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Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla
Open accessAbstract
This study investigated the spatio-temporal properties of blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signals in gray matter, excluding the confounding, inaccurate contributions of large blood vessels. We quantified the spatial specificity of the BOLD response, and we investigated whether this specificity varies as a function of time from stimulus onset. fMRI was performed at 7 Tesla (T), where mapping signals of parenchymal origin are easily detected. Two abutting visual stimuli were adjusted to elicit responses centered on a flat gray matter region in V1. fMRI signals were sampled at high-resolution orthogonal to the retinotopic boundary between the representations of the stimuli. Signals from macro-vessels were masked out. Principal component analysis revealed that the first component in space accounted for 96.2+/-1.6% of the variance over time. The spatial profile of this time-invariant response was fitted with a model consisting of the convolution of a step function and a Gaussian point-spread-function (PSF). The mean full-width at half-maximal-height of the fitted PSF was 2.34+/-0.20 mm. Based on simulations of confounding effects, we estimate that BOLD PSF in human gray matter is smaller than 2 mm. A time-point to time-point analysis revealed that the PSF obtained during the 3rd (1.52 mm) and 4th (1.99 mm) seconds of stimulation were narrower than the mean PSF obtained from the 5th second on (2.42+/-0.15 mm). The position of the edge of the responding region was offset (1.72+/-0.07 mm) from the boundary of the stimulated region, indicating a spatial non-linearity. Simulations showed that the effective contrast between active and non-active columns is reduced 25-fold when imaged using a PSF whose width is equal to the cycle of the imaged columnar organization. Thus, the PSF of the hyper-oxygenated BOLD response in human gray matter is narrower than that reported at 1.5 T, where macro-vessels dominate the mapping signals. The initial phase of this response is more spatially specific than later phases. Data acquisition methods that suppress macro-vascular signals should increase the spatial specificity of BOLD fMRI. The choice of optimal stimulus duration represents a trade-off between the spatial specificity and the overhead associated with short stimulus duration.
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“At the low field strengths suitable for scanning children and adolescents (<7 T), the PSF in a region of cortex is thought to be largely determined by proximity to local blood vessels, rather than by local grey matter density, horizontal connections, synapses and so on (Sirotin et al., 2009; Shmuel et al., 2007).”
From The social brain in adolescence: evidence from functional magnetic resonance imaging and behavioural studies (2011) · cites this paper as Shmuel et al., 2007
Citations
Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.24.9868 · Cited by 6,483
SENSE: Sensitivity encoding for fast MRI
Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5 · Cited by 6,303
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5675 · Cited by 4,212
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.13.5951 · Cited by 3,681
Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1
Boynton, G., Engel, S., Glover, G., & Heeger, D. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-13-04207.1996 · Cited by 2,522
Fox, P. T., & Raichle, M. E. (1986) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.83.4.1140 · Cited by 2,056
Time course EPI of human brain function during task activation
Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., & Hyde, J. S. (1992) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910250220 · Cited by 1,935
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835
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- Duvernoy, 1981, Vascularization of the human cerebral-cortex, Acta Anatomica, 111, 36
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Keliris, G. A., Shmuel, A., Ku, S.-P., Pfeuffer, J., Oeltermann, A., Steudel, T., & Logothetis, N. K. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.057 · Cited by 41
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Kamba, M., Sung, Y., & Ogawa, S. (2007) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21193 · Cited by 11
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Kleinschmidt, A. K. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.03.047 · Cited by 8
Chaimow, D., & Shmuel, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/097154 · Cited by 8
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Wu, H., Chang, K.-J., Wen, X., Yarmishyn, A. A., Dai, H.-J., Chan, K.-H., Hsiao, Y., Chen, M.-T., Chien, Y., Ma, H., et al. (2022) · Materials Today Advances · doi:10.1016/j.mtadv.2022.100322 · Cited by 6
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Yacoub, E. S., Harel, N., & Shmuel, A. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_26 · Cited by 4
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Locally Measured Neuronal Correlates of Functional MRI Signals
Shmuel, A., & Maier, A. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_6 · Cited by 6
Self-citationAmor, Z., Ciuciu, P., Radhakrishna, C. G., Daval‐Frérot, G., Mauconduit, F., Thirion, B., & Vignaud, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299925 · Cited by 4
Chaimow, D., Lorenz, R., & Weiskopf, N. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0085 · Cited by 3
Self-citationfMRI Using GRAPPA EPI with High Spatial Resolution Improves BOLD Signal Detection at 3T
Mintzopoulos, D., Astrakas, L. G., Hiroki, M., Khanicheh, A., Wald, L. L., Rosen, B. R., & Tzika, A. A. (2009) · The Open Magnetic Resonance Journal · doi:10.2174/1874769800902010057 · Cited by 4
de Beeck, H. O., & Nakatani, C. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316847916.017 · Cited by 4
Metadata
- Authors
- Shmuel, A., Yacoub, E., Chaimow, D., Logothetis, N. K., & Ugurbil, K.
- Affiliations
- University of Minnesota (US)
- Max Planck Institute for Biological Cybernetics (DE)
- Corresponding author
- Shmuel, Amir
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2007-04
- Volume
- 35
- Issue
- 2
- Pages
- 539-552
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2989431
- OpenAlex
- W176484045
- Cited by
- 294
- References
- 57
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 14 · 2023: 10 · 2022: 8 · 2021: 15 · 2020: 10 · 2019: 30 · 2018: 12 · 2017: 22 · 2016: 20 · 2015: 16 · 2014: 19 · 2013: 17 · 2012: 18
- FWCI
- 12.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- European Molecular Biology Organization
- National Institutes of Health
- National Institute of Mental Health (R01MH070800)
- National Institute of Biomedical Imaging and Bioengineering (R01 EB00331, R01EB000331)
- National Center for Research Resources (P41RR008079, P41RR08079)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- point spread function, BOLD signal, functional MRI, gray matter, 7 Tesla, spatial specificity, primary visual cortex, columnar organization, stimulus duration
- MeSH terms
- Adult, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Mathematics, Oxygen, Time Factors
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