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
Flagged first, then by citations
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 4,901–5,000 of 6,331
Modelling Working Memory Through Attentional Mechanisms
Taylor, J. G., Fragopanagos, N. F., & Korsten, N. (2006) · Lecture notes in computer science · doi:10.1007/11840817_58 · Cited by 2
Kato, H., & Izumiyam, M. (2010) · Sciyo eBooks · doi:10.5772/9884 · Cited by 2
Chapter 24 Repetitive transcranial magnetic stimulation (rTMS) in monkeys
Ugawa, Y., Okabe, S., Hayashi, T., Ohnishi, T., & Nonaka, Y. (2006) · Supplements to Clinical neurophysiology · doi:10.1016/s1567-424x(09)70028-5 · Cited by 2
Lorenz, J., & Auffermann, W. F. (2010) · Der Schmerz · doi:10.1007/s00482-010-0900-8 · Cited by 2
BOLD Response and EEG Gamma Oscillations
Leicht, G., Herrmann, C. S., & Mulert, C. (2009) · Springer eBooks · doi:10.1007/978-3-540-87919-0_23 · Cited by 3
The pre-ictal state in focal epilepsy
Baumgartner, C., & Aull‐Watschinger, S. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67865-8 · Cited by 2
A future for systems and computational neuroscience in France?
Faugeras, O. D., Frégnac, Y., & Samuelides, M. (2007) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2007.10.012 · Cited by 2
Ji, S.-Y., Ward, K., & Najarian, K. (2009) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-9-s1-s6 · Cited by 2
Brain Plasticity and Clinical Applications of fMRI
Matthews, P. M. (2005) · Rivista di Neuroradiologia · doi:10.1177/197140090501800411 · Cited by 2
Imaging Epilepsy and Epileptic Seizures Using fMRI
Glynn, S. M., & Detre, J. A. (2013) · Springer eBooks · doi:10.1007/978-3-642-34342-1_14 · Cited by 2
Brain Computer Interface using EEG Sensors Based on an fMRI Experiment
Choi, S. H., & Lee, M. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716746 · Cited by 2
Functional MRI of postnatal visual development in normal rat superior colliculi
Chan, K. C., Xing, K., Cheung, M. M., Zhou, I. Y., & Wu, E. X. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5332756 · Cited by 2
Turning Symbolic: The Representation of Motion Direction in Working Memory
Malkinson, T. S., Pertzov, Y., & Zohary, E. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00165 · Cited by 2
Cerebral blood flow physiology, pharmacology, and pathophysiology
Chugh, C., & Kofke, W. A. (2016) · Oxford University Press eBooks · doi:10.1093/med/9780198739555.003.0003 · Cited by 2
fMRI of the Sensorimotor System
Filippi, M., Messina, R., & Rocca, M. A. (2016) · Neuromethods · doi:10.1007/978-1-4939-5611-1_17 · Cited by 2
3.5 Parallel EEG-fMRI ICA Decomposition
Eichele, T., & Calhoun, V. D. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195372731.003.0012 · Cited by 2
Liu, M., Liu, C., Wu, J., Ли, Б., Zhou, Z.-G., Dai, P.-S., Yu, J., & Chang, X.-R. (2017) · Journal of Acupuncture and Tuina Science · doi:10.1007/s11726-017-1006-y · Cited by 2
Evidential power and explanatory gap in forensic imaging
Ramsthaler, F., Kettner, M., Birngruber, C. G., & Verhoff, M. A. (2017) · Rechtsmedizin · doi:10.1007/s00194-017-0217-3 · Cited by 2
Detection and Analysis of Human Brain Disorders
Tiwari, D., & Jha, H. C. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0923-6_61 · Cited by 2
Hamaide, J., Bigler, A., & der Linden, A. V. (2018) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-812028-6.00025-2 · Cited by 2
Bolton, T. A. W., & Ville, D. V. D. (2019) · International Conference on Image and Graphics Processing (ICIGP) · doi:10.1145/3313950.3314188 · Cited by 2
Marhl, U., Jodko-Władzińska, A., Brühl, R., Sander, T. H., & Jazbinšek, V. (2019) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2019.8756998 · Cited by 3
Heugel, N., Liebenthal, E., & Beardsley, S. A. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108401 · Cited by 2
Infra-slow brain dynamics as a marker for cognitive function and decline.
Ajmera, S., Rajagopal, S., Rehman, R. U., & Sridharan, D. (2019) · PubMed · Cited by 2
Grafton, S. T. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.00148-3 · Cited by 2
Decision Neuroscience and Organizational Ethics
Robertson, D. C. (2019) · Advances in neuroethics · doi:10.1007/978-3-030-27177-0_9 · Cited by 2
de Beeck, H. O., & Nakatani, C. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316847916.016 · Cited by 2
fMRI and Human Pain Perception
Iannetti, G. D., & Apkarian, A. V. (2020) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190860509.013.11 · Cited by 2
Functional Magnetic Resonance Imaging
Hainc, N., Reinhardt, J., & Stippich, C. (2020) · Springer eBooks · doi:10.1007/978-3-030-48419-4_19 · Cited by 2
Kawashima, Y., Li, R., Chen, S. C., Vickery, R. M., Morley, J. W., & Tsuchiya, N. (2021) · PLoS ONE · doi:10.1371/journal.pone.0240147 · Cited by 2
Li, Z., & Shigemasu, H. (2021) · i-Perception · doi:10.1177/20416695211018222 · Cited by 2
Origins of Dissociations in the English Past Tense: A Synthetic Brain Imaging Model
Westermann, G., & Jones, S. D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.688908 · Cited by 3
Measuring Cerebellar Processing and Sensorimotor Functions in Non-Human Primates
Flierman, N. A., Avila, E., Zeeuw, C. I. D., & Badura, A. (2022) · Neuromethods · doi:10.1007/978-1-0716-2026-7_14 · Cited by 2
Jezzard, P., & Toosy, A. (2005) · Springer eBooks · doi:10.1007/3-540-27644-0_8 · Cited by 2
Hoptman, M. J., Tural, Ü., Lim, K. O., Javitt, D. C., & Oberlin, L. E. (2022) · Brain Sciences · doi:10.3390/brainsci12020156 · Cited by 2
The Electrophysiological Background of the fMRI Signal
Kayser, C., & Logothetis, N. K. (2013) · Springer eBooks · doi:10.1007/978-3-642-34342-1_4 · Cited by 2
Self-citationA multiplex of connectome trajectories enables several connectivity patterns in parallel
Mostame, P., Wirsich, J., Alderson, T. H., Ridley, B., Giraud, A., Carmichael, D. W., Vulliémoz, S., Guye, M., Lemieux, L., & Sadaghiani, S. (2025) · eLife · doi:10.7554/elife.98777.4 · Cited by 2
Hierarchical Causality Network: Find the Effective Connectivity in Visual Cortex
Du, M., Jing, H., Ma, Y., & Zheng, N. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-08333-4_33 · Cited by 2
Cognitive Functions as Revealed by Imaging of the Human Brain
Martín, R. S., & Huettel, S. A. (2012) · Springer eBooks · doi:10.1007/978-1-4614-1997-6_82 · Cited by 2
Fasoli, D., Coletta, L., Gutierrez‐Barragan, D., Gini, S., Gozzi, A., & Panzeri, S. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013995 · Cited by 2
Functional MRI Evidence for Neural Plasticity at Early Stages of Visual Processing in Humans
Schwartz, S. (2009) · Springer eBooks · doi:10.1007/978-4-431-73019-4_3 · Cited by 2
Tolias, A. S., Kourtzi, Z., & Logothetis, N. K. (2003) · MPG.PuRe (Max Planck Society) · doi:10.5555/949823.949832 · Cited by 2
Self-citationLiebenthal, E. (2011) · Biological and medical physics series · doi:10.1007/978-1-4419-9997-9_3 · Cited by 2
Huettel, S. A. (2009) · Elsevier eBooks · doi:10.1016/b978-008045046-9.00318-1 · Cited by 2
Incorporating Functional MRI into Clinical Pharmacology Trials
Verma, A. K., Declercq, R., Coimbra, A., & Achten, E. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0134-7_10 · Cited by 2
Structural and Functional Neuroimaging Methods: Applications to Substance Abuse and Addiction
Yang, Y., Chefer, S., Geng, X., Gu, H., Chen, X., & Stein, E. A. (2011) · Wiley eBooks · doi:10.1002/9781119998938.ch3 · Cited by 2
Cortical processing of vocal sounds in primates
Petkov, C. I., Kayser, C., & Logothetis, N. K. (2010) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-374593-4.00014-0 · Cited by 2
Self-citationNeuronal Models for EEG–fMRI Integration
Kilner, J. M., & Friston, K. J. (2009) · Springer eBooks · doi:10.1007/978-3-540-87919-0_22 · Cited by 2
Allocentric spatial judgements by re-mapping egocentric coordinates: a fMRI study.
Werner, S. (2005) · MPG.PuRe (Max Planck Society) · Cited by 2
Three correlated essays on the neural foundations of economic decision-making
Hsu, M. (2007) · doi:10.7907/je01-f098 · Cited by 2
Learning and Representation of Declarative Memories by Single Neurons in the Human Brain
Rutishauser, U. (2008) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/gx3n-qd05 · Cited by 2
Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis
Graben, P. B., Drenhaus, H., Brehm, E., Rhode, B., Saddy, D., & Frisch, S. (2007) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2795434 · Cited by 2
Nitsch, C. (2009) · Zeitschrift für Literaturwissenschaft und Linguistik · doi:10.1007/bf03379543 · Cited by 2
The discussion of methodological limitations in number representation studies is incomplete
Orban, G. A. (2009) · Behavioral and Brain Sciences · doi:10.1017/s0140525x09990823 · Cited by 2
Update on Temporal Lobe Epilepsy
Mirsattari, S. M., & Blume, W. T. (2013) · Epilepsy Research and Treatment · doi:10.1155/2013/579487 · Cited by 2
Remifentanil-Induced Cerebral Blood Flow Effects
Ramani, R. (2008) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000297420.13305.38 · Cited by 2
Biophysical Mechanisms Supporting Conscious Perception: Prospects for an Artificial AstrocytePreprint
Pereira, A., & Pereira, A. (2011) · Nature Precedings · doi:10.1038/npre.2011.6484.1 · Cited by 2
The circulatory and metabolic correlates of functional activity
Raichle, M. E. (2002) · International Congress Series · doi:10.1016/s0531-5131(02)00167-x · Cited by 2
Dienel, G. A. (2006) · Wiley eBooks · doi:10.1002/9783527611225.ch23 · Cited by 2
Löken, L. S. (2009) · Gothenburg University Publications Electronic Archive (Gothenburg University) · Cited by 2
Proton Density-Weighted Functional Magnetic Resonance Imaging at 0.35 Tesla
Yang, P., Wang, J., Chao, Y., Guang, L., & Shi, J. (2008) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2008.896 · Cited by 2
Allgemeine Physiologie der Großhirnrinde
Birbaumer, N., & Schmidt, R. F. (2010) · Springer-Lehrbuch · doi:10.1007/978-3-642-01651-6_8 · Cited by 2
Spontaneous blood oxygen fluctuation in awake and sedated brain cortex – a BOLD fMRI study
Vesa, K. (2004) · Cited by 2
The Cutting Edge of fMRI and High‐Field fMRI
Kim, D. (2005) · International review of neurobiology · doi:10.1016/s0074-7742(05)66005-9 · Cited by 2
Theories, data analysis, and simulation models in neuroimaging: an overview
Sommer, F. T., Hirsch, J. A., & Wichert, A. (2003) · The MIT Press eBooks · Cited by 2
Functional MRI of the Spinal Cord
Stroman, P. W., & Filippi, M. (2009) · Neuromethods · doi:10.1007/978-1-60327-919-2_28 · Cited by 2
Pourshoghi, A., Pourrezaei, K., & Zakeri, I. (2015) · doi:10.17918/etd-7184 · Cited by 2
Heterogeneous data fusion for brain psychology applications
Li, L. (2011) · Spiral (Imperial College London) · doi:10.25560/6420 · Cited by 2
Multimodal integration of EEG and functional magnetic resonance recordings
Babiloni, F., Claudio, B., Filippo, C., Maria, R. P., Alessandra, B., Laura, A., & Febo, C. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2004.1404483 · Cited by 2
Human cortical functions in auditory change detection evaluated with multiple brain research methods
Rinne, T. (2001) · Työväentutkimus Vuosikirja · Cited by 2
Mazziotta, J. C., & Toga, A. W. (2002) · Elsevier eBooks · doi:10.1016/b978-012693019-1/50033-2 · Cited by 2
Comparison of stimulus-evoked BOLD responses in human and monkey visual cortexPreprint
Patel, G. H., Cohen, A. L., Baker, J. T., Snyder, L. H., & Corbetta, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/345330 · Cited by 2
Prokopiou, P. C., Xifra‐Porxas, A., Kassinopoulos, M., Boudrias, M., & Mitsis, G. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.29.178483 · Cited by 2
Neurovascular-modulationPreprint
Khadka, N., & Bikson, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.21.214494 · Cited by 2
Biointegrated Implantable Brain Devices
Das, R., & Heidari, H. (2020) · Wiley eBooks · doi:10.1002/9781119644316.ch4 · Cited by 2
Sea of Electrodes Array (SEA): Customizable 3D High-Density High-Count Neural Probe Array Technology
Zardini, S., & ., S. I. A. (2019) · Deep Blue (University of Michigan) · Cited by 2
Laminar-specific interhemispheric connectivity mapping with bilateral line-scanning fMRIPreprint
Choi, S., Chen, Y., Zeng, H., Biswal, B. B., & Yu, X. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.08.433876 · Cited by 2
CORTICAL MECHANISMS OF SEEING AND HEARING SPEECH
Möttönen, R. (2004) · Aaltodoc (Aalto University) · Cited by 2
Schnitzler, A., & Pollok, B. (2010) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.6253958.6325058 · Cited by 2
Burhan, A. M., Bartha, R., Bocti, C., BORRIE, M. J., Laforce, R. J., Rosa‐Neto, P., & Soucy, J. (2013) · Alzheimer s Research & Therapy · doi:10.1186/alz192 · Cited by 2
High-resolution Dynamic Human Brain Neural Activity Recording Using 3T MRIPreprint
Zhang, Y., Sun, K., Ren, J., Hu, Q., Wang, Y., Li, S., Chen, T., Xu, N., Guo, N., Fu, X., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.31.542967 · Cited by 2
Shedding light on motor-independent communication
Nagels-Coune, L. (2024) · doi:10.26481/dis.20240418ln · Cited by 2
High-resolution awake mouse fMRI at 14 TeslaPreprint
Hike, D., Liu, X., Xie, Z., Zhang, B., Choi, S., Zhou, X. A., Liu, A., Murstein, A., Jiang, Y., Devor, A., et al. (2024) · eLife · doi:10.7554/elife.95528.1 · Cited by 2
Shatalina, E., Onwordi, E. C., Whitehurst, T., Whittington, A., Mansur, A., Arumuham, A., Marques, T. R., Gunn, R. N., Natesan, S., Rabiner, E. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.25.614893 · Cited by 2
Picha, S. G., Hojjati, S. H., Nayak, S., Ozoria, S., Chernek, P., Calimag, J., Yazdi, B. G., & Razlighi, Q. R. (2024) · medRxiv · doi:10.1101/2024.11.20.24317658 · Cited by 2
A multiplex of connectome trajectories enables several connectivity patterns in parallelPreprint
Mostame, P., Wirsich, J., Alderson, T. H., Ridley, B., Giraud, A., Carmichael, D. W., Vulliémoz, S., Guye, M., Lemieux, L., & Sadaghiani, S. (2025) · eLife · doi:10.7554/elife.98777.2 · Cited by 2
Moore, J. W., Bridgen, P., Pickles, E., Ciò, P. D., Billimoria, L., Tomazinho, I., Costa, C. D., Gallo, D., Hartung, G. A., Uus, A. U., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.18.670552 · Cited by 2
Desynchronization of pathological low-frequency brain activity by the hypnotic drug zolpidem.Preprint
Hall, S., Yamawaki, N., Fisher, A., Clauss, R. P., Woodhall, G. L., & Stanford, I. M. (2008) · Nature Precedings · doi:10.1038/npre.2008.1966.1 · Cited by 2
Brain plasticity and upper limb function after stroke : some implications for rehabilitation.
Lindberg, P. G. (2007) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 2
Le Bihan, D. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1858655 · Cited by 1
Suyono, T. A., Prasetyo, P. E. A., Shafira, E., Tanjung, F. Y., Syazwani, F., & Yudiana, W. (2020) · Jurnal Psikologi Sosial · doi:10.7454/jps.2020.14 · Cited by 1
Zhang, H., Chiu, P. W., Ip, I. N., Liu, T., Wong, G. H. Y., Song, Y., Wong, S. W. H., Herrup, K., & Mak, H. K. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15399 · Cited by 1
Lin, Y., Zhou, X., Naya, Y., Gardner, J. L., & Sun, P. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.541314 · Cited by 1
Im, K. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00004-1 · Cited by 1
Leicht, G., & Mulert, C. (2014) · Springer eBooks · doi:10.1007/978-3-642-54542-9_4 · Cited by 1
EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization
Rusiniak, M., Bornfleth, H., Cho, J., Wolak, T. P., Ille, N., Berg, P., & Scherg, M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.842420 · Cited by 1
Language Processing in Atypical Development: Looking Below the Surface with MEG
Mody, M. (2019) · Springer eBooks · doi:10.1007/978-3-030-00087-5_26 · Cited by 1
(2016) · Elsevier eBooks · doi:10.1016/b978-0-12-407908-3.00002-9 · Cited by 1
Deep Learning Techniques on Brain Images
Sahu, M., Upadhyay, Y., Khoriya, N., Biswas, A., Chandrawanshi, M., & Patel, O. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2273/1/012026 · Cited by 1
Christova, P. S., James, L. M., & Georgopoulos, A. P. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00252.2022 · Cited by 1
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
