161 related articles for article (PubMed ID: 38498530)
1. Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn.
Wang HT; Meisler SL; Sharmarke H; Clarke N; Gensollen N; Markiewicz CJ; Paugam F; Thirion B; Bellec P
PLoS Comput Biol; 2024 Mar; 20(3):e1011942. PubMed ID: 38498530
[TBL] [Abstract][Full Text] [Related]
2. Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn.
Wang HT; Meisler SL; Sharmarke H; Clarke N; Gensollen N; Markiewicz CJ; Paugam F; Thirion B; Bellec P
bioRxiv; 2023 Jul; ():. PubMed ID: 37131781
[TBL] [Abstract][Full Text] [Related]
3. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI.
Parkes L; Fulcher B; Yücel M; Fornito A
Neuroimage; 2018 May; 171():415-436. PubMed ID: 29278773
[TBL] [Abstract][Full Text] [Related]
4. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.
Patel AX; Kundu P; Rubinov M; Jones PS; Vértes PE; Ersche KD; Suckling J; Bullmore ET
Neuroimage; 2014 Jul; 95(100):287-304. PubMed ID: 24657353
[TBL] [Abstract][Full Text] [Related]
5. Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis.
Li Y; Saxe R; Anzellotti S
PLoS One; 2019; 14(9):e0222914. PubMed ID: 31550276
[TBL] [Abstract][Full Text] [Related]
6. Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.
Xu Y; Tong Y; Liu S; Chow HM; AbdulSabur NY; Mattay GS; Braun AR
Neuroimage; 2014 Dec; 103():33-47. PubMed ID: 25225001
[TBL] [Abstract][Full Text] [Related]
7. The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity.
Misaki M; Bodurka J
J Neural Eng; 2021 Jul; 18(4):. PubMed ID: 34126595
[No Abstract] [Full Text] [Related]
8. A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.
Mayer AR; Ling JM; Dodd AB; Shaff NA; Wertz CJ; Hanlon FM
Hum Brain Mapp; 2019 Sep; 40(13):3843-3859. PubMed ID: 31119818
[TBL] [Abstract][Full Text] [Related]
9. Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing.
Phạm DĐ; McDonald DJ; Ding L; Nebel MB; Mejia AF
Neuroimage; 2023 Apr; 270():119972. PubMed ID: 36842522
[TBL] [Abstract][Full Text] [Related]
10. Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI.
Steel A; Garcia BD; Silson EH; Robertson CE
Neuroimage; 2022 Dec; 264():119723. PubMed ID: 36328274
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.
Pruim RHR; Mennes M; Buitelaar JK; Beckmann CF
Neuroimage; 2015 May; 112():278-287. PubMed ID: 25770990
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Mitigating head motion artifact in functional connectivity MRI.
Ciric R; Rosen AFG; Erus G; Cieslak M; Adebimpe A; Cook PA; Bassett DS; Davatzikos C; Wolf DH; Satterthwaite TD
Nat Protoc; 2018 Dec; 13(12):2801-2826. PubMed ID: 30446748
[TBL] [Abstract][Full Text] [Related]
15. Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks.
Mascali D; Moraschi M; DiNuzzo M; Tommasin S; Fratini M; Gili T; Wise RG; Mangia S; Macaluso E; Giove F
Hum Brain Mapp; 2021 Apr; 42(6):1805-1828. PubMed ID: 33528884
[TBL] [Abstract][Full Text] [Related]
16. Advancing motion denoising of multiband resting-state functional connectivity fMRI data.
Williams JC; Tubiolo PN; Luceno JR; Van Snellenberg JX
Neuroimage; 2022 Apr; 249():118907. PubMed ID: 35033673
[TBL] [Abstract][Full Text] [Related]
17. Evaluating denoising strategies in resting-state functional magnetic resonance in traumatic brain injury (EpiBioS4Rx).
Weiler M; Casseb RF; de Campos BM; Crone JS; Lutkenhoff ES; Vespa PM; Monti MM;
Hum Brain Mapp; 2022 Oct; 43(15):4640-4649. PubMed ID: 35723510
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. fMRIPrep: a robust preprocessing pipeline for functional MRI.
Esteban O; Markiewicz CJ; Blair RW; Moodie CA; Isik AI; Erramuzpe A; Kent JD; Goncalves M; DuPre E; Snyder M; Oya H; Ghosh SS; Wright J; Durnez J; Poldrack RA; Gorgolewski KJ
Nat Methods; 2019 Jan; 16(1):111-116. PubMed ID: 30532080
[TBL] [Abstract][Full Text] [Related]
20. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.
Pruim RHR; Mennes M; van Rooij D; Llera A; Buitelaar JK; Beckmann CF
Neuroimage; 2015 May; 112():267-277. PubMed ID: 25770991
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]