Relation between functional magnetic resonance imaging (fMRI) and single neuron, local field potential (LFP) and electrocorticography (ECoG) activity in human cortex
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The relation between changes in the blood oxygen dependent metabolic changes imaged by functional magnetic resonance imaging (fMRI) and neural events directly recorded from human cortex from single neurons, local field potentials (LFPs) and electrocorticogram (ECoG) is critically reviewed, based on the published literature including findings from the authors' laboratories. All these data are from special populations, usually patients with medically refractory epilepsy, as this provides the major opportunity for direct cortical neuronal recording in humans. For LFP and ECoG changes are often sought in different frequency bands, for single neurons in frequency of action potentials. Most fMRI studies address issues of functional localization. The relation of those findings to localized changes in neuronal recordings in humans has been established in several ways. Only a few studies have directly compared changes in activity from the same sites in the same individual, using the same behavioral measure. More often the comparison has been between fMRI and electrophysiologic changes in populations recorded from the same functional anatomic system as defined by lesion effects; in a few studies those systems have been defined by fMRI changes such as the "default" network. The fMRI-electrophysiologic relationships have been evaluated empirically by colocalization of significant changes, and by quantitative analyses, often multiple linear regression. There is some evidence that the fMRI-electrophysiology relationships differ in different cortical areas, particularly primary motor and sensory cortices compared to association cortex, but also within areas of association cortex. Although crucial for interpretation of fMRI changes as reflecting neural activity in human cortex, controversy remains as to these relationships. Supported by: Dutch Technology Foundation and University of Utrecht Grant UGT7685, ERC-Advanced grant 320708 (NR) and NIH grant NS065186 (JO).
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Gamma activity has been shown to correlate with brain activation and fMRI both in primary sensory motor cortices and in association cortex.
“Nevertheless, gamma activity has been shown to correlate with brain activation and fMRI both in primary sensory motor cortices and in association cortex (see recent review by Ojemann and cooleague; Ojemann, Ojemann, & Ramsey, 2013).”
From Brain oscillations, hypnosis, and hypnotizability (2015) · cites this paper as Ojemann, Ojemann, & Ramsey, 2013
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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 6,542 · see citing works
Glial and neuronal control of brain blood flow
Attwell, D., Buchan, A. M., Charpak, S., Lauritzen, M., MacVicar, B. A., & Newman, E. A. (2010) · Nature · doi:10.1038/nature09613 · Cited by 2,511 · see citing works
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 · see citing works
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Research works printed but not linked: 49
- Bennett C. M. Miller M. B. (2010). How reliable are the results from functional magnetic resonance imaging? Ann. N. Y. Acad. Sci. 1191, 33– 55 20392279
- Bettus G. Ranjeva J.-P. Wendling F. Benar C. Confort-Gouny S. Regis J. (2011). Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations. PLoS ONE 6: e20071 10.1371/journal.pone.0020071 21625517 PMC3098283 · DOI
- Birn R. M. (2012). The role of physiological noise in resting-state functional connectivity. Neuroimage 62, 864– 870 10.1016/j.neuroimage.2012.01.016 22245341 PMC13374118 · DOI
- Buzsáki G. Wang X. J. (2012). Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203– 225 22443509 PMC4049541
- Conner C. R. Ellmore T. M. Peters T. A. DiSano M. A. Tandon N. (2011). Variability of the relationship between electrophysiology and BOLD-fMRI across cortical regions in humans. J. Neurosci. 31, 12855– 12865 10.1523/JNEUROSCI.1457-11.2011 21900564 PMC3322193 · DOI
- Ekstrom A. (2010). How and why the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res. Rev. 62, 233– 244 10.1016/j.brainresrev.2009.12.004 20026191 PMC3546820 · DOI
- Ekstrom A. Suthana N. Millett D. Fried I. Bookheimer S. (2009). Correlation between BOLD fMRI and theta-band local field potentials in the human hippocampal area. J. Neurophysiol. 101, 2668– 2678 10.1152/jn.91252.2008 19244353 PMC2681439 · DOI
- Ekstrom A. Viskontas I. Kahana M. Jacobs J. Upchurch K. Bookheimer S. (2007). Contrasting roles of neural firing rate and local field potentials in human memory. Hippocampus 17, 606– 617 10.1002/hipo.20300 17546683 PMC3568989 · DOI
- Engel A. K. Fries P. Singer W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704– 716 10.1038/35094565 11584308 · DOI · Paper page
- Engel J. Jr. Kuhl D. E. Phelps M. E. Mazziota J. C. (1982). Interictal cerebral glucose metabolism in partial epilepsy and its relation to EEG change. Ann. Neurol. 12, 510– 517 10.1002/ana.410120603 6818896 · DOI
- Engell A. D. Huettel S. McCarthy G. (2012). The fMRI BOLD signal tracks electrophysiological spectral perturbations, not event-related potentials. Neuroimage 59, 2600– 2606 10.1016/j.neuroimage.2011.08.079 21925278 PMC3277784 · DOI
- Freeman W. J. Rogers L. J. Holmes M. D. Silbergeld D. L. (2000). Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands. J. Neurosci. Methods 95, 111– 121 10.1016/S0165-0270(99)00160-0 10752481 · DOI
- Gao J. H. Liu H. L. (2012). Inflow effects on functional MRI. Neuroimage 62, 1035– 1039 10.1016/j.neuroimage.2011.09.088 22019882 · DOI
- Giussani C. Roux F.-E. Ojemann J. Sganzerla E. P. Pirillo D. Papagno C. (2010). Is preoperative functional magnetic resonance imaging reliable for language areas mapping in brain tumor surgery? Review of language functional magnetic imaging and direct cortical stimulation studies. Neurosurgery 66, 113– 120 10.1227/01.NEU.0000360392.15450.C9 19935438 · DOI
- Haegens S. Luther L. Jensen O. (2012). Somatosensory anticipatory alpha activity increases to suppress distracting input. J. Cogn. Neurosci. 24, 677– 685 10.1162/jocn_a_00164 22066587 · DOI
- Harvey B. M. Vansteensel M. J. Ferrier C. H. Petridou N. Zuiderbaan W. Aarnoutse E. J. (2013). Frequency specific spatial interactions in human electrocorticography: V1 alpha oscillations reflect surround suppression. Neuroimage 65, 424– 432 10.1016/j.neuroimage.2012.10.020 23085107 · DOI
- He B. J. Snyder A. Z. Zempel J. M. Smyth M. D. Raichle M. E. (2008). Electrophysiological correlates of the brain's intrinsic large-scale functional architecture. Proc. Natl. Acad. Sci. U.S.A. 105, 16039– 16044 10.1073/pnas.0807010105 18843113 PMC2564983 · DOI · Paper page
- Heeger D. J. Ress D. (2002). What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3, 142– 151 10.1038/nrn730 11836522 · DOI
- Hermes D. Miller K. J. Noordmans H. J. Vansteensel M. J. Ramsey N. F. (2010). Automated electrocorticographic electrode localization on individually rendered brain surfaces. J. Neurosci. Methods 185, 293– 298 10.1016/j.jneumeth.2009.10.005 19836416 · DOI
- Hermes D. Miller K. J. Vansteensel M. J. Aarnoutse E. J. Leijten F. S. S. Ramsey N. F. (2012). Neurophysiologic correlates of fMRI in human motor cortex. Hum. Brain Mapp. 33, 1689– 1699 10.1002/hbm.21314 21692146 PMC6870225 · DOI
- Huettel S. A. McKeown M. J. Song A. W. Hart S. Spencer D. D. Allison T. (2004). Linking hemodynamic and electrophysiological measures of brain activity: evidence from functional MRI and intracranial field potentials. Cereb. Cortex 14, 165– 173 10.1093/cercor/bhg115 14704213 · DOI
- Katzner S. Nauhaus I. Benucci A. Bonin V. Ringach D. L. Carandini M. (2009). Local origin of field potentials in visual cortex. Neuron 61, 35– 41 10.1016/j.neuron.2008.11.016 19146811 PMC2730490 · DOI
- Khursheed F. Tandon N. Tertel K. Pieters T. A. Disano M. A. Ellmore T. M. (2011). Frequency-specific electrocorticographic correlates of working memory delay period fMRI activity. Neuroimage 56, 1773– 1782 10.1016/j.neuroimage.2011.02.062 21356314 PMC3085578 · DOI
- Ko A. Darvas F. Poliakov A. Ojemann J. Sorenson L. B. (2011). Quasi-periodic fluctuations in default mode network electrophysiology. J. Neurosci. 31, 11728– 11732 10.1523/JNEUROSCI.5730-10.2011 21832202 PMC6623135 · DOI
- Lachaux J.-P. Fonlupt P. Kahane P. Minotti L. Hoffman D. Bertrand O. (2007). Relationship between task-related gamma oscillations and BOLD signal: new insights from combined fMRI and intracranial EEG. Hum. Brain Mapp. 28, 1368– 1375 10.1002/hbm.20352 17274021 PMC6871347 · DOI
- Logothetis N. K. (2008). What we can do and what we cannot do with fMRI. Nature 453, 869– 878 10.1038/nature06976 18548064 · DOI · Paper page
- McDonald C. R. Thesen T. Carlson C. Blumberg M. Girard H. M. Trongnetrpunya A. (2010). Multimodal imaging of repetition priming: using fMRI, MEG and intracranial EEG to reveal spatiotemporal profiles of word processing. Neuroimage 53, 707– 717 10.1016/j.neuroimage.2010.06.069 20620212 PMC2930128 · DOI
- Meltzer J. A. Zaveri H. P. Goncharova I. I. Distasio M. M. Papademetris X. Spencer S. S. (2008). Effects of working memory load on oscillatory power in human intracranial EEG. Cereb. Cortex 18, 1843– 1855 10.1093/cercor/bhm213 18056698 PMC2474453 · DOI
- Miller K. J. Hermes D. Honey C. J. Hebb A. O. Ramsey N. F. Knight R. T. (2012). Human motor cortical activity is selectively phase-entrained on underlying rhythms. PLoS Comput. Biol. 8: e1002655 10.1371/journal.pcbi.1002655 22969416 PMC3435268 · DOI
- Miller K. J. Sorensen L. B. Ojemann J. G. den Nijs M. (2009). Power-law scaling in the brain surface electric potential. PLoS Comput. Biol. 5: e1000609 10.1371/journal.pcbi.1000609 20019800 PMC2787015 · DOI · Paper page
- Mukamel R. Gelbard H. Arieli A. Hasson U. Fried I. Malach R. (2005). Coupling between neuronal firing, field potentials and fMRI in human auditory cortex. Science 309, 951– 954 10.1126/science.1110913 16081741 · DOI
- Neggers S. F. Hermans E. J. Ramsey N. F. (2008). Enhanced sensitivity with fast three-dimensional blood-oxygen-level-dependent functional MRI: comparison of SENSE-PRESTO and 2D-EPI at 3 T. NMR Biomed. 21, 663– 676 10.1002/nbm.1235 18189337 · DOI
- Niessing J. Ebisch B. Schmidt K. E. Niessing M. Singer W. Galuske R. A. (2005). Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309, 948– 951 10.1126/science.1110948 16081740 · DOI · Paper page
- Nir Y. Fisch L. Mukamel R. Gelbard-Sagiv H. Arielli A. Fried I. (2007). Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations. Curr. Biol. 17, 1275– 1285 10.1016/j.cub.2007.06.066 17686438 · DOI · Paper page
- Nir Y. Mukamel R. Dinstein I. Privman E. Harel M. Fisch L. (2008). Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat. Neurosci. 11, 1100– 1108 19160509 PMC2642673
- Ojemann G. Ojemann J. Lettich E. Berger M. (1989). Cortical language localization in left, dominant hemisphere: an electrical stimulation mapping investigation in 117 patients. J. Neurosurg. 71, 316– 326 10.3171/jns.1989.71.3.0316 2769383 · DOI
- Ojemann G. A. (1989). Some brain mechanisms for reading, in Brain and Reading, eds Von Euler C. Lundberg I. Lennerstrand G. (Hampshire: MacMillan Press Ltd), 47– 59
- Ojemann G. A. Corina D. P. Corrigna N. Schoenfield-McNeill J. Poliakov A. Zamora L. (2010). Neuronal correlates of functional magnetic resonance imaging in human temporal cortex. Brain 133, 46– 59 10.1093/brain/awp227 19773355 PMC2801320 · DOI
- Ojemann G. A. Dodrill C. B. (1985). Verbal memory deficits after left temporal lobectomy for epilepsy: mechanism and intraoperative prediction. J. Neurosurg. 62, 101– 107 10.3171/jns.1985.62.1.0101 3964840 · DOI
- Ojemann G. A. Schoenfield-McNeill J. Corina D. (2002). Anatomic subdivisions in human temporal cortical neuronal activity related to recent verbal memory. Nat. Neurosci. 5, 64– 71 10.1038/nn785 11753418 · DOI
- Privman E. Nir Y. Kramer U. Kipervasser S. Andelman F. Neufeld M. Y. (2007). Enhanced category tuning revealed by intracranial electroencephalograms in higher-order human visual areas. J. Neurosci. 27, 6234– 6242 10.1523/JNEUROSCI.4627-06.2007 17553996 PMC6672161 · DOI
- Puce A. Allison T. Spencer S. S. Spencer D. D. McCarthy G. (1997). Comparison of cortical activation evoked by faces measured by intracranial field potentials and functional fMRI; two case studies. Hum. Brain Mapp. 5, 298– 305 10.1002/(SICI)1097-0193(1997)5:4<298::AID-HBM16>3.0.CO;2-A 20408232 · DOI
- Puce A. Constable R. T. Luby M. L. McCarthy G. Nobre A. C. Spencer D. D. (1995). Functional magnetic resonance imaging of sensory and motor cortex: comparison with electrophysiological localization. J. Neurosurg. 83, 262– 270 10.3171/jns.1995.83.2.0262 7616272 · DOI
- Raemaekers M. Vink M. Zandbelt B. van Wezel R. J. A. Kahn R. S. Ramsey N. F. (2007). Test-retest reliability of fMRI activation during prosaccades and antisaccades. Neuroimage 36, 532– 542 10.1016/j.neuroimage.2007.03.061 17499525 · DOI
- Ritter P. Freyer F. Curio G. Villringer A. (2008). High-frequency (600 Hz) population spikes in human EEG delineate thalamic and cortical fMRI activation sites. Neuroimage 42, 483– 490 10.1016/j.neuroimage.2008.05.026 18586526 · DOI
- Roberts K. C. Tran T. T. Song A. W. Woldorff M. G. (2007). Component structure of event-related fMRI responses in the different neurovascular compartments. Magn. Reson. Imaging 25, 328– 334 10.1016/j.mri.2006.08.010 17371721 · DOI
- Slutzky M. W. Jordan L. R. Krieg T. Chen M. Mogul D. J. Miller L. E. (2010). Optimal spacing of surface electrode arrays for brain-machine interface applications. J. Neural Eng. 7, 26004 10.1088/1741-2560/7/2/026004 20197598 PMC2844916 · DOI
- Voyvodic J. (2012). Reproducibility of single-subject fMRI language mapping with AMPLE normalization. J. Magn. Reson. Imaging 36, 569– 580 10.1002/jmri.23686 22581466 PMC3423500 · DOI
- Zanos S. Zanos T. P. Marmarelis V. Z. Ojemann G. A. Fetz E. E. (2012). Relationships between spike-free local field potentials and spike timing in human temporal cortex. J. Neurophysiol. 107, 1808– 1821 10.1152/jn.00663.2011 22157112 PMC3331669 · DOI
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- Authors
- Ojemann, G. A., Ojemann, J., & Ramsey, N. F.
- Affiliations
- University of Washington (US)
- Seattle Children's Hospital (US)
- University Medical Center Utrecht (NL)
- Corresponding author
- Ojemann, George A.
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 7
- Pages
- 34-34
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC3576621
- OpenAlex
- W2160722627
- Cited by
- 92
- References
- 3
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 5 · 2020: 7 · 2019: 12 · 2018: 7 · 2017: 9 · 2016: 8 · 2015: 10 · 2014: 10 · 2013: 4
- FWCI
- 7.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health
- Stichting voor de Technische Wetenschappen (UGT7685)
- National Institute of Neurological Disorders and Stroke (R01NS065186)
- FP7 Ideas: European Research Council (320708)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- blood oxygenation level-dependent signal, local field potentials, electrocorticography, single-unit recording, human cortex, drug-resistant epilepsy, functional localization, association cortex, default mode network
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