Synopsis
Motivation:
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366-02-016
ISMRM Abstract
Validation of Inter- and Intra-Volume Motion Correction Methods Using HCP Data: Gender Differences in Functional Connectivity
Accepted
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1. Hajnal JV, Myers R, Oatridge A, et al. Artifacts due to stimulus correlated motion in functional imaging of the brain. Magn Reson Med 1994; 31: 283-291. 1994/03/01. DOI: 10.1002/mrm.1910310307. [doi]
2. Jiang A, Kennedy DN, Baker JR, et al. Motion detection and correction in functional MR imaging. Human Brain Mapping 1995; 3: 224-235. DOI: https://doi.org/10.1002/hbm.460030306. [doi]
3. Friston KJ, Williams S, Howard R, et al. Movement-related effects in fMRI time-series. Magn Reson Med 1996; 35: 346-355. 1996/03/01. DOI: 10.1002/mrm.1910350312. [doi]
4. Grootoonk S, Hutton C, Ashburner J, et al. Characterization and correction of interpolation effects in the realignment of fMRI time series. Neuroimage 2000; 11: 49-57. 2000/02/25. DOI: 10.1006/nimg.1999.0515. [doi]
5. Beall EB and Lowe MJ. SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction. Neuroimage 2014; 101: 21-34. 2014/06/28. DOI: 10.1016/j.neuroimage.2014.06.038 [doi]
6. Shin W, Taylor P and Lowe M. Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data. Aperture Neuro 2024; 2024;4.
7. Glover GH, Li TQ and Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 2000; 44: 162-167. 2000/07/14. DOI: 10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E [pii]. [doi]
8. Glasser MF, Sotiropoulos SN, Wilson JA, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 2013; 80: 105-124. 2013/05/15. DOI: 10.1016/j.neuroimage.2013.04.127. [doi]
9. Griffanti L, Salimi-Khorshidi G, Beckmann CF, et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage 2014; 95: 232-247. 2014/03/25. DOI: 10.1016/j.neuroimage.2014.03.034. [doi]
10. Glasser MF, Smith SM, Marcus DS, et al. The Human Connectome Project's neuroimaging approach. Nat Neurosci 2016; 19: 1175-1187. 2016/08/30. DOI: 10.1038/nn.4361. [doi]
11. Glasser MF, Coalson TS, Robinson EC, et al. A multi-modal parcellation of human cerebral cortex. Nature 2016; 536: 171-178. 2016/07/21. DOI: 10.1038/nature18933. [doi]
12. Bright MG and Murphy K. Is fMRI "noise" really noise? Resting state nuisance regressors remove variance with network structure. Neuroimage 2015; 114: 158-169. 2015/04/12. DOI: 10.1016/j.neuroimage.2015.03.070. [doi]
13. Hallquist MN, Hwang K and Luna B. The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage 2013; 82: 208-225. DOI: https://doi.org/10.1016/j.neuroimage.2013.05.116. [doi]
14. Ding Z, Xu L, Gao Y, et al. Cortical modulation of resting state BOLD signals in white matter. Scientific Reports 2025; 15: 30056. DOI: 10.1038/s41598-025-14352-x. [doi]
15. Vos de Wael R, Hyder F and Thompson GJ. Effects of Tissue-Specific Functional Magnetic Resonance Imaging Signal Regression on Resting-State Functional Connectivity. Brain Connect 2017; 7: 482-490. 2017/08/22. DOI: 10.1089/brain.2016.0465. [doi]
16. Mahadevan AS, Tooley UA, Bertolero MA, et al. Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data. Neuroimage 2021; 241: 118408. 2021/07/21. DOI: 10.1016/j.neuroimage.2021.118408. [doi]
17. Morfini F, Whitfield-Gabrieli S and Nieto-Castañón A. Functional connectivity MRI quality control procedures in CONN. Front Neurosci 2023; 17: 1092125. 2023/04/11. DOI: 10.3389/fnins.2023.1092125. [doi]
18. Zhang R, Rolls ET, Cheng W, et al. Different cortical connectivities in human females and males relate to differences in strength and body composition, reward and emotional systems, and memory. Brain Struct Funct 2024; 229: 47-61. 2023/10/20. DOI: 10.1007/s00429-023-02720-0. [doi]
19. Ritchie SJ, Cox SR, Shen X, et al. Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants. Cereb Cortex 2018; 28: 2959-2975. 2018/05/18. DOI: 10.1093/cercor/bhy109. [doi]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/366-02-016