagnostické zobrazování MeSH
- mozkomíšní mok MeSH
- počítačové zpracování obrazu metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
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