The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Neurophysiological investigation of the basis of the fMRI signal
Abstract
Functional magnetic resonance imaging (fMRI) is widely used to study the operational organization of the human brain, but the exact relationship between the measured fMRI signal and the underlying neural activity is unclear. Here we present simultaneous intracortical recordings of neural signals and fMRI responses. We compared local field potentials (LFPs), single- and multi-unit spiking activity with highly spatio-temporally resolved blood-oxygen-level-dependent (BOLD) fMRI responses from the visual cortex of monkeys. The largest magnitude changes were observed in LFPs, which at recording sites characterized by transient responses were the only signal that significantly correlated with the haemodynamic response. Linear systems analysis on a trial-by-trial basis showed that the impulse response of the neurovascular system is both animal- and site-specific, and that LFPs yield a better estimate of BOLD responses than the multi-unit responses. These findings suggest that the BOLD contrast mechanism reflects the input and intracortical processing of a given area rather than its spiking output.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Importantly, local field potentials and tissue oxygen response, which directly contribute to the generation of the BOLD signals (Logothetis et al., 2001), were preserved at both frequencies.”
From Perception is associated with the brain’s metabolic response to sensory stimulation (2022) · cites this paper as Logothetis et al., 2001
In the citing paper
“Although the electrophysiological activity in V1 could not be directly assessed in our experiment, based on the literature (Logothetis et al., 2001) we can obtain a rough surrogate of V1 output by evaluating fMRI signals in the secondary visual areas, which receive input directly from V1.”
From Perception is associated with the brain’s metabolic response to sensory stimulation (2022) · cites this paper as Logothetis et al., 2001
In the citing paper
“and EEG oscillations are associated with hemodynamic signals (29-32).”
From Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep (2019) · cites this paper as [29]
In the citing paper
“When recorded simultaneously during sensory stimulation, the BOLD response is more correlated with LFP than MUA (129, 130).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [129]
In the citing paper
“Increasing evidence suggests that the primary source of fMRI is synaptic activity, rather than spiking activity, in the gray matter (129, 130).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [129]
In the citing paper
“The BOLD response is thought to be generated by local excitatory synaptic activity (Logothetis et al., 2001), leading to precisely localised changes in blood flow in response to the release of glutamate (Attwell and Iadecola, 2002).”
From The social brain in adolescence: evidence from functional magnetic resonance imaging and behavioural studies (2011) · cites this paper as Logothetis et al., 2001
In the citing paper
“Under the view that the fMRI BOLD signal reflects in part the input into an area and intrinsic computations within it (Logothetis et al., 2001)”
From States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning (2010) · cites this paper as Logothetis et al., 2001
In the citing paper
“which was also observed under anesthesia in previous EEG-fMRI and intrinsic optical signal (IOS)-fMRI studies35,36.”
From Local hippocampal fast gamma rhythms precede brain-wide hyperemic patterns during spontaneous rodent REM sleep (2018) · cites this paper as [35]
In the citing paper
“Gradient echo T2*-weighted echo-planar images (EPIs) sensitive to the BOLD contrast were acquired as an index of local increases in synaptic activity (48)”
From Threatening a rubber hand that you feel is yours elicits a cortical anxiety response. (2007) · cites this paper as [48]
In the citing paper
“fMRI is one the most common and reliable methods of measuring brain function at decent spatial and temporal resolutions, but given that it is an indirect measure of brain activity, reflecting a paradigm related change in metabolic consumption (Logothetis, Pauls et al. 2001), interpretation of gene effects is limited.”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Logothetis, Pauls et al. 2001
Citations
Cites
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
Pellerin, L., & Magistretti, P. J. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.22.10625 · Cited by 2,927
Neuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581
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
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
Gray, C. M., & Singer, W. (1989) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.86.5.1698 · Cited by 2,463
Coherent oscillations: A mechanism of feature linking in the visual cortex?
Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M., & Reitboeck, H. J. (1988) · Biological Cybernetics · doi:10.1007/bf00202899 · Cited by 2,329
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
Multi-planar image formation using NMR spin echoes
Mansfield, P. (1977) · Journal of Physics C Solid State Physics · doi:10.1088/0022-3719/10/3/004 · Cited by 1,809
Synchronization of cortical activity and its putative role in information processing and learning
Singer, W. (1993) · Annual Review of Physiology · doi:10.1146/annurev.ph.55.030193.002025 · Cited by 1,685
Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model
Buxton, R. B., Wong, E. C., & Frank, L. R. (1998) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910390602 · Cited by 1,665
Research works printed but not linked: 2
- Juergens, E., Eckhorn, R., Frien, A. & Woelbern, T. Brain and Evolution 418 (Thieme, Berlin, 1996).
- Sokoloff, L. in Basic Neurochemistry (eds Siegel, G., Agranoff, B., Albers, R. W. & Molinoff, P.) 565–590 (Raven, New York, 1989).
Cited by
Sorted by citations
Items 2,101–2,200 of 6,331
Altered baseline brain activities before food intake in obese men: A resting state fMRI study
Zhang, B., Tian, D., Yu, C., Zhang, J., Xiao, T., von Deneen, K. M., Zang, Y., Walter, M., & Liu, Y. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.10.020 · Cited by 48
Binding by asynchrony: the neuronal phase code
Nádasdy, Z. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00051 · Cited by 48
Ritter, P., Freyer, F., Curio, G., & Villringer, A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.026 · Cited by 48
Aslan, S., Huang, H., Uh, J., Mishra, V. R., Xiao, G., van Osch, M. J. P., & Lu, H. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.082 · Cited by 48
Hyder, F. (2010) · Frontiers in Neuroenergetics · doi:10.3389/fnene.2010.00018 · Cited by 48
Basing Perceptual Decisions on the Most Informative Sensory Neurons
Scolari, M., & Serences, J. T. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00273.2010 · Cited by 52
Longitudinal imaging of the ageing mouse
Dall’Ara, E., Boudiffa, M., Taylor, C., Schug, D., Fiegle, E., Kennerley, A. J., Damianou, C., Tozer, G. M., Kiessling, F. M., & Müller, R. (2016) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2016.08.001 · Cited by 49
Shared orthographic neuronal representations for spelling and reading
Purcell, J. J., Jiang, X., & Eden, G. F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.054 · Cited by 48
Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width
Liu, T., Cable, D. M., & Gardner, J. L. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2453-17.2017 · Cited by 51
EEG captures affective touch: CT-optimal touch and neural oscillations
Mohr, M. V., Crowley, M. J., Walthall, J., Mayes, L. C., Pelphrey, K. A., & Rutherford, H. J. V. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-017-0560-6 · Cited by 49
Cognitive Neuroscience of Memory
Slotnick, S. D. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316026687 · Cited by 49
Stroman, P. W., Ioachim, G., Powers, J. M., Staud, R. M., & Pukall, C. F. (2018) · Pain · doi:10.1097/j.pain.0000000000001302 · Cited by 49
How reliable is cerebral blood flow to map changes in neuronal activity?
Lecrux, C., Bourourou, M., & Hamel, É. (2019) · Autonomic Neuroscience · doi:10.1016/j.autneu.2019.01.005 · Cited by 54
Xifra‐Porxas, A., Kassinopoulos, M., & Mitsis, G. D. (2021) · eLife · doi:10.7554/elife.62324 · Cited by 53
Blake, P. Y., Johnson, B., & VanMeter, J. W. (2003) · Journal of Neuro-Ophthalmology · doi:10.1097/00041327-200303000-00009 · Cited by 50
Effects of sound bandwidth on fMRI activation in human auditory brainstem nuclei
Hawley, M. L., Melcher, J. R., & Fullerton, B. C. (2005) · Hearing Research · doi:10.1016/j.heares.2005.01.005 · Cited by 48
Discriminating the Cortical Representation Sites of Tongue and Lip Movement by Functional MRI
Heßelmann, V., Sorger, B., Lasek, K., Guntinas‐Lichius, O., Krug, B., Sturm, V. J. F., Goebel, R., & Lackner, K. J. (2004) · Brain Topography · doi:10.1023/b:brat.0000019184.63249.e8 · Cited by 48
Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control
Mars, R. B., Shea, N. J., Kolling, N., & Rushworth, M. F. S. (2010) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470211003668272 · Cited by 51
The Relationship between Task Performance and Functional Magnetic Resonance Imaging Response
Buračas, G. T., Fine, I., & Boynton, G. M. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.4476-04.2005 · Cited by 48
Babiloni, C. (2003) · Behavioural Brain Research · doi:10.1016/j.bbr.2003.10.004 · Cited by 48
Imaging localized neuronal activity at fast time scales through biomechanics
Patz, S., Fovargue, D., Schregel, K., Nazari, N., Palotai, M., Barbone, P. E., Fabry, B., Hammers, A., Holm, S., Kozerke, S., et al. (2019) · Science Advances · doi:10.1126/sciadv.aav3816 · Cited by 48
Development of the action observation network during early adolescence: a longitudinal study
Shaw, D. J., Grosbras, M.-H., Leonard, G., Pike, G. B., & Paus, T. (2012) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsq105 · Cited by 47
Oxytocin modulates unconditioned fear response in lactating dams: An fMRI study
Febo, M., Shields, J., Ferris, C. F., & King, J. A. (2009) · Brain Research · doi:10.1016/j.brainres.2009.09.043 · Cited by 47
Tesler, F., Linne, M., & Alain Destexhe, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-32618-0 · Cited by 47
Flanagan, K., & Saikia, M. J. (2023) · Sensors · doi:10.3390/s23208482 · Cited by 47
The Formation of Hierarchical Decisions in the Visual Cortex
Lorteije, J. A. M., Zylberberg, A., Ouellette, B. G., Zeeuw, C. I. D., Sigman, M., & Roelfsema, P. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.08.015 · Cited by 47
Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective
Chen, M., Han, J., Hu, X., Jiang, X., Guo, L., & Liu, T. (2013) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9238-z · Cited by 48
Zhang, D., Song, H., Xu, R., Zhou, W., Ling, Z., & Hong, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.069 · Cited by 47
Modulation of Fronto-Cortical Activity by Modafinil: A Functional Imaging and Fos Study in the Rat
Gozzi, A., Colavito, V., Etet, P. F. S., Montanari, D., Fiorini, S., Tambalo, S., Bifone, A., Zucconi, G. G., & Bentivoglio, M. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.260 · Cited by 49
Chan, K. C., Xing, K. K., Cheung, M. M., Zhou, I. Y., & Wu, E. X. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.069 · Cited by 47
Vasomotion and Neurovascular Coupling in the Visual Thalamus In Vivo
Rivadulla, C., de Labra, C., Grieve, K. L., & Cudeiro, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028746 · Cited by 47
Structure-function-behavior relationship in estrogen-induced synaptic plasticity.
Vierk, R., Bayer, J., Freitag, S., Muhia, M., Kutsche, K., Wolbers, T., Kneussel, M., Sommer, T., & Rune, G. (2015) · Hormones and Behavior · doi:10.1016/j.yhbeh.2015.05.008 · Cited by 45
fMRI connectivity, meaning and empiricism
Olivier David, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.073 · Cited by 47
Multi-kernel based nonlinear models for connectivity identification of brain networks
Karanikolas, G. V., Giannakis, G. B., Slavakis, K., & Leahy, R. M. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472892 · Cited by 47
Smith, S. K., Lee, C. A., Dausch, M. E., Horman, B. M., Patisaul, H. B., McCarty, G. S., & Sombers, L. A. (2016) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.6b00363 · Cited by 47
Kimmerly, D. S. (2017) · Autonomic Neuroscience · doi:10.1016/j.autneu.2017.05.008 · Cited by 49
Ghosts in machine learning for cognitive neuroscience: Moving from data to theory
Carlson, T. A., Goddard, E., Kaplan, D. M., Klein, C., & Ritchie, J. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.019 · Cited by 51
Binocular response modulation in the lateral geniculate nucleus
Dougherty, K., Schmid, M. C., & Maier, A. (2018) · The Journal of Comparative Neurology · doi:10.1002/cne.24417 · Cited by 49
Chen, Z., Wang, Q., Liang, M., Zhao, R., Zhu, J., Xiong, W., Su, Z., Yu, C., & Xue, Y. (2018) · Neuroradiology · doi:10.1007/s00234-018-2061-x · Cited by 47
Functional brain imaging of symptoms and cognition in schizophrenia
Kircher, T. T. J., & Thienel, R. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)50022-0 · Cited by 47
Scaling laws and persistence in human brain activity
Stefan Thurner, A., Windischberger, C., Moser, E., Walla, P., & Barth, M. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(03)00279-6 · Cited by 49
Bohning, D. E., Shastri, A., Lomarev, M. P., Lorberbaum, J. P., Nahas, Z. H., & George, M. S. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10271 · Cited by 47
Cortical theta wanes for language
Hermes, D., Miller, K. J., Vansteensel, M. J., Edwards, E., Ferrier, C. H., Bleichner, M. G., van Rijen, P. C., Aarnoutse, E. J., & Ramsey, N. F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.029 · Cited by 51
Functional imaging and neuropsychology findings: how can they be linked?
Shallice, T. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.023 · Cited by 47
Contrasting roles of neural firing rate and local field potentials in human memory
Ekstrom, A. D., Viskontas, I. V., Kahana, M. J., Jacobs, J., Upchurch, K., Bookheimer, S. Y., & Fried, I. (2007) · Hippocampus · doi:10.1002/hipo.20300 · Cited by 49
Integration of EEG source imaging and fMRI during continuous viewing of natural movies
Whittingstall, K., Bartels, A., Singh, V., Kwon, S., & Logothetis, N. K. (2010) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2010.03.042 · Cited by 47
Self-citationMatthews, W. J., Terhune, D. B., van Rijn, H., Eagleman, D. M., Sommer, M. A., & Meck, W. H. (2014) · Timing & Time Perception Reviews · doi:10.1163/24054496-00101005 · Cited by 46
Levels of biological plausibility
Love, B. C. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0632 · Cited by 45
Functional Ultrasound Neuroimaging
Montaldo, G., Urban, A., & Macé, É. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-111020-100706 · Cited by 49
Richer, N., Bradford, J. C., & Ferris, D. P. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105718 · Cited by 46
Targeting the functional properties of cortical neurons using fMR-adaptation
Malach, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.002 · Cited by 50
Shulman, R. G., Hyder, F., & Rothman, D. L. (2014) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2014.145 · Cited by 48
Bentley, W. J., Li, J. M., Snyder, A. Z., Raichle, M. E., & Snyder, L. H. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu260 · Cited by 46
Mendoza-Halliday, D., Schneiderman, M., Kaul, C., & Martínez-Trujillo, J. (2011) · Journal of Vision · doi:10.1167/11.1.11 · Cited by 46
Cortical Correlates of Human Motion Perception Biases
Vintch, B., & Gardner, J. L. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2809-13.2014 · Cited by 47
Meusel, L.-A., Kansal, N., Tchistiakova, E., Yuen, W., MacIntosh, B. J., Greenwood, C. E., & Anderson, N. D. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00148 · Cited by 49
BOLD neurovascular coupling does not change significantly with normal aging
Grinband, J., Steffener, J., Razlighi, Q. R., & Stern, Y. (2017) · Human Brain Mapping · doi:10.1002/hbm.23608 · Cited by 47
Physical activity and cognition: A narrative review of the evidence for older adults
Etnier, J. L., Drollette, E. S., & Slutsky, A. B. (2018) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2018.12.006 · Cited by 55
Chang, C., Dong, D., Jiang, Y., Ming, Q., Zhong, X. B., Sun, X., Xiong, G., Gao, Y., & Yao, S. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00245 · Cited by 46
Kinetic Modeling of Hyperpolarized Carbon-13 Pyruvate Metabolism in the Human Brain
Mammoli, D., Gordon, J. W., Autry, A. W., Larson, P. E. Z., Li, Y., Chen, H., Chung, B. T., Shin, P., Criekinge, M. V., Carvajal, L. E., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2926437 · Cited by 46
Kalyvas, A., Koutsarnakis, C., Komaitis, S., Karavasilis, E., Christidi, F., Skandalakis, G. P., Liouta, E., Papakonstantinou, O., Kelekis, N., Duffau, H., et al. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01987-6 · Cited by 48
Advances in simultaneous PET/MR for imaging neuroreceptor function
Sander, C. Y. M., Hansen, H. D., & Wey, H. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20910038 · Cited by 46
Haselden, W. D., Kedarasetti, R. T., & Drew, P. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008069 · Cited by 47
Liu, C., Guo, F., Qian, C., Zhang, Z., Sun, K., Wang, D. J. J., He, S., & Zhang, P. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101897 · Cited by 48
Functional Magnetic Resonance Imaging
Lin, A., & Way, H.-Y. M. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386456-7.07610-3 · Cited by 47
Customizing MRI‐Compatible Multifunctional Neural Interfaces through Fiber Drawing
Antonini, M., Sahasrabudhe, A., Tabet, A., Schwalm, M., Rosenfeld, D., Garwood, I. C., Park, J., Loke, G. Z. J., Khudiyev, T., Kanık, M., et al. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202104857 · Cited by 48
Neuroplasticity and MRI: A perfect match
Hamaide, J., Groof, G. D., & der Linden, A. V. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.005 · Cited by 49
High-Resolution Neurometabolic Coupling in the Lateral Geniculate Nucleus
Li, B., & Freeman, R. D. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1505-07.2007 · Cited by 46
Lüchinger, R., Michels, L., Martin, E., & Brandeis, D. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.031 · Cited by 49
The Effect of Labeling Parameters on Perfusion-Based fMRI in Nonhuman Primates
Zappe, A. C., Pfeuffer, J., Merkle, H., Logothetis, N. K., & Goense, J. B. M. (2007) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/sj.jcbfm.9600564 · Cited by 46
Self-citationSato, W., Kochiyama, T., Uono, S., & Yoshikawa, S. (2008) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsn016 · Cited by 46
Functional MRI experiments: acquisition, analysis and interpretation of data
Ramsey, N. F. (2002) · European Neuropsychopharmacology · doi:10.1016/s0924-977x(02)00101-3 · Cited by 46
The brain's orienting response: An event‐related functional magnetic resonance imaging investigation
Friedman, D., Goldman, R. I., Stern, Y., & Brown, T. R. (2008) · Human Brain Mapping · doi:10.1002/hbm.20587 · Cited by 46
Functional magnetic resonance imaging
Matthews, P. M. (2008) · CRC Press eBooks · doi:10.1201/b15118-65 · Cited by 46
Spatio-temporal information analysis of event-related BOLD responses
Alpert, G. F., Sun, F. T., Handwerker, D. A., D’Esposito, M., & Knight, R. T. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.10.020 · Cited by 46
Stroman, P. W., Kornelsen, J., Lawrence‐Dewar, J. M., & Malisza, K. L. (2005) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2005.07.009 · Cited by 46
Bartelle, B. B., Barandov, A., & Jasanoff, A. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.4050-15.2016 · Cited by 45
Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics
Chang, C., & Chen, J. E. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100279 · Cited by 45
Gaining insight into the neural basis of resting-state fMRI signal
Ma, Z., Zhang, Q., Tu, W., & Zhang, N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118960 · Cited by 48
Advanced Neuroimaging Role in Traumatic Brain Injury: A Narrative Review
Hu, L., Yang, S., Jin, B., & Wang, C. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.872609 · Cited by 43
BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning
Yang, Y., Ye, C., Su, G., Zhang, Z., Chang, Z., Chen, H., Chan, P., Yu, Y., & Ma, T. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3414476 · Cited by 54
Optimized parallel transmit and receive radiofrequency coil for ultrahigh-field MRI of monkeys
Gilbert, K. M., Gati, J. S., Barker, K. C., Everling, S., & Menon, R. S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.048 · Cited by 45
High-Field fMRI for Human Applications: An Overview of Spatial Resolution and Signal Specificity
Olman, C. A., & Yacoub, E. S. (2011) · The Open Neuroimaging Journal · doi:10.2174/1874440001105010074 · Cited by 49
Aberrant processing of deviant stimuli in schizophrenia revealed by fusion of fMRI and EEG data
Calhoun, V. D., Wu, L., Kiehl, K. A., Eichele, T., & Pearlson, G. D. (2010) · Acta Neuropsychiatrica · doi:10.1111/j.1601-5215.2010.00467.x · Cited by 45
Preictal changes in cerebral haemodynamics: Review of findings and insights from intracerebral EEG
Schwartz, T. H., Hong, S.-B., Bagshaw, A. P., Chauvel, P., & Bénar, C. (2011) · Epilepsy Research · doi:10.1016/j.eplepsyres.2011.07.013 · Cited by 45
Functional Magnetic Resonance Imaging of Mild Traumatic Brain Injury
Jantzen, K. J. (2010) · Journal of Head Trauma Rehabilitation · doi:10.1097/htr.0b013e3181e5477c · Cited by 48
Gavrilescu, M. C., Stuart, G. W., Rossell, S. L., Henshall, K. R., McKay, C. M., Sergejew, A. A., Copolov, D., & Egan, G. F. (2007) · Human Brain Mapping · doi:10.1002/hbm.20446 · Cited by 45
Kato, T. (2004) · International Congress Series · doi:10.1016/j.ics.2004.05.052 · Cited by 45
Mehta, J. P., Verber, M., Wieser, J., Schmit, B. D., & Schindler-Ivens, S. (2012) · Motor Control · doi:10.1123/mcj.16.2.158 · Cited by 45
Stroman, P. W., Khan, H. S., Bosma, R. L., Cotoi, A. I., Leung, R. H., Cadotte, D. W., & Fehlings, M. G. (2016) · Journal of Neurotrauma · doi:10.1089/neu.2015.4257 · Cited by 45
Henderson, L. A., Fatouleh, R. H., Lundblad, L. C., McKenzie, D. K., & Macefield, V. G. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00090 · Cited by 45
Shi, D., Mi, G., Bhattacharya, S., Nayar, S., & Webster, T. J. (2016) · International Journal of Nanomedicine · doi:10.2147/ijn.s108333 · Cited by 45
How to choose the right MR sequence for your research question at 7 T and above?
Marques, J. P., & Norris, D. G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.044 · Cited by 45
Orsini, C. A., Colón-Pérez, L. M., Heshmati, S. C., Setlow, B., & Febo, M. (2018) · eNeuro · doi:10.1523/eneuro.0081-18.2018 · Cited by 47
A neural signature of pattern separation in the monkey hippocampus
Sakon, J. J., & SUZUKI, W. A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900804116 · Cited by 46
Functional dynamics of dopamine synthesis during monetary reward and punishment processing
Hahn, A., Reed, M. B., Pichler, V., Michenthaler, P., Rischka, L., Godbersen, G. M., Wadsak, W., Hacker, M., & Lanzenberger, R. R. (2021) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x211019827 · Cited by 50
Prolonged T values in newborn versus adult brain: Implications for fMRI studies of newborns
Rivkin, M. J., Wolraich, D., Als, H., McAnulty, G. B., Butler, S., Conneman, N., Fischer, C., Vajapeyam, S., Robertson, R. L., & Mulkern, R. V. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20098 · Cited by 45
Jolesz, F. A., Talos, I., Schwartz, R. B., Mamata, H., Kacher, D. F., Hynynen, K., McDannold, N. J., Saiviroonporn, P., & Zao, L. (2002) · Neuroimaging Clinics of North America · doi:10.1016/s1052-5149(02)00036-9 · Cited by 45
Changing plans: neural correlates of executive control in monkey and human frontal cortex
Dias, E. C., McGinnis, T., Smiley, J. F., Foxe, J. J., Schroeder, C. E., & Javitt, D. C. (2006) · Experimental Brain Research · doi:10.1007/s00221-006-0444-4 · Cited by 45
Deactivation of Brain Areas During Self-Regulation of Slow Cortical Potentials in Seizure Patients
Strehl, U., Trevorrow, T., Veit, R., Hinterberger, T., Kotchoubey, B., Erb, M., & Birbaumer, N. (2006) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-006-9006-6 · Cited by 45
Metadata
- Authors
- Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A.
- Affiliations
- Max Planck Institute for Biological Cybernetics (DE)
- Max Planck Society (DE)
- Corresponding author
- Logothetis, Nikos K.
- ORCID
- Logothetis, Nikos K.
- Venue
- Nature
- Published
- 2001-07-12
- Volume
- 412
- Issue
- 6843
- Pages
- 150-157
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2114104729
- Cited by
- 6,551
- References
- 50
- Citations by year
- 2026: 182 · 2025: 226 · 2024: 197 · 2023: 209 · 2022: 245 · 2021: 286 · 2020: 244 · 2019: 301 · 2018: 234 · 2017: 311 · 2016: 258 · 2015: 297 · 2014: 288 · 2013: 298 · 2012: 321
- FWCI
- 168.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Max-Planck-Gesellschaft
- 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
- BOLD signal, local field potentials, intracortical recording, neurovascular coupling, hemodynamic response, visual cortex, linear systems analysis, impulse response, monkeys, neural activity estimation
- MeSH terms
- Action Potentials, Animals, Electrodes, Electrophysiology, Hemodynamics, Macaca mulatta, Magnetic Resonance Imaging, Synaptic Transmission, Neurons, Oxygen, Photic Stimulation, Signal Processing, Computer-Assisted, Visual Cortex, Contrast Sensitivity
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