The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Motion correction and the use of motion covariates in multiple‐subject fMRI analysis
Open accessAbstract
The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event‐related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task‐correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block‐design and an event‐related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion‐corrected data and motion covariates included in the GLM; and (4) with non–motion‐corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event‐related data. When motion parameters were included in the GLM for event‐related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event‐related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations. Hum. Brain Mapp, 2006. © 2006 Wiley‐Liss, Inc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Temporal dynamics of brain activation during a working memory task
Cohen, J. D., Perlstein, W. M., Braver, T. S., Nystrom, L. E., Noll, D. C., Jonides, J., & Smith, E. E. (1997) · Nature · doi:10.1038/386604a0 · Cited by 1,876
Cited by
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529
Johnstone, T., van Reekum, C. M., Urry, H. L., Kalin, N. H., & Davidson, R. J. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2063-07.2007 · Cited by 1,051
Self-citationLonsdorf, T. B., Menz, M. M., Andreatta, M., Fullana, M. A., Golkar, A., Haaker, J., Heitland, I., Hermann, A., Kuhn, M., Kruse, O., et al. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.026 · Cited by 837
Siegel, J. S., Power, J. D., Dubis, J. W., Vogel, A. C., Church, J. A., Schlaggar, B. L., & Petersen, S. E. (2014) · Human Brain Mapping · doi:10.1002/hbm.22307 · Cited by 546
Methods for cleaning the BOLD fMRI signal
Caballero-Gaudes, C., & Reynolds, R. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.018 · Cited by 542
Ventromedial Prefrontal Cortex Is Critical for the Regulation of Amygdala Activity in Humans
Motzkin, J. C., Philippi, C. L., Wolf, R. C., Baskaya, M. K., & Koenigs, M. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.02.014 · Cited by 454
Heller, A. S., Johnstone, T., Shackman, A. J., Light, S. N., Peterson, M. J., Kolden, G. G., Kalin, N. H., & Davidson, R. J. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0910651106 · Cited by 444
Self-citationLanguage control and lexical competition in bilinguals: an event-related fMRI study
Abutalebi, J., Annoni, J.-M., Zimine, I., Pegna, A. J., Seghier, M. L., Lee-Jahnke, H., Lazeyras, F., Cappa, S. F., & Khateb, A. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm182 · Cited by 381
Confound modelling in UK Biobank brain imaging
Alfaro-Almagro, F., McCarthy, P., Afyouni, S., Andersson, J. L. R., Bastiani, M., Miller, K. L., Nichols, T. E., & Smith, S. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117002 · Cited by 249
van Reekum, C. M., Johnstone, T., Urry, H. L., Thurow, M. E., Schaefer, H. S., Alexander, A. L., & Davidson, R. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.03.052 · Cited by 271
Self-citationPace-Schott, E. F., Amole, M. C., Aue, T., Balconi, M., Bylsma, L. M., Critchley, H., Demaree, H. A., Friedman, B. H., Gooding, A. E. K., Gosseries, O., et al. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.05.002 · Cited by 221
Amygdala–prefrontal coupling underlies individual differences in emotion regulation
Lee, H., Heller, A. S., van Reekum, C. M., Nelson, B., & Davidson, R. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.044 · Cited by 219
“Single-subject GLM included separate regressors for each regulation condition (negative pictures: enhance, maintain, suppress, and neutral pictures: maintain) to estimate hemodynamic response functions (HRFs; modeled by a set of tent basis functions), six motion estimate covariates (Johnstone et al., 2006), and a seco…”
BackgroundMethodSelf-citationAlexander-Bloch, A., Clasen, L., Stockman, M., Ronan, L., Lalonde, F., Giedd, J., & Raznahan, A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23180 · Cited by 178
How to control for confounds in decoding analyses of neuroimaging data
Snoek, L., Miletić, S., & Scholte, H. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.074 · Cited by 143
Han, K., Mac Donald, C. L., Johnson, A. M., Barnes, Y., Wierzechowski, L., Zonies, D., Oh, J., Flaherty, S., Fang, R., Raichle, M. E., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.017 · Cited by 123
Schulz, K. P., Fan, J., Bédard, A.-C. V., Clerkin, S. M., Ivanov, I., Tang, C. Y., Halperin, J. M., & Newcorn, J. H. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.2053 · Cited by 114
Ma, Z.-Z., Wu, J.-J., Cao, Z., Hua, X.-Y., Zheng, M.-X., Xing, X.-X., Ma, J., & Xu, J.-G. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-024-01387-w · Cited by 97
Gesture in the developing brain
Dick, A. S., Goldin‐Meadow, S., Solodkin, A., & Small, S. L. (2012) · Developmental Science · doi:10.1111/j.1467-7687.2011.01100.x · Cited by 60
Visual-motor functional connectivity in preschool children emerges after handwriting experience
Vinci-Booher, S., James, T. W., & James, K. H. (2016) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2016.07.006 · Cited by 58
Disrupted prefrontal activity during emotion processing in complicated grief: An fMRI investigation
Arizmendi, B., Kaszniak, A. W., & O'Connor, M.-F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.054 · Cited by 57
Holsen, L. M., Dalton, K. M., Johnstone, T., & Davidson, R. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.009 · Cited by 56
Self-citationWSPM: Wavelet-based statistical parametric mapping
Van De Ville, D., Seghier, M. L., Lazeyras, F., Blu, T., & Unser, M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.06.011 · Cited by 46
Ventromedial Prefrontal Cortex Lesions Alter Neural and Physiological Correlates of Anticipation
Motzkin, J. C., Philippi, C. L., Wolf, R. C., Baskaya, M. K., & Koenigs, M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1446-14.2014 · Cited by 44
Schulz, K. P., Clerkin, S. M., Fan, J., Halperin, J. M., & Newcorn, J. H. (2013) · Psychopharmacology · doi:10.1007/s00213-012-2893-8 · Cited by 35
Guanfacine potentiates the activation of prefrontal cortex evoked by warning signals
Clerkin, S. M., Schulz, K. P., Halperin, J. M., Newcorn, J. H., Ivanov, I., Tang, C. Y., & Fan, J. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.04.013 · Cited by 28
Keulers, E. H. H., Goulas, A., Jolles, J., & Stiers, P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.079 · Cited by 16
Metadata
- Authors
- Johnstone, T., Ores Walsh, K. S., Greischar, L. L., Alexander, A. L., Fox, A. S., Davidson, R. J., & Oakes, T. R.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2006-09-22
- Volume
- 27
- Issue
- 10
- Pages
- 779-788
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- OpenAlex
- W2103040438
- Cited by
- 320
- References
- 3
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
