The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Data Quality Influences Observed Links Between Functional Connectivity and Behavior
Open accessAbstract
A growing field of research explores links between behavioral measures and functional connectivity (FC) assessed using resting-state functional magnetic resonance imaging. Recent studies suggest that measurement of these relationships may be corrupted by head motion artifact. Using data from the Human Connectome Project (HCP), we find that a surprising number of behavioral, demographic, and physiological measures (23 of 122), including fluid intelligence, reading ability, weight, and psychiatric diagnostic scales, correlate with head motion. We demonstrate that "trait" (across-subject) and "state" (across-day, within-subject) effects of motion on FC are remarkably similar in HCP data, suggesting that state effects of motion could potentially mimic trait correlates of behavior. Thus, head motion is a likely source of systematic errors (bias) in the measurement of FC:behavior relationships. Next, we show that data cleaning strategies reduce the influence of head motion and substantially alter previously reported FC:behavior relationship. Our results suggest that spurious relationships mediated by head motion may be widespread in studies linking FC to behavior.
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Citations
Cites
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747
Disruption of Large-Scale Brain Systems in Advanced Aging
Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., & Buckner, R. L. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.038 · Cited by 1,592
Self-citationResting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502
Self-citationSmith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., Ugurbil, K., Barch, D. M., Van Essen, D. C., & Miller, K. L. (2015) · Nature Neuroscience · doi:10.1038/nn.4125 · Cited by 859
Wise, R. G., Ide, K., Poulin, M. J., & Tracey, I. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.11.025 · Cited by 664
Fair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., Schlaggar, B. L., Mennes, M., Gutman, D., Bangaru, S., et al. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00080 · Cited by 463
Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht040 · Cited by 187 · cited 2×
“As approaches for removing motion artifact became more widely recognized, several papers incorporating appropriate controls for head motion reported largely typical findings in high functioning adults with ASD (Redcay et al. 2013; Tyszka et al. 2014; Mitra et al. 2015).”
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Baniqued, P. L., Gallen, C. L., Voss, M. W., Burzynska, A. Z., Wong, C. N., Cooke, G. E., Duffy, K., Fanning, J., Ehlers, D. K., Salerno, E. A., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00426 · Cited by 112
Siddiqi, S. H., Trapp, N. T., Hacker, C. D., Laumann, T. O., Kandala, S., Hong, X., Trillo, L., Shahim, P., Leuthardt, E. C., Carter, A. R., et al. (2019) · Journal of Neurotrauma · doi:10.1089/neu.2018.5889 · Cited by 106
Self-citationRegional GABA Concentrations Modulate Inter-network Resting-state Functional Connectivity
Chen, X., Fan, X., Hu, Y., Zuo, C., Whitfield-Gabrieli, S., Holt, D., Gong, Q., Yang, Y., Pizzagalli, D. A., Du, F., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy059 · Cited by 62
Functional split brain in a driving/listening paradigm
Sasai, S., Boly, M., Mensen, A., & Tononi, G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613200113 · Cited by 43
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2018) · doi:10.1101/473603 · Cited by 16
Metadata
- Authors
- Siegel, J. S., Mitra, A., Laumann, T. O., Seitzman, B. A., Raichle, M., Corbetta, M., & Snyder, A. Z.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-08-22
- Volume
- 27
- Issue
- 9
- Pages
- 4492-4502
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2509256556
- Cited by
- 265
- References
- 8
- FWCI
- 20.37
- Type
- journal article
- Language
- en
- Open access
- green
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