The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Coupling Between Neuronal Firing, Field Potentials, and fMRI in Human Auditory Cortex
Abstract
Functional magnetic resonance imaging (fMRI) is an important tool for investigating human brain function, but the relationship between the hemodynamically based fMRI signals in the human brain and the underlying neuronal activity is unclear. We recorded single unit activity and local field potentials in auditory cortex of two neurosurgical patients and compared them with the fMRI signals of 11 healthy subjects during presentation of an identical movie segment. The predicted fMRI signals derived from single units and the measured fMRI signals from auditory cortex showed a highly significant correlation ( r = 0.75, P < 10 –47 ). Thus, fMRI signals can provide a reliable measure of the firing rate of human cortical neurons.
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
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Neurovascular regulation in the normal brain and in Alzheimer's disease
Iadecola, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1387 · Cited by 2,213
Intersubject synchronization of cortical activity during natural vision
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004) · Science · doi:10.1126/science.1089506 · Cited by 1,839
Self-citationLaufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., & Kleinschmidt, A. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1831638100 · Cited by 1,028
Cited by
High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2006) · Science · doi:10.1126/science.1128115 · Cited by 2,650
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition
Wang, X.-J. (2010) · Physiological Reviews · doi:10.1152/physrev.00035.2008 · Cited by 1,939
Raichle, M. E., & Mintun, M. A. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112819 · Cited by 1,630
Low-frequency neuronal oscillations as instruments of sensory selection
Schroeder, C. E., & Lakatos, P. (2009) · Trends in Neurosciences · doi:10.1016/j.tins.2008.09.012 · Cited by 1,528
The cognitive neuroscience of ageing
Grady, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3256 · Cited by 1,286
Large-scale cortical correlation structure of spontaneous oscillatory activity
Hipp, J. F., Hawellek, D. J., Corbetta, M., Siegel, M., & Engel, A. K. (2012) · Nature Neuroscience · doi:10.1038/nn.3101 · Cited by 1,113
Cortical oscillations and sensory predictions
Arnal, L. H., & Giraud, A.-L. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.05.003 · Cited by 1,114
Manning, J. R., Jacobs, J., Fried, I., & Kahana, M. J. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2041-09.2009 · Cited by 973
Self-citationReward-motivated learning: mesolimbic activation precedes memory formation
Adcock, R. A., Thangavel, A., Whitfield-Gabrieli, S., Knutson, B., & Gabrieli, J. D. E. (2006) · Neuron · doi:10.1016/j.neuron.2006.03.036 · Cited by 977
Trujillo, C. A., Gao, R., Negraes, P. D., Gu, J., Buchanan, J., Preissl, S., Wang, A., Wu, W., Haddad, G. G., Chaim, I. A., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.08.002 · Cited by 974
Shmuel, A., Augath, M., Oeltermann, A., & Logothetis, N. K. (2006) · Nature Neuroscience · doi:10.1038/nn1675 · Cited by 898
Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms
Buzsáki, G., Logothetis, N., & Singer, W. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.002 · Cited by 886
The DIVA model: A neural theory of speech acquisition and production
Tourville, J. A., & Guenther, F. H. (2011) · Language and Cognitive Processes · doi:10.1080/01690960903498424 · Cited by 748
High-Frequency Brain Activity and Muscle Artifacts in MEG/EEG: A Review and Recommendations
Muthukumaraswamy, S. D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00138 · Cited by 697
Electrophysiological correlates of the brain's intrinsic large-scale functional architecture
He, B. J., Snyder, A. Z., Zempel, J. M., Smyth, M. D., & Raichle, M. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807010105 · Cited by 670
Nir, Y., Fisch, L., Mukamel, R., Gelbard-Sagiv, H., Arieli, A., Fried, I., & Malach, R. (2007) · Current Biology · doi:10.1016/j.cub.2007.06.066 · Cited by 621
Self-citationQuestions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Voytek, B., & Knight, R. T. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.04.016 · Cited by 556
Slow cortical dynamics and the accumulation of information over long timescales
Honey, C. J., Thesen, T., Donner, T. H., Silbert, L. J., Carlson, C. E., Devinsky, O., Doyle, W. K., Rubin, N., Heeger, D. J., & Hasson, U. (2012) · Neuron · doi:10.1016/j.neuron.2012.08.011 · Cited by 532
Self-citationPromises and limitations of human intracranial electroencephalography
Parvizi, J., & Kastner, S. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0108-2 · Cited by 631
Neurophysiology of the BOLD fMRI Signal in Awake Monkeys
Goense, J. B. M., & Logothetis, N. K. (2008) · Current Biology · doi:10.1016/j.cub.2008.03.054 · Cited by 583
Biophysical and Physiological Origins of Blood Oxygenation Level-Dependent fMRI Signals
Kim, S.-G., & Ogawa, S. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2012.23 · Cited by 547
Swann, N., Tandon, N., Canolty, R., Ellmore, T. M., McEvoy, L. K., Dreyer, S., DiSano, M., & Aron, A. R. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3359-09.2009 · Cited by 474
Boto, E., Meyer, S. S., Shah, V., Alem, O., Knappe, S., Kruger, P., Fromhold, T. M., Lim, M., Glover, P. M., Morris, P. G., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.034 · Cited by 471
Single-trial EEG–fMRI reveals the dynamics of cognitive function
Debener, S., Ullsperger, M., Siegel, M., & Engel, A. K. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.09.010 · Cited by 428
Energy-Efficient Action Potentials in Hippocampal Mossy Fibers
Alle, H., Roth, A., & Geiger, J. R. P. (2009) · Science · doi:10.1126/science.1174331 · Cited by 429
Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity
Viswanathan, A., & Freeman, R. D. (2007) · Nature Neuroscience · doi:10.1038/nn1977 · Cited by 397
Transitions in neural oscillations reflect prediction errors generated in audiovisual speech
Arnal, L. H., Wyart, V., & Giraud, A.-L. (2011) · Nature Neuroscience · doi:10.1038/nn.2810 · Cited by 353
Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions
Hasson, U., & Frith, C. D. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2015.0366 · Cited by 371
Self-citationYuan, H., Zotev, V., Phillips, R., Drevets, W. C., & Bodurka, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.031 · Cited by 352
Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns
Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508523113 · Cited by 296
The fMRI signal, slow cortical potential and consciousness
He, B. J., & Raichle, M. E. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.004 · Cited by 332
The relation of brain oscillations to attentional networks
Fan, J., Byrne, J., Worden, M. S., Guise, K. G., McCandliss, B. D., Fossella, J., & Posner, M. I. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1833-07.2007 · Cited by 286
Yuan, H., Liu, T., Szarkowski, R., Rios, C., Ashe, J., & He, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.028 · Cited by 274
Syntactic structure building in the anterior temporal lobe during natural story listening
Brennan, J., Nir, Y., Hasson, U., Malach, R., Heeger, D. J., & Pylkkänen, L. (2012) · Brain and Language · doi:10.1016/j.bandl.2010.04.002 · Cited by 279
Self-citationA brief history of human brain mapping
Raichle, M. E. (2009) · Trends in Neurosciences · doi:10.1016/j.tins.2008.11.001 · Cited by 286
Movement related activity in the high gamma range of the human EEG
Ball, T., Demandt, E., Mutschler, I., Neitzel, E., Mehring, C., Vogt, K., Aertsen, A., & Schulze-Bonhage, A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.032 · Cited by 274
Hyder, F., Rothman, D. L., & Bennett, M. R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214912110 · Cited by 271
Hippocampal sharp-wave ripples linked to visual episodic recollection in humans
Norman, Y., Yeagle, E. M., Khuvis, S., Harel, M., Mehta, A. D., & Malach, R. (2019) · Science · doi:10.1126/science.aax1030 · Cited by 270
Self-citationKeller, C. J., Bickel, S., Honey, C. J., Groppe, D. M., Entz, L., Craddock, R. C., Lado, F. A., Kelly, C., Milham, M., & Mehta, A. D. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4837-12.2013 · Cited by 245
Minzenberg, M. J., Firl, A. J., Yoon, J. H., Gomes, G. C., Reinking, C., & Carter, C. S. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2010.150 · Cited by 247
Evaluating the gray and white matter energy budgets of human brain function
Yu, Y., Herman, P., Rothman, D. L., Agarwal, D., & Hyder, F. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x17708691 · Cited by 211
Golfinopoulos, E., Tourville, J. A., & Guenther, F. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.023 · Cited by 200
On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study
Boto, E., Bowtell, R., Krüger, P., Fromhold, T. M., Morris, P. G., Meyer, S. S., Barnes, G. R., & Brookes, M. J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0157655 · Cited by 194
Dissociation of broadband high-frequency activity and neuronal firing in the neocortex
Leszczyński, M., Barczak, A., Kajikawa, Y., Ulbert, I., Falchier, A. Y., Tal, I., Haegens, S., Melloni, L., Knight, R. T., & Schroeder, C. E. (2020) · Science Advances · doi:10.1126/sciadv.abb0977 · Cited by 188
Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
He, B., & Liu, Z. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008233 · Cited by 171
Electrophysiological correlates of the BOLD signal for EEG-informed fMRI
Murta, T., Leite, M., Carmichael, D. W., Figueiredo, P., & Lemieux, L. (2015) · Human Brain Mapping · doi:10.1002/hbm.22623 · Cited by 165
Wang, L., Saalmann, Y. B., Pinsk, M. A., Arcaro, M. J., & Kastner, S. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.033 · Cited by 158
The “Task B problem” and other considerations in developmental functional neuroimaging
Church, J. A., Petersen, S. E., & Schlaggar, B. L. (2010) · Human Brain Mapping · doi:10.1002/hbm.21036 · Cited by 148
Luan, L., Robinson, J. T., Aazhang, B., Chi, T., Yang, K., Li, X., Rathore, H., Singer, A., Yellapantula, S., Fan, Y., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.011 · Cited by 150
Rauch, A., Rainer, G., & Logothetis, N. K. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800312105 · Cited by 146
Functional fission of parvalbumin interneuron classes during fast network events
Varga, C., Oijala, M., Lish, J., Szabo, G. G., Bezaire, M., Marchionni, I., Golshani, P., & Soltesz, I. (2014) · eLife · doi:10.7554/elife.04006 · Cited by 130
Exploring the electrophysiological correlates of the default-mode network with intracerebral EEG
Jerbi (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00027 · Cited by 133
Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal
Wen, H., & Liu, Z. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0187-16.2016 · Cited by 100
Spontaneous Fluctuations and Non-linear Ignitions: Two Dynamic Faces of Cortical Recurrent Loops
Moutard, C., Dehaene, S., & Malach, R. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.018 · Cited by 99
Self-citationEstimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI
Rosa, M. J., Kilner, J., Blankenburg, F., Josephs, O., & Penny, W. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.011 · Cited by 105
fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints
Liu, Z., & He, B. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.003 · Cited by 103
On the relationship between slow cortical potentials and BOLD signal changes in humans
Khader, P., Schicke, T., Röder, B., & Rösler, F. (2008) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2007.05.018 · Cited by 99
Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall
Dickey, C. W., Verzhbinsky, I. A., Jiang, X., Rosen, B. Q., Kajfez, S., Stedelin, B., Shih, J. J., Ben-Haim, S., Raslan, A. M., Eskandar, E. N., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2107797119 · Cited by 87
Muckli, L. (2010) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.20236 · Cited by 94
Ojemann, G. A., Ojemann, J., & Ramsey, N. F. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00034 · Cited by 92
Heterogeneous Single-Unit Selectivity in an fMRI-Defined Body-Selective Patch
Popivanov, I. D., Jastorff, J., Vanduffel, W., & Vogels, R. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.2748-13.2014 · Cited by 90
Temporal scaling of neural responses to compressed and dilated natural speech
Lerner, Y., Honey, C. J., Katkov, M., & Hasson, U. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00497.2013 · Cited by 75
Self-citationLinear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals
Liu, Z., Rios, C., Zhang, N., Yang, L., Chen, W., & He, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.017 · Cited by 88
Collinger, J. L., Kryger, M. A., Barbara, R., Betler, T., Bowsher, K., Brown, E. H. P., Clanton, S. T., Degenhart, A. D., Foldes, S. T., Gaunt, R. A., et al. (2014) · Clinical and Translational Science · doi:10.1111/cts.12086 · Cited by 68
Klink, P. C., Chen, X., Vanduffel, W., & Roelfsema, P. R. (2021) · eLife · doi:10.7554/elife.67304 · Cited by 63
Ozker, M., Doyle, W., Devinsky, O., & Flinker, A. (2022) · PLOS Biology · doi:10.1371/journal.pbio.3001493 · Cited by 59
Mash, L. E., Reiter, M. A., Linke, A. C., Townsend, J., & Müller, R. (2018) · Developmental Neurobiology · doi:10.1002/dneu.22570 · Cited by 52
Local neuronal relational structures underlying the contents of human conscious experience
Malach, R. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab028 · Cited by 53
Self-citationBarnett, A. J., Nguyen, M., Spargo, J., Yadav, R., Cohn-Sheehy, B. I., & Ranganath, C. (2024) · Neuron · doi:10.1016/j.neuron.2023.10.010 · Cited by 55
Bönstrup, M., Schulz, R., Feldheim, J., Hummel, F. C., & Gerloff, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.052 · Cited by 56
Vishne, G., Gerber, E. M., Knight, R. T., & Deouell, L. Y. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112752 · Cited by 46
A rapid-onset diffusion functional MRI signal reflects neuromorphological coupling dynamics
Nunes, D., Gil, R., & Shemesh, N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117862 · Cited by 43
Attention strongly increases oxygen metabolic response to stimulus in primary visual cortex
Moradi, F., Buračas, G. T., & Buxton, R. B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.078 · Cited by 42
Early cortical signals in visual stimulus detection
Kwon, H., Kronemer, S. I., Christison-Lagay, K. L., Khalaf, A., Li, J., Ding, J. Z., Freedman, N. C., & Blumenfeld, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118608 · Cited by 31
Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., & Hu, X. P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00246 · Cited by 29
Perceptual stability reflected in neuronal pattern similarities in human visual cortex
Broday-Dvir, R., Norman, Y., Harel, M., Mehta, A. D., & Malach, R. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112614 · Cited by 28
Self-citationFrontal cortex selects representations of the talker’s mouth to aid in speech perception
Ozker, M., Yoshor, D., & Beauchamp, M. S. (2018) · eLife · doi:10.7554/elife.30387 · Cited by 24
Noy, N., Bickel, S., Zion‐Golumbic, E., Harel, M., Golan, T., Davidesco, I., Schevon, C. A., McKhann, G. M., Goodman, R. R., Schroeder, C. E., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22892 · Cited by 17
Self-citationKim, J.-H., De Asis-Cruz, J., Krishnamurthy, D., & Limperopoulos, C. (2023) · eLife · doi:10.7554/elife.80878 · Cited by 10
Poghosyan, V., Ioannou, S., Al-Amri, K. M., Al-Mashhadi, S. A., Al-Mohammed, F., Al-Otaibi, T., & Al-Saeed, W. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.948063 · Cited by 8
Theriault (n.d.) · doi:10.31234/osf.io/pkzr8 · Cited by 51
(2020) · doi:10.1017/9781108339216 · Cited by 2
Metadata
- Authors
- Mukamel, R., Gelbard, H., Arieli, A., Hasson, U., Fried, I., & Malach, R.
- Venue
- Science
- Published
- 2005-08-05
- Volume
- 309
- Issue
- 5736
- Pages
- 951-954
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2052314238
- Cited by
- 1,035
- References
- 4
- FWCI
- 34.99
- Type
- journal article
- Language
- en
- Open access
- closed
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.