205 related articles for article (PubMed ID: 31336189)
1. Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression.
Nalci A; Luo W; Liu TT
Neuroimage; 2019 Nov; 202():116005. PubMed ID: 31336189
[TBL] [Abstract][Full Text] [Related]
2. Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI.
Nalci A; Rao BD; Liu TT
Neuroimage; 2019 Jan; 184():1005-1031. PubMed ID: 30223062
[TBL] [Abstract][Full Text] [Related]
3. Evaluation of nuisance removal for functional MRI of rodent brain.
Chuang KH; Lee HL; Li Z; Chang WT; Nasrallah FA; Yeow LY; Singh KKDR
Neuroimage; 2019 Mar; 188():694-709. PubMed ID: 30593905
[TBL] [Abstract][Full Text] [Related]
4. Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines.
Kopal J; Pidnebesna A; Tomeček D; Tintěra J; Hlinka J
Hum Brain Mapp; 2020 Dec; 41(18):5325-5340. PubMed ID: 32881215
[TBL] [Abstract][Full Text] [Related]
5. Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics.
Gotts SJ; Gilmore AW; Martin A
Neuroimage; 2020 Jan; 205():116289. PubMed ID: 31629827
[TBL] [Abstract][Full Text] [Related]
6. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
Aquino KM; Fulcher BD; Parkes L; Sabaroedin K; Fornito A
Neuroimage; 2020 May; 212():116614. PubMed ID: 32084564
[TBL] [Abstract][Full Text] [Related]
7. On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture.
Chamberland M; Girard G; Bernier M; Fortin D; Descoteaux M; Whittingstall K
Brain Connect; 2017 Oct; 7(8):491-503. PubMed ID: 28825322
[TBL] [Abstract][Full Text] [Related]
8. Impact of global signal regression on characterizing dynamic functional connectivity and brain states.
Xu H; Su J; Qin J; Li M; Zeng LL; Hu D; Shen H
Neuroimage; 2018 Jun; 173():127-145. PubMed ID: 29476914
[TBL] [Abstract][Full Text] [Related]
9. Combining Prospective Acquisition CorrEction (PACE) with retrospective correction to reduce motion artifacts in resting state fMRI data.
Lanka P; Deshpande G
Brain Behav; 2019 Aug; 9(8):e01341. PubMed ID: 31297966
[TBL] [Abstract][Full Text] [Related]
10. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity.
Hallquist MN; Hwang K; Luna B
Neuroimage; 2013 Nov; 82():208-25. PubMed ID: 23747457
[TBL] [Abstract][Full Text] [Related]
11. Modular preprocessing pipelines can reintroduce artifacts into fMRI data.
Lindquist MA; Geuter S; Wager TD; Caffo BS
Hum Brain Mapp; 2019 Jun; 40(8):2358-2376. PubMed ID: 30666750
[TBL] [Abstract][Full Text] [Related]
12. Controlling for the effect of arterial-CO
Golestani AM; Chen JJ
Neuroimage; 2020 Aug; 216():116874. PubMed ID: 32335260
[TBL] [Abstract][Full Text] [Related]
13. Test-retest reliability of dynamic functional connectivity in resting state fMRI.
Zhang C; Baum SA; Adduru VR; Biswal BB; Michael AM
Neuroimage; 2018 Dec; 183():907-918. PubMed ID: 30120987
[TBL] [Abstract][Full Text] [Related]
14. Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model.
Krishnamurthy V; Krishnamurthy LC; Schwam DM; Ealey A; Shin J; Greenberg D; Morris RD
Brain Connect; 2018 Mar; 8(2):94-105. PubMed ID: 29226700
[TBL] [Abstract][Full Text] [Related]
15. Disambiguating the role of blood flow and global signal with partial information decomposition.
Colenbier N; Van de Steen F; Uddin LQ; Poldrack RA; Calhoun VD; Marinazzo D
Neuroimage; 2020 Jun; 213():116699. PubMed ID: 32179104
[TBL] [Abstract][Full Text] [Related]
16. Global signal regression strengthens association between resting-state functional connectivity and behavior.
Li J; Kong R; Liégeois R; Orban C; Tan Y; Sun N; Holmes AJ; Sabuncu MR; Ge T; Yeo BTT
Neuroimage; 2019 Aug; 196():126-141. PubMed ID: 30974241
[TBL] [Abstract][Full Text] [Related]
17. Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies.
Patriat R; Molloy EK; Birn RM
Brain Connect; 2015 Nov; 5(9):582-95. PubMed ID: 26107049
[TBL] [Abstract][Full Text] [Related]
18. Dynamic-flip-angle ECG-gating with nuisance signal regression improves resting-state BOLD functional connectivity mapping by reducing cardiogenic noise.
Hu C; Tokoglu F; Scheinost D; Qiu M; Shen X; Peters DC; Galiana G; Constable RT
Magn Reson Med; 2019 Sep; 82(3):911-923. PubMed ID: 31016782
[TBL] [Abstract][Full Text] [Related]
19. The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI.
Almgren H; Van de Steen F; Razi A; Friston K; Marinazzo D
Neuroimage; 2020 Mar; 208():116435. PubMed ID: 31816423
[TBL] [Abstract][Full Text] [Related]
20. Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project.
Burgess GC; Kandala S; Nolan D; Laumann TO; Power JD; Adeyemo B; Harms MP; Petersen SE; Barch DM
Brain Connect; 2016 Nov; 6(9):669-680. PubMed ID: 27571276
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]