158 related articles for article (PubMed ID: 37873165)
1. Sliding windows analysis can undo the effects of preprocessing when applied to fMRI data.
Lindquist MA
bioRxiv; 2024 Apr; ():. PubMed ID: 37873165
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals.
Vakamudi K; Trapp C; Talaat K; Gao K; Sa De La Rocque Guimaraes B; Posse S
Brain Connect; 2020 Oct; 10(8):448-463. PubMed ID: 32892629
[No Abstract] [Full Text] [Related]
4. Comparing test-retest reliability of dynamic functional connectivity methods.
Choe AS; Nebel MB; Barber AD; Cohen JR; Xu Y; Pekar JJ; Caffo B; Lindquist MA
Neuroimage; 2017 Sep; 158():155-175. PubMed ID: 28687517
[TBL] [Abstract][Full Text] [Related]
5. Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI.
Raval V; Nguyen KP; Pinho M; Dewey RB; Trivedi M; Montillo AA
Neuroinformatics; 2022 Oct; 20(4):879-896. PubMed ID: 35291020
[TBL] [Abstract][Full Text] [Related]
6. Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination.
Shirer WR; Jiang H; Price CM; Ng B; Greicius MD
Neuroimage; 2015 Aug; 117():67-79. PubMed ID: 25987368
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis.
Valsasina P; Hidalgo de la Cruz M; Filippi M; Rocca MA
Front Neurosci; 2019; 13():618. PubMed ID: 31354402
[TBL] [Abstract][Full Text] [Related]
10. On applicability of PCA, voxel-wise variance normalization and dimensionality assumptions for sliding temporal window sICA in resting-state fMRI.
Remes JJ; Abou Elseoud A; Ollila E; Haapea M; Starck T; Nikkinen J; Tervonen O; Silven O
Magn Reson Imaging; 2013 Oct; 31(8):1338-48. PubMed ID: 23845397
[TBL] [Abstract][Full Text] [Related]
11. A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity.
Kassinopoulos M; Mitsis GD
Magn Reson Imaging; 2022 Jan; 85():228-250. PubMed ID: 34715292
[TBL] [Abstract][Full Text] [Related]
12. Connectivity dynamics from wakefulness to sleep.
Damaraju E; Tagliazucchi E; Laufs H; Calhoun VD
Neuroimage; 2020 Oct; 220():117047. PubMed ID: 32562782
[TBL] [Abstract][Full Text] [Related]
13. Motion-Dependent Effects of Functional Magnetic Resonance Imaging Preprocessing Methodology on Global Functional Connectivity.
DeSalvo MN
Brain Connect; 2020 Dec; 10(10):578-584. PubMed ID: 33216639
[No Abstract] [Full Text] [Related]
14. Validating dynamicity in resting state fMRI with activation-informed temporal segmentation.
Duda M; Koutra D; Sripada C
Hum Brain Mapp; 2021 Dec; 42(17):5718-5735. PubMed ID: 34510647
[TBL] [Abstract][Full Text] [Related]
15. A Wavelet-Based Approach for Estimating Time-Varying Connectivity in Resting-State Functional Magnetic Resonance Imaging.
Savva AD; Matsopoulos GK; Mitsis GD
Brain Connect; 2022 Apr; 12(3):285-298. PubMed ID: 34155908
[No Abstract] [Full Text] [Related]
16. Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.
Chen X; Zhang H; Zhang L; Shen C; Lee SW; Shen D
Hum Brain Mapp; 2017 Oct; 38(10):5019-5034. PubMed ID: 28665045
[TBL] [Abstract][Full Text] [Related]
17. Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology.
Cao L; Kohut SJ; Frederick BD
Front Neuroimaging; 2022; 1():1031991. PubMed ID: 37555145
[TBL] [Abstract][Full Text] [Related]
18. Test-Retest Reliability of "High-Order" Functional Connectivity in Young Healthy Adults.
Zhang H; Chen X; Zhang Y; Shen D
Front Neurosci; 2017; 11():439. PubMed ID: 28824362
[TBL] [Abstract][Full Text] [Related]
19. Manifold learning for fMRI time-varying functional connectivity.
Gonzalez-Castillo J; Fernandez IS; Lam KC; Handwerker DA; Pereira F; Bandettini PA
Front Hum Neurosci; 2023; 17():1134012. PubMed ID: 37497043
[TBL] [Abstract][Full Text] [Related]
20. The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury.
Vergara VM; Mayer AR; Damaraju E; Calhoun VD
Brain Behav; 2017 Oct; 7(10):e00809. PubMed ID: 29075569
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]