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 4,601–4,700 of 6,331
Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humansPreprint
Franceschiello, B., Rumac, S., Leidi, M., Hilbert, T., Nau, M., Dziadosz, M., Degano, G., Roy, C., Gaglianese, A., Petri, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.13.540663 · Cited by 4
Lee, A. M., Sturm, V. E., Dawes, H. E., Krystal, A. D., & Chang, E. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.05.583610 · Cited by 4
Brain network estimation from dense EEG signals : application to neurological disorders
Kabbara, A. (2018) · theses.fr (ABES) · doi:10.70675/d3d377dbze714z4938z995cz0e931172cb78 · Cited by 4
Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia
Ding, J., Thye, M. D., Edmondson-Stait, A. J., Szaflarski, J. P., & Mirman, D. (2024) · Brain Communications · doi:10.1093/braincomms/fcae313 · Cited by 4
Fairchild, G. T., Holler, D. E., Fabbri, S., Gomez, M. A., Rudd, M. E., & Snow, J. C. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf210 · Cited by 3
Joint models reveal human subcortical underpinnings of choice and learning behavior
Miletić, S., Stevenson, N., Bazin, P., Alkemade, A., Isherwood, S. J., Trutti, A. C., Tse, D. H. Y., Haberg, A. K., & Forstmann, B. U. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502269122 · Cited by 3
A critical review of high‐frequency activity for functional mapping in SEEG
Sagar, P., Hudson, M. R., Rayner, G., O'Brien, T. J., Laing, J., & Neal, A. J. (2025) · Epilepsia · doi:10.1111/epi.18619 · Cited by 3
Krohne, L. G., Nygaard, S., Ottenheijm, M. E., Liu, M. L., Thielscher, A., Siebner, H. R., & Madsen, K. H. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121533 · Cited by 3
Zhou, Z., Liu, J., Wu, W. E., Fang, R., Liu, S., Wei, Q., Yan, R., Guo, Y., Tao, Q., Wang, Y., et al. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00911-9 · Cited by 3
20 years of concept cells: From invariant responses to a unique coding of human memory
Quiroga, R. Q. (2026) · Neuron · doi:10.1016/j.neuron.2026.01.026 · Cited by 3
Weber, A. J., Ng, B., Greathouse, K. M., Bennett, D. A. A., Tasaki, S., Gaiteri, C., & Herskowitz, J. H. (2026) · Neuron · doi:10.1016/j.neuron.2026.03.001 · Cited by 3
Spiking the mind: Rethinking the role of cortical feedback in visual mental imagery.
Koenig‐Robert, R., Pace, T., & Pearson, J. (2026) · Psychological Review · doi:10.1037/rev0000621 · Cited by 3
Hanna, S., Ho, V. M., Lucas, M., Vadera, S., Qing, K. Y., Deng, D., King‐Stephens, D., Sazgar, M., Mnatsakanyan, L., Lin, J., et al. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae60d0 · Cited by 3
Quantum‐Like Dynamics in Whole‐Brain Models of the Human Connectome
Deco, G., Perl, Y. S., Greenstein, N., Chandaria, S., Scholes, G. D., & Kringelbach, M. L. (2026) · Advanced Science · doi:10.1002/advs.77103 · Cited by 3
Wang, Q., Tao, Y., Yuan, J., Jiang, Z., Ding, Z., Zhang, Z., Zhao, J. L., & Wang, J. (2019) · Fibers and Polymers · doi:10.1007/s12221-019-8770-x · Cited by 3
Vaudano, A. E., Olivotto, S., Ruggieri, A., Gessaroli, G., Talami, F., Parmeggiani, A., GIORGIS, V. D., Veggiotti, P., & Meletti, S. (2019) · Human Brain Mapping · doi:10.1002/hbm.24815 · Cited by 3
Soltysik, D. A. (2020) · Brain and Behavior · doi:10.1002/brb3.1617 · Cited by 3
Presurgical Functional MRI and Diffusion Tensor Imaging
Stippich, C. (2021) · Medical radiology · doi:10.1007/978-3-030-83343-5_1 · Cited by 3
Li, Q., Dong, Y., Zhang, Y., Wang, X., Jiang, N., Huang, J., Cui, H., Tian, L., Zheng, Y., Li, X., et al. (2023) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio58561.2023.10354892 · Cited by 3
A Functional Magnetic Resonance Imaging Study of Phrase Structure and Subject Island Violations
Matchin, W., Almeida, D., Hickok, G. S., & Sprouse, J. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02266 · Cited by 3
Lafitte, M. N., Andrea, C.-S., Kadone, H., Hoshino, E., Yamazaki, M., Minagawa‐Kawai, Y., & Suzuki, K. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3578796 · Cited by 3
Ultra-high field MRI compatible electrical and optical neural interface
Zhang, Y., Song, L., Wang, A., Zhan, G., Zhang, X., Zhang, L., Gan, Z., & Kang, X. (2025) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2025.116805 · Cited by 3
Yu, M., Liu, J., & Ji, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111877 · Cited by 3
Xifra‐Porxas, A., Kassinopoulos, M., Prokopiou, P. C., Boudrias, M., & Mitsis, G. D. (2025) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2025.1653206 · Cited by 3
The structure and function of intrinsic brain activity
Lewis, C. (2015) · Cited by 3
Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis
Arumana, J. M. (2012) · Cited by 3
Machine Learning for Functional Brain Mapping
Björnsdotter, M. (2010) · InTech eBooks · doi:10.5772/8605 · Cited by 3
Spatial computation with gamma oscillations
Engelhard, B., & Vaadia, E. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00165 · Cited by 4
Sinha, R. K., & Prasad, G. (2013) · Journal of Clinical Engineering · doi:10.1097/jce.0b013e3182a9045a · Cited by 3
Adaptive experimental condition selection in event-related fMRI
Bakhous, C., Forbes, F., Vincent, T. L., Chaâri, L., Michel Dojat, D., & Ciuciu, P. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235920 · Cited by 3
Strelnikov, K., & Barone, P. (2013) · Journal of Neuroscience and Neuroengineering · doi:10.1166/jnsne.2013.1086 · Cited by 3
Combined functional data from multispectral non-ionizing and non-invasive brain imaging
Karanasiou, I. S. (2009) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2009.5394312 · Cited by 3
The Emerging Standard Neurobiological Model of Decision Making
Wu, S., & Glimcher, P. W. (2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199844371.013.45 · Cited by 3
Rajput, R., Kaur, R., Chadha, R., Mani, S., Rachana, R., Kaur, H., & Singh, M. (2018) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-5225-5282-6.ch001 · Cited by 4
Task-Based Presurgical Functional MRI in Patients with Brain Tumors
Stippich, C., Blatow, M., & Alzamora, M. G. (2021) · Medical radiology · doi:10.1007/978-3-030-83343-5_4 · Cited by 3
Mononen, T., Kujala, J., Liljeström, M., Leppäaho, E., Samuel Kaski, A., & Salmelin, R. H. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1019572 · Cited by 3
Experimental Design and Data Analysis Strategies
Wirsich, J., Bagshaw, A. P., Guye, M., Lemieux, L., & Bénar, C. (2022) · Springer eBooks · doi:10.1007/978-3-031-07121-8_12 · Cited by 3
Advancements in Measuring Cognition Using EEG and fNIRS
Chandra, S., & Choudhury, A. (2023) · Springer eBooks · doi:10.1007/978-981-19-1550-5_102-1 · Cited by 3
Multimodal methods to help interpret resting-state fMRI
Zhou, X. A., Jiang, Y., Man, W., & Yu, X. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91688-2.00007-2 · Cited by 3
Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI
Ubaghs, R. L. E. M., Boehringer, R., Marks, M., Hesse, H. K., Yanik, M. F., Zerbi, V., & Grewe, B. F. (2026) · Nature Methods · doi:10.1038/s41592-026-03154-2 · Cited by 3
Yen, S., Wang, Y., & Liao, L. (2024) · APL Bioengineering · doi:10.1063/5.0193267 · Cited by 3
Actionable Event Annotation and Analysis in fMRI: A Practical Guide to Event Handling
Denissen, M., Richlan, F., Birklbauer, J., Pawlik, M., Ravenschlag, A. N., Himmelstoß, N. A., Hutzler, F., & Robbins, K. A. (2024) · Neuromethods · doi:10.1007/978-1-0716-4260-3_7 · Cited by 3
Natural Course of Disease of Spinal Cord Injury
Schubert, M. J. W. (2017) · Springer eBooks · doi:10.1007/978-3-319-46293-6_4 · Cited by 3
Inter-subject fMRI registration based on functional networks
Chen, H., Zhao, Y., Li, Y., Lv, J., & Liu, T. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950653 · Cited by 3
Naming disorders anomic aphasia.
Lorwatanapongsa, P. (2005) · PubMed · Cited by 3
Seeing patterns in neuroimaging data
Wright, J. (2018) · Progress in brain research · doi:10.1016/bs.pbr.2018.10.025 · Cited by 3
Transsacadic Information and Corollary Discharge in Local Field Potentials of Macaque V1
Paradiso, M. A., Akers-Campbell, S., Ruiz, O., Niemeyer, J. E., Geman, S., & Loper, J. (2019) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00063 · Cited by 3
Ultrafast fMRI sequences for studying the cognitive brain architectures
Ushakov, V., Orlov, V. A., Kartashov, S., & Malakhov, D. G. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.11.099 · Cited by 3
Quiñones, D. R., Fernández-Mollá, L. M., Pacheco‐Torres, J., Caramés, J. M., Canals, S., & Moratal, D. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.01.009 · Cited by 3
A Review of Machine Learning Models for Predicting Autism Spectrum Disorder
Kanchanamala, P., & Sagar, G. L. (2019) · Helix · doi:10.29042/2019-4797-4801 · Cited by 3
Beratung, Coaching und Neurowissenschaft – Produktive Zusammenarbeit oder geschickte Vermarktung?
Voigt, T. H. (2019) · Organisationsberatung Supervision Coaching · doi:10.1007/s11613-019-00612-z · Cited by 3
Bressler, D. W., Rokem, A., & Silver, M. A. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01470 · Cited by 3
Functional MRI biomarkers of peripartum psychiatric disorders
Hare, M. M., Duan, C., & Deligiannidis, K. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815508-0.00013-8 · Cited by 3
Imaging Guidance for Therapeutic Delivery: The Dawn of Neuroenergetics
Alambyan, V., Pace, J., Sukpornchairak, P., Yu, X., Alnimir, H., Tatton, R., Chitturu, G., Yarlagadda, A., & Ramos-Estébanez, C. (2020) · Neurotherapeutics · doi:10.1007/s13311-020-00843-4 · Cited by 3
Combining Noninvasive Electromagnetic and Hemodynamic Measures of Human Brain Activity
Lin, F., Witzel, T., Hämäläinen, M., & Nummenmaa, A. R. (2020) · Springer eBooks · doi:10.1007/978-3-030-45623-8_10 · Cited by 3
Connecting Brain and Machine: The Mind Is the Next Frontier
Vukelić, M. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-64590-8_16 · Cited by 3
Long-Term Stable Recording of Single-Neuron Spike Activity in the Amygdala in Conscious Rabbits
Vasil’eva, L. N., & Bondar, I. V. (2021) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-021-01075-5 · Cited by 3
Functional Magnetic Resonance Imaging
Liu, Z., & Cao, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_11 · Cited by 3
Research on Characteristic of Chronic Spontaneous Urticaria Based on Multiscale Entropy
Wang, S., Song, P., Ma, R., Wang, Y., Yu, B., Wang, M., Wang, M., Shen, J., Dai, Y., Wang, Y., et al. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/6691356 · Cited by 3
Intelligent command and control of UAV Based on brain computer and eye tracking combined technology
Wu, X., Li, H., & Chen, J. (2021) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec51613.2021.9482111 · Cited by 3
Measuring Trust in Social Neuroeconomics: a Tutorial
Engelmann, J. B. (2010) · Hermeneutische Blätter · doi:10.51686/hbl.2010.1.17 · Cited by 3
Modeling the effect of cerebral capillary blood flow on neuronal firing
Chen, L., Hannawi, Y., & Terman, D. H. (2022) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2022.111018 · Cited by 3
MEG and Multimodal Integration
Ahlfors, S. P. (2019) · Springer eBooks · doi:10.1007/978-3-030-00087-5_7 · Cited by 3
Wu, J., Nahab, F. B., Allen, J. W., Hu, R., Dehkharghani, S., & Qiu, D. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.780896 · Cited by 3
Physiological Foundations of Neural Signals
Whittingstall, K., & Logothetis, N. K. (2013) · CRC Press eBooks · doi:10.1201/b14756-3 · Cited by 3
Self-citationFunctional MRI in Radiology—A Personal Review
Lotze, M., Domín, M., Langner, S., & Platz, T. (2022) · Healthcare · doi:10.3390/healthcare10091646 · Cited by 3
Classification of Attention Deficit Hyperactivity Disorder Using Machine Learning
Singh, J., Kaur, G., & Kapoor, N. (2022) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat55367.2022.9971947 · Cited by 3
Optical Neuroimaging: Studies of the Neuronal and Vascular Effects of Cocaine
Du, C., Volkow, N. D., & Pan, Y. (2022) · Springer eBooks · doi:10.1007/978-3-030-88832-9_166 · Cited by 3
High-frequency neuronal signal better explains multi-phase BOLD response
Zhang, Q., Cramer, S. R., Turner, K. L., Neuberger, T., Drew, P. J., & Zhang, N. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119887 · Cited by 3
Ma, J., Shen, L., Song, L., Guo, Q., Parvizi, J., Han, B., & Chen, Q. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad083 · Cited by 3
Nakamura, N. H., Fukunaga, M., Nakamura, N. H., & Oku, Y. (2023) · Progress in Neurobiology · doi:10.60124/j.pneuro.2023.10.01 · Cited by 4
Predicting Autism Spectrum Disorder Based on Resting-State fMRI and Graph Theory
Qiu, L., & Zhai, J. (2024) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit61980.2024.10581646 · Cited by 3
Mapping brain maturation and sexual dimorphism in adolescence
Paus, T. (2008) · doi:10.1017/cbo9780511551963.006 · Cited by 3
fMRI: Applications in Cognitive Neuroscience
D’Esposito, M., Kayser, A. S., & Chen, A. (2009) · Neuromethods · doi:10.1007/978-1-60327-919-2_10 · Cited by 3
Application of Pharmacological MRI to Preclinical Drug Discovery and Development
Ireland, M. D., & Williams, S. (2007) · Springer eBooks · doi:10.1007/1-4020-3910-7_100 · Cited by 3
Sports Performance and the Brain
Nakata, H. (2015) · Springer eBooks · doi:10.1007/978-4-431-55315-1_1 · Cited by 3
Sensitivity of MRI for directly detecting neuronal electrical activities in rat brain slices
Kim, D., Someya, T., & Sekino, M. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6609764 · Cited by 3
Functional MRI detection of hemodynamic response of repeated median nerve stimulation
Ai, L., Oya, H., Howard, M. A., & Xiong, J. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.10.012 · Cited by 3
BOLD signal and vessel dynamics: a hierarchical cluster analysis
Garreffa, G., Ken, S., Macrì, M. A., Giulietti, G., Giove, F., Colonnese, C., Venditti, E., Cesare, E. D., Galasso, V., & Maraviglia, B. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2005.11.004 · Cited by 3
A role for neuronal KATP channels in metabolic control of the seizure gate
Yamada, K., & Inagaki, N. (2001) · Trends in Pharmacological Sciences · doi:10.1016/s0165-6147(00)01840-x · Cited by 3
Tourette syndrome, ADHD, and the limbic system: investigating the pathophysiology
Sörös, P. (2008) · Developmental Medicine & Child Neurology · doi:10.1111/j.1469-8749.2008.03009.x · Cited by 3
Pre-activating the mesial temporal lobe facilitates learning
Chan, A. S.-Y., & Cheung, M. (2006) · Neuroscience Letters · doi:10.1016/j.neulet.2006.07.064 · Cited by 3
Optical imaging of visually guided reaching in macaque posterior parietal cortex
Heider, B., & Siegel, R. M. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0513-y · Cited by 3
Spatiotemporal scales and links between electrical neuroimaging modalities
Andino, S. L. G., Perrig, S. H., & Menéndez, R. G. D. P. (2011) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-011-0769-4 · Cited by 3
Meeting Report: Choosing the Right MR Tools for the Job
Fitzpatrick, S. M., & Rothman, D. L. (2002) · Journal of Cognitive Neuroscience · doi:10.1162/08989290260138690 · Cited by 3
Yoon, J. H., Larson, P., Grandelis, A., La, C., Cui, E., Carter, C. S., & Minzenberg, M. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00775 · Cited by 3
Exploring nociceptive response by BOLD fMRI in α-chloralose anesthetized rats
Shih, Y. I., Chen, Y., Chen, J.-M., Lung, Y., Chen, J., Chang, C., Chen, J., & Jaw, F. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.259508 · Cited by 3
Methods for Fine Scale Functional Imaging of Tactile Motion in Human and Nonhuman Primates
Friedman, R. M., Dillenburger, B., Wang, F., Avison, M. J., Gore, J. C., Roe, A. W., & Chen, L. M. (2011) · The Open Neuroimaging Journal · doi:10.2174/1874440001105010160 · Cited by 3
Use of Mri to Monitor Parkinson’s Disease
Planetta, P. J., Prodoehl, J., Corcos, D. M., & Vaillancourt, D. E. (2011) · Neurodegenerative Disease Management · doi:10.2217/nmt.10.6 · Cited by 3
Thompson, G. J., Pan, W., Magnuson, M. E., & Keilholz, S. D. (2012) · BMC Neuroscience · doi:10.1186/1471-2202-13-s1-o19 · Cited by 3
Dependence of Spike-Count Correlations on Spike-Train Statistics and Observation Time Scale
Tetzlaff, T., & Diesmann, M. (2010) · Springer eBooks · doi:10.1007/978-1-4419-5675-0_6 · Cited by 3
Neuronal representation of stand and squat in the primary motor cortex of monkeys
Ma, C., Ma, X., Zhang, H., Xu, J., & He, J. (2015) · Behavioral and Brain Functions · doi:10.1186/s12993-015-0061-0 · Cited by 3
Reproducible paired sources from concurrent EEG-fMRI data using BICAR
Brown, K., Kasper, R. W., Giesbrecht, B., Carlson, J. M., & Grafton, S. T. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.07.012 · Cited by 3
Presurgical Functional MRI and Diffusion Tensor Imaging
Stippich, C. (2014) · Medical radiology · doi:10.1007/978-3-662-45123-6_1 · Cited by 3
Ji, S.-Y., & Najarian, K. (2008) · IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) · doi:10.1109/bibmw.2008.4686210 · Cited by 3
Neurobiología de la toma de decisiones
Romo, R. (2003) · Cited by 3
Functional MRI of the Visual System
Dumoulin, S. O. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_15 · Cited by 4
The Social Brain in Childhood and Adolescence
Kadosh, K. C., & Haller, S. P. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00174-3 · Cited by 3
Guang, L., Liu, M., Li, L., & Chaohui, Y. (2002) · Chinese Physics Letters · doi:10.1088/0256-307x/19/9/353 · Cited by 3
Functional magnetic resonance imaging
Jackson, G. D., Badawy, R. A. B., & Gotman, J. (2012) · Handbook of clinical neurology · doi:10.1016/b978-0-444-52898-8.00023-9 · Cited by 3
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