Cargando…
Potential pitfalls when denoising resting state fMRI data using nuisance regression
In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated...
Autores principales: | Bright, Molly G., Tench, Christopher R., Murphy, Kevin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5489212/ https://www.ncbi.nlm.nih.gov/pubmed/28025128 http://dx.doi.org/10.1016/j.neuroimage.2016.12.027 |
Ejemplares similares
-
Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure
por: Bright, Molly G., et al.
Publicado: (2015) -
Advancing motion denoising of multiband resting-state functional connectivity fMRI data
por: Williams, John C., et al.
Publicado: (2022) -
Hemodynamic timing in resting-state and breathing-task BOLD fMRI
por: Gong, Jingxuan, et al.
Publicado: (2023) -
Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn
por: Wang, Hao-Ting, et al.
Publicado: (2023) -
Regression dynamic causal modeling for resting‐state fMRI
por: Frässle, Stefan, et al.
Publicado: (2021)