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 3,701–3,800 of 6,331
Dynamic nonlinearities in BOLD contrast: neuronal or hemodynamic?
Bandettini, P. A., Birn, R. M., Kelley, D., & Saad, Z. S. (2002) · International Congress Series · doi:10.1016/s0531-5131(02)00174-7 · Cited by 13
On the Neuronal Activity in the Human Brain during Visual Recognition, Imagery and Binocular Rivalry
Kreiman, G. (2002) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/e0xz-qp78 · Cited by 13
WORKING MEMORY: DEVELOPMENT, DISORDERS AND TRAINING
Westerberg, H. (2004) · Figshare · Cited by 13
Laurent, M.-A., Jacques, C., Yan, X., Jurczynski, P., Colnat‐Coulbois, S., Maillard, L. G., Cam, S. L., Ranta, R., Cottereau, B. R., Koessler, L., et al. (2025) · eLife · doi:10.7554/elife.104779 · Cited by 13
Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI
Spencer, A. P. C., Nguyen-Duc, J., de Riedmatten, I., Szczepankiewicz, F., & Jelescu, I. O. (2025) · Nature Communications · doi:10.1038/s41467-025-60357-5 · Cited by 12
Ma, J., Liu, F., Wang, Y., Ma, L., Niu, Y., Wang, J., Ye, Z., & Zhang, J. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103245 · Cited by 12
Feedforward attentional selection in sensory cortex
Westerberg, J. A., Schall, J. D., Woodman, G. F., & Maier, A. (2023) · Nature Communications · doi:10.1038/s41467-023-41745-1 · Cited by 12
Kinsey, S., Kazimierczak, K., Andrés-Camazón, P., Chen, J., Adalı, T., Kochunov, P., Adhikari, B. M., Ford, J. M., van Erp, T. G. M., Dhamala, M., et al. (2024) · Nature Mental Health · doi:10.1038/s44220-024-00341-y · Cited by 12
Physiological noise: a comprehensive review on informative randomness in neural systems
Scarciglia, A., Bonanno, C., & Valenza, G. (2025) · Physics of Life Reviews · doi:10.1016/j.plrev.2025.04.001 · Cited by 12
Reservoir computing reconstructs blood-oxygen-level-dependent signals: whole-brain modeling study
Badarin, A., Klinshov, V. V., Smelov, P. S., & Andreev, A. V. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-025-01702-z · Cited by 12
The generalizability of cortical area parcellations across early childhood
Tu, J. C., Myers, M. J., Li, W., Li, J. J., Wang, X., Dierker, D. L., Day, T. K. M., Snyder, A. Z., Latham, A. R., Kenley, J. K., et al. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf116 · Cited by 12
Vo, T. T., Jung, W. B., Jin, T., Im, G. H., Lee, S., & Kim, S. (2025) · Nature Communications · doi:10.1038/s41467-025-61771-5 · Cited by 12
Sleep alters neurovascular and hydrodynamic coupling in the human brain
Väyrynen, T., Tuunanen, J., Helakari, H., Elabasy, A., Korhonen, V. O., Huotari, N., Piispala, J., Kallio, M., Nedergaard, M., & Kiviniemi, V. J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2510731123 · Cited by 12
Mulert, C., & Hegerl, U. (2008) · On thinking · doi:10.1007/978-3-540-68044-4_7 · Cited by 12
Godde, B., Diamond, M. E., & Braun, C. (2010) · Neuroscience Letters · doi:10.1016/j.neulet.2010.06.027 · Cited by 12
Ganesan, M., Kumar, A., Krishnan, S. K., Lalitha, E., Manjula, B., & Amudhavel, J. (2015) · International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET) · doi:10.1145/2743065.2743118 · Cited by 12
Advanced Magnetic Resonance Imaging in Pediatric Multiple Sclerosis
Verhey, L. H., & Sled, J. G. (2013) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2012.12.011 · Cited by 12
Du, J., Vegh, V., & Reutens, D. C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.015 · Cited by 12
Evolution of brain activation after stroke in a constant-effort versus constant-output motor task
Bönstrup, M., Schulz, R., Cheng, B., Feldheim, J. F., Zimerman, M., Thomalla, G., Hummel, F. C., & Gerloff, C. P. (2015) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-150527 · Cited by 12
Gao, Y., Chen, W., & Zhang, X. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2812151 · Cited by 12
A Causal Role for the Pedunculopontine Nucleus in Human Instrumental Learning
Skvortsova, V., Palminteri, S., Buot, A., Karachi, C., Welter, M., Grabli, D., & Pessiglione, M. (2020) · Current Biology · doi:10.1016/j.cub.2020.11.042 · Cited by 13
Buss, A. T., Magnotta, V. A., Penny, W. D., Schöner, G., Huppert, T. J., & Spencer, J. P. (2021) · Psychological Review · doi:10.1037/rev0000264 · Cited by 12
Contribution of astrocytes to neurovascular coupling in the spinal cord of the rat
Paquette, T., Piché, M., & Leblond, H. (2021) · The Journal of Physiological Sciences · doi:10.1186/s12576-021-00800-6 · Cited by 13
Imaging human engrams using 7 Tesla magnetic resonance imaging
Willems, T., & Henke, K. (2021) · Hippocampus · doi:10.1002/hipo.23391 · Cited by 13
Neuroimaging of psychiatric disorders
Iorio‐Morin, C., Sarica, C., Elias, G. J. B., Harmsen, I. E., & Hodaie, M. (2022) · Progress in brain research · doi:10.1016/bs.pbr.2021.12.001 · Cited by 13
Yang, L., Xiao, A., Li, Q., Zhong, H.-F., Su, T., Shi, W., Ying, P., Liang, R., Xu, S., Shao, Y., et al. (2022) · Aging · doi:10.18632/aging.203877 · Cited by 12
Becker, L., Keck, A., Rohleder, N., & Müller, N. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.883926 · Cited by 12
Brain-wide neural co-activations in resting human
Ding, L., Shou, G., Cha, Y., Sweeney, J. A., & Yuan, H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119461 · Cited by 13
McDonald, M. A., Tayebi, M., McGeown, J., Kwon, E. E., Holdsworth, S., & Danesh-Meyer, H. (2022) · Brain and Behavior · doi:10.1002/brb3.2714 · Cited by 12
Increased functional connectivity of white-matter in myotonic dystrophy type 1
Li, J., Li, J., Huang, P., Huang, L., Ding, Q., Zhan, L., Li, M., Zhang, J., Zhang, H., Cheng, L. L., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.953742 · Cited by 12
Cognitive dysfunctions in high myopia: An overview of potential neural morpho-functional mechanisms
Li, K., Wang, Q., Wang, L., & Huang, Y. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.1022944 · Cited by 12
Late dominance of the right hemisphere during narrative comprehension
Youssofzadeh, V., Conant, L. L., Stout, J. D., Ustine, C., Humphries, C., Gross, W. L., Shah‐Basak, P. P., Mathis, J., Awe, E., Allen, L., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119749 · Cited by 12
Choi, S., Chen, Y., Zeng, H., Biswal, B. B., & Yu, X. (2023) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x231158434 · Cited by 12
Akhrif, A., Romanos, M., Peters, K., Furtmann, A.-K., Caspers, J., Lesch, K., Meisenzahl‐Lechner, E., & Neufang, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282813 · Cited by 12
Pre-supplementary motor cortex mediates learning transfer from perceptual to motor timing
Sánchez-Moncada, I., Concha, L., & Merchant, H. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.3191-20.2023 · Cited by 12
Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks
Liu, Q., Zhao, Y., Attanti, S., Voss, J. L., Schoenbaum, G. M., & Kahnt, T. (2024) · Nature Communications · doi:10.1038/s41467-024-45880-1 · Cited by 12
Christova, M., Golaszewski, S. M., Ischebeck, A. K., Kunz, A. B., Rafolt, D., Nardone, R., & Gallasch, E. (2012) · Human Brain Mapping · doi:10.1002/hbm.22102 · Cited by 12
Refractive surgery in anisometropic adult patients induce plastic changes in primary visual cortex
Vuori, E., Vanni, S., Henriksson, L., Tervo, T., & Holopainen, J. M. (2011) · Acta Ophthalmologica · doi:10.1111/j.1755-3768.2011.02152.x · Cited by 12
Cerebral processing of spontaneous reversals of the rotating Necker cube
Schoth, F., Waberski, T. D., Krings, T., Gobbelé, R., & Büchner, H. (2007) · Neuroreport · doi:10.1097/wnr.0b013e3281668007 · Cited by 12
Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds
Ruijssevelt, L. V., Groof, G. D., van der Kant, A., Poirier, C., Audekerke, J. V., Verhoye, M., & der Linden, A. V. (2013) · Journal of Visualized Experiments · doi:10.3791/4369 · Cited by 12
Pillay, S., Liu, X., Baracskay, P., & Hudetz, A. G. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0254 · Cited by 12
Yin, H., Liu, Y., Li, M., & Hu, D. (2010) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2010.12.004 · Cited by 12
Studying the human somatosensory hand area: A new way to compare fMRI and MEG
Stoeckel, M. C., Pollok, B., Schnitzler, A., & Seitz, R. J. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.05.016 · Cited by 12
Sarlo, M., de Zambotti, M., Gallicchio, G., Devigili, A., & Stegagno, L. (2012) · Journal of Behavioral Medicine · doi:10.1007/s10865-012-9410-8 · Cited by 12
Wang, Z., Logothetis, N. K., & Liang, H. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/5/4/008 · Cited by 12
Self-citationKoenig‐Robert, R., VanRullen, R., & Tsuchiya, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144858 · Cited by 12
Burhan, A. M., Anazodo, U., Chung, J. K., Arena, A., Graff‐Guerrero, A., & Mitchell, D. G. V. (2016) · Behavioural Neurology · doi:10.1155/2016/1637392 · Cited by 13
Tools in NeuroIS Research: An Overview
Riedl, R., & Léger, P. (2015) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-662-45091-8_3 · Cited by 12
Riemann, S., Helbing, C., & Angenstein, F. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x16634715 · Cited by 12
Bayram, A., Karahan, E., Bılgıç, B., Ademoğlu, A., & Demıralp, T. (2016) · Vision Research · doi:10.1016/j.visres.2016.09.001 · Cited by 12
Li, R., Lai, Y., Zhang, Y., Yao, L., & Wu, X. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00002 · Cited by 12
Intranasal oxytocin decreases cross-frequency coupling of neural oscillations at rest
Rutherford, H. J. V., Guo, X. M., Wu, J., Graber, K. M., Hayes, N. J., Pelphrey, K. A., & Mayes, L. C. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2017.09.017 · Cited by 13
Cerebral blood flow modulations during preparatory attention and proactive inhibition
Duschek, S., Hoffmann, A., Montoro, C. I., del Paso, G. A. R., Schuepbach, D., & Ettinger, U. (2018) · Biological Psychology · doi:10.1016/j.biopsycho.2018.07.003 · Cited by 12
Cheng, X., Berman, A. J. L., Polimeni, J. R., Buxton, R. B., Gagnon, L., Devor, A., Sakadžić, S., & Boas, D. A. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27660 · Cited by 12
A short, robust brain activation control task optimised for pharmacological fMRI studies
Harvey, J.-L., Demetriou, L., McGonigle, J., & Wall, M. B. (2018) · PeerJ · doi:10.7717/peerj.5540 · Cited by 13
Vizioli, L., Bratch, A., Lao, J., Uǧurbil, K., Muckli, L. F., & Yacoub, E. S. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.06.029 · Cited by 13
Baek, H.-S., Kim, G.-W., Sundaram, T., Park, K., & Jeong, G. (2019) · Sexual Medicine · doi:10.1016/j.esxm.2019.06.013 · Cited by 12
Sundar, L. K. S., Baajour, S., Beyer, T., Lanzenberger, R. R., Traub‐Weidinger, T., Rausch, I., Pataraia, E., Hahn, A., Rischka, L., Hienert, M., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00252 · Cited by 12
Diagnosis of schizophrenia from R-fMRI data using Ripplet transform and OLPP
Sartipi, S., Kalbkhani, H., & Shayesteh, M. G. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09122-y · Cited by 12
Peng, Z., Liu, Y., Li, B., Ge, Q., Liang, R., Li, Q., Shi, W., Yu, Y., & Shao, Y. (2021) · Brain and Behavior · doi:10.1002/brb3.2018 · Cited by 12
Tae, W., Lee, S., Choi, S.-Y., & Pyun, S. (2021) · Scientific Reports · doi:10.1038/s41598-020-79782-1 · Cited by 12
Emerging Utility of Applied Magnetic Resonance Imaging in the Management of Traumatic Brain Injury
Nadel, J., McNally, J. S., DiGiorgio, A. M., & Grandhi, R. (2021) · Medical Sciences · doi:10.3390/medsci9010010 · Cited by 12
Graben, P. B. (2007) · Springer eBooks · doi:10.1007/978-3-540-73159-7_1 · Cited by 12
Evoked electrical and cerebral vascular responses during sleep and following sleep deprivation
Schei, J. L., & Rector, D. M. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53839-0.00015-6 · Cited by 13
Neurophysiology: The Brain at Work
Friston, K. J. (2008) · Current Biology · doi:10.1016/j.cub.2008.03.042 · Cited by 12
Kim, J. H., Song, I., Lee, S. M., Choi, H., Byeon, H., Kim, I. Y., & Lee, S. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2263226 · Cited by 12
Anomalous functional organization of barrel cortex in GAP-43 deficient mice
Dubroff, J. G., Stevens, R. T., Hitt, J. M., Hodge, C. J., & McCasland, J. S. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.054 · Cited by 12
Sander, T. H., Leistner, S., Wabnitz, H., Mackert, B., Macdonald, R., & Trahms, L. (2010) · Computational Intelligence and Neuroscience · doi:10.1155/2010/785279 · Cited by 12
Neuroimaging in refractory epilepsy. Current practice and evolving trends
Ramli, N. M., Rahmat, K., Lim, K. S., & Tan, C. T. (2015) · European Journal of Radiology · doi:10.1016/j.ejrad.2015.03.024 · Cited by 12
Developmental changes in the vascular network of the rat visual areas 17, 18 and 18a
Michaloudi, H. C., Batzios, C., Grivas, I. N., Chiotelli, M., & Papadopoulos, G. C. (2006) · Brain Research · doi:10.1016/j.brainres.2006.05.069 · Cited by 12
The Use of Motor Mapping to Aid Resection of Eloquent Gliomas
Choi, B. D., Mehta, A. I., Batich, K. A., Friedman, A. H., & Sampson, J. H. (2012) · Neurosurgery Clinics of North America · doi:10.1016/j.nec.2012.01.013 · Cited by 12
Casciaro, S., Bianco, R., & Distante, A. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2008.02.006 · Cited by 12
Linearity of the fMRI response in category‐selective regions of human visual cortex
Horner, A. J., & Andrews, T. J. (2008) · Human Brain Mapping · doi:10.1002/hbm.20694 · Cited by 12
The hemodynamics of oddball processing during single-tone and two-tone target detection tasks
Stevens, M. C. G., Laurens, K. R., Liddle, P. F., & Kiehl, K. A. (2005) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2005.07.012 · Cited by 13
Physiology and Physics of the fMRI Signal
Uludaǧ, K., & Uǧurbil, K. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_8 · Cited by 12
Neural Bases for Social Attention in Healthy Humans
Puce, A., Latinus, M., Rossi, A., daSilva, E. B., Parada, F. J., Love, S., Ashourvan, A., & Jayaraman, S. (2015) · Springer eBooks · doi:10.1007/978-3-319-21368-2_4 · Cited by 12
Aksenov, D. P., Li, L., Miller, M. J., & Wyrwicz, A. M. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13384 · Cited by 12
Discrete Modules and Mesoscale Functional Circuits for Thermal Nociception within Primate S1 Cortex
Yang, P., Wu, R., Wu, T., Shi, Z., & Chen, L. M. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2795-17.2017 · Cited by 12
Cerebral blood flow modulations during proactive control in major depressive disorder
Hoffmann, A., Montoro, C. I., del Paso, G. A. R., & Duschek, S. (2018) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2018.07.003 · Cited by 12
Human V6 Integrates Visual and Extra-Retinal Cues during Head-Induced Gaze Shifts
Schindler, A., & Bartels, A. (2018) · iScience · doi:10.1016/j.isci.2018.09.004 · Cited by 12
Disentangling Multispectral Functional Connectivity With Wavelets
Billings, J., Thompson, G. J., Pan, W., Magnuson, M. E., Medda, A., & Keilholz, S. D. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00812 · Cited by 13
Basic Principles of Functional MRI
Tang, C. Y. (2018) · Oxford University Press eBooks · doi:10.1093/med/9780190297763.003.0002 · Cited by 12
Introduction to Human Neuroimaging
de Beeck, H. O., & Nakatani, C. (2019) · Cambridge University Press eBooks · doi:10.1017/9781009349925 · Cited by 16
Wu, C. W., Tsai, P.-J., Chen, S. C.-J., Li, C., Hsu, A., Wu, H.-Y., Ko, Y.-T., Hung, P.-C., Chang, C., Lin, C., et al. (2019) · Sleep and Biological Rhythms · doi:10.1007/s41105-019-00232-1 · Cited by 12
Horwitz, B., & Glabus, M. F. (2005) · International review of neurobiology · doi:10.1016/s0074-7742(05)66009-6 · Cited by 12
Complementary aspects of diffusion imaging and fMRI; I: structure and function
Mulkern, R. V., Davis, P. E., Haker, S. J., Estépar, R. S. J., Panych, L. P., Maier, S. E., & Rivkin, M. J. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2006.01.007 · Cited by 12
Dubroff, J. G., Stevens, R. T., Hitt, J. M., Maier, D. L., McCasland, J. S., & Hodge, C. J. (2005) · Somatosensory & Motor Research · doi:10.1080/08990220500084990 · Cited by 12
Imaging In Vivo Brain-Hormone Interaction in the Control of Eating and Obesity
James, G. A., Guo, W., & Liu, Y. (2001) · Diabetes Technology & Therapeutics · doi:10.1089/15209150152811243 · Cited by 12
Warren, D. E., Sutterer, M. J., Bruss, J., Abel, T. J., Jones, A., Kawasaki, H., Voss, M. W., Cassell, M. D., Howard, M. A., & Tranel, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/127571 · Cited by 13
The Interplay Between Information Flux and Temporal Dynamics in Infraslow FrequenciesPreprint
Golesorkhi, M., Tumati, S., Gómez‐Pilar, J., Stamatakis, E. A., & Northoff, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.11.106476 · Cited by 12
Streams of conscious visual experience
Martín-Signes, M., Chica, A. B., Bartolomeo, P., & de Schotten, M. T. (2024) · Communications Biology · doi:10.1038/s42003-024-06593-9 · Cited by 12
A Novel Recognition and Classification Approach for Motor Imagery Based on Spatio-Temporal Features
Lv, R., Chang, W., Yan, G., Nie, W., Zheng, L., Guo, B., & Sadiq, M. T. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3464550 · Cited by 11
BOLD amplitude correlates of preclinical Alzheimer’s disease
Hrybouski, S., Das, S. R., Xie, L., Brown, C., Flamporis, M., Lane, J., Nasrallah, I. M., Detre, J. A., Yushkevich, P. A., & Wolk, D. A. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.03.007 · Cited by 13
Kiviniemi, V. J., Helakari, H., Raitamaa, L., Huotari, N., Rajna, Z., Järvelä, M., Elabasy, A., Tuunanen, J., Poltojainen, V., Väyrynen, T., et al. (2025) · NMR in Biomedicine · doi:10.1002/nbm.70092 · Cited by 11
Mapping the white-matter functional connectome: a personal perspective
Li, J., Chen, H., & Liao, W. (2025) · Psychoradiology · doi:10.1093/psyrad/kkaf028 · Cited by 12
Stock, A., Vanessa Ness, A., & Beste, C. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.036 · Cited by 11
Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI
Lessa, P. S., Sato, J. R., Cardoso, E. F., Neto, C. G., Valadares, A. P., & Amaro, E. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.09.005 · Cited by 11
An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing
Soman, K., Muralidharan, V., & Chakravarthy, V. S. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00052 · Cited by 11
Hosford, P. S., Wells, J. A., Christie, I. N., Lythgoe, M. F., Millar, J., & Gourine, A. V. (2019) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2020.100034 · Cited by 11
Rudas, J., Martínez, D., Castellanos, G., Athena Demertzi, A., Martial, C., Carrière, M., Aubinet, C., Soddu, A., Laureys, S., & Gómez, F. A. (2020) · Brain Connectivity · doi:10.1089/brain.2019.0716 · Cited by 11
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
