The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling
Open accessAbstract
Most brain research to date has focused on studying the amplitude of evoked fMRI responses, though there has recently been an increased interest in measuring onset, peak latency and duration of the responses as well. A number of modeling procedures provide measures of the latency and duration of fMRI responses. In this work we compare several techniques that vary in their assumptions, model complexity, and interpretation. For each method, we introduce methods for estimating amplitude, peak latency, and duration and for performing inference in a multi-subject fMRI setting. We then assess the techniques’ relative sensitivity and their propensity for mis-attributing task effects on one parameter (e.g., duration) to another (e.g., amplitude). Finally, we introduce methods for quantifying model misspecification and assessing bias and power-loss related to the choice of model. Overall, the results show that it is surprisingly difficult to accurately recover true task-evoked changes in BOLD signal and that there are substantial differences among models in terms of power, bias and parameter confusability. Because virtually all fMRI studies in cognitive and affective neuroscience employ these models, the results bear on the interpretation of hemodynamic response estimates across a wide variety of psychological and neuroscientific studies.
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Cites
Mediation in experimental and nonexperimental studies: New procedures and recommendations.
Shrout, P. E., & Bolger, N. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.4.422 · Cited by 11,335
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Cited by
An fMRI-Based Neurologic Signature of Physical Pain
Wager, T. D., Atlas, L. Y., Lindquist, M. A., Roy, M., Woo, C.-W., & Kross, E. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1204471 · Cited by 1,597
Self-citationBrain mediators of predictive cue effects on perceived pain
Atlas, L. Y., Bolger, N., Lindquist, M. A., & Wager, T. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.0057-10.2010 · Cited by 389
Self-citationfMRI reveals language-specific predictive coding during naturalistic sentence comprehension
Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215
State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices
Hong, K.-S., & Nguyen, H.-D. (2014) · Biomedical Optics Express · doi:10.1364/boe.5.001778 · Cited by 92
Lopez-Duran, N. L., Mayer, S. E., & Abelson, J. L. (2014) · Stress · doi:10.3109/10253890.2014.915517 · Cited by 85
Metadata
- Authors
- Lindquist, M. A., Meng Loh, J., Atlas, L. Y., & Wager, T. D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 45
- Issue
- 1
- Pages
- S187-S198
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2151969869
- Cited by
- 551
- References
- 2
- FWCI
- 5.97
- Type
- journal article
- Language
- en
- Open access
- green
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