The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Hemodynamic correlates of EEG: A heuristic
Abstract
In this note we describe a heuristic, starting with a dimensional analysis, which relates hemodynamic changes to the spectral profile of ongoing EEG activity. In brief, this analysis suggests that 'activation', as indexed by increases in hemodynamic signals, should be associated with a loss of power in lower EEG frequencies, relative to higher frequencies. The fact that activation is expressed in terms of frequency (i.e., per second) is consistent with a dimensional analysis in the sense that activations reflect the rate of energy dissipation (per second). In this heuristic, activation causes an acceleration of temporal dynamics leading to (i) increased energy dissipation; (ii) decreased effective membrane time constants; (iii) increased effective coupling among neuronal ensembles; and (iv) a shift in the EEG spectral profile to higher frequencies. These predictions are consistent with empirical observations of how changes in the EEG spectrum are expressed hemodynamically. Furthermore, the heuristic provides a simple measure of neuronal activation based on spectral analyses of EEG.
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
“This is because it is generally assumed that fMRI signals scale with the predominant frequency of neuronal activity (Kilner et al., 2005; Laufs & Duncan 2007; Rosa et al., 2010).”
From Network discovery with DCM (2011) · cites this paper as Kilner et al., 2005
Citations
Cites
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Thalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564
The Theory of Stochastic Processes
Cox, D. R., Miller, H. D., Weiss, G., & Shuler, K. (1966) · Physics Today · doi:10.1063/1.3047872 · Cited by 3,324
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Dayan, P., & Abbott, L. F. (2001) · Cited by 3,153
Dynamic Statistical Parametric Mapping
Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., & Halgren, E. (2000) · Neuron · doi:10.1016/s0896-6273(00)81138-1 · Cited by 1,787
Synaptic depression and cortical gain control
Abbott, L. F., Varela, J. A., Sen, K., & Nelson, S. B. (1997) · Science · doi:10.1126/science.275.5297.221 · Cited by 1,690
The Theory of Stochastic Processes.
Bartholomew, D. J., Cox, D., & Miller, H. D. (1966) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2343509 · Cited by 1,201
Magistretti, P. J., Pellerin, L., Rothman, D. L., & Shulman, R. G. (1999) · Science · doi:10.1126/science.283.5401.496 · Cited by 1,170
Simultaneous EEG and fMRI of the alpha rhythm
Goldman, R. I., Stern, J. M., Engel, J., & Cohen, M. S. (2002) · Neuroreport · doi:10.1097/00001756-200212200-00022 · Cited by 1,131
Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., & Kleinschmidt, A. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1831638100 · Cited by 1,028
Cited by
Sorted by citations
Items 101–200 of 205
EEG-fMRI Based Information Theoretic Characterization of the Human Perceptual Decision System
Ostwald, D., Porcaro, C., Mayhew, S. D., & Bagshaw, A. P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033896 · Cited by 32
EEG-fMRI reciprocal functional neuroimaging
Yang, L., Liu, Z., & He, B. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.02.153 · Cited by 31
Cognitive neurophysiology: Beyond averaging
Fell, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.019 · Cited by 31
Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements
Al-Quraishi, M. S., Elamvazuthi, I., Tang, T. B., Al‐Qurishi, M., Adil, S. H., & Ebrahim, M. (2021) · Brain Sciences · doi:10.3390/brainsci11060713 · Cited by 33
Giovannella, M., Ibañez, D. P., Gregori-Pla, C., Kacprzak, M., Mitjà, G., Ruffini, G., & Durduran, T. (2018) · Neurophotonics · doi:10.1117/1.nph.5.1.015001 · Cited by 31
Visual areas become less engaged in associative recall following memory stabilization
Nieuwenhuis, I. L. C., Takashima, A., Oostenveld, R., Fernández, G., & Jensen, O. B. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.12.052 · Cited by 30
Johnson, M. H., Großmann, T., & Farroni, T. (2008) · Advances in child development and behavior · doi:10.1016/s0065-2407(08)00008-6 · Cited by 29
Temporal Profile of Amygdala Gamma Oscillations in Response to Faces
Sato, W., Kochiyama, T., Uono, S., Matsuda, K., Usui, K., Inoue, Y., & Toichi, M. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00142 · Cited by 30
Hemington, K. S., & Reynolds, J. N. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.03.025 · Cited by 27
Transverse patterning dissociates human EEG theta power and hippocampal BOLD activation
Meltzer, J. A., Fonzo, G. A., & Constable, R. T. (2008) · Psychophysiology · doi:10.1111/j.1469-8986.2008.00719.x · Cited by 26
Sclocco, R., Tana, M. G., Visani, E., Gilioli, I., Panzica, F., Franceschetti, S., Cerutti, S., & Bianchi, A. M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00186 · Cited by 24
fMRI MODELS OF DENDRITIC AND ASTROCYTIC NETWORKS
Poznański, R. R., & Riera, J. J. (2006) · Journal of Integrative Neuroscience · doi:10.1142/s0219635206001173 · Cited by 24
Marks, V. S., Saboo, K., Topçu, Ç., Lech, M., Thayib, T. P., Nejedlý, P., Křemen, V., Worrell, G. A., & Kucewicz, M. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118637 · Cited by 22
Omidvarnia, A., Pedersen, M., Vaughan, D. N., Walz, J. M., Abbott, D. F., Zalesky, A., & Jackson, G. D. (2017) · Human Brain Mapping · doi:10.1002/hbm.23723 · Cited by 23
High‐frequency peripheral vibration decreases completion time on a number of motor tasks
Macerollo, A., Palmer, C. E., Foltynie, T., Korlipara, P., Limousin, P. D., Edwards, M. J., & Kilner, J. M. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14050 · Cited by 22
Multimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of Integration
Blinowska, K. J., Müller-Putz, G., Kaiser, V., Astolfi, L., Vanderperren, K., Huffel, S. V., & Lemieux, L. (2009) · Computational Intelligence and Neuroscience · doi:10.1155/2009/813607 · Cited by 22
An electrophysiological validation of stochastic DCM for fMRI
Jean Daunizeau, A., Lemieux, L., Vaudano, A. E., Friston, K. J., & Stephan, K. E. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00103 · Cited by 21
Vitali, P. (2015) · Functional Neurology · doi:10.11138/fneur/2015.30.1.009 · Cited by 21
Decorrelated Input Dissociates Narrow Band γ Power and BOLD in Human Visual Cortex
Butler, R., Bernier, P., Lefebvre, J., Gilbert, G., & Whittingstall, K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3938-16.2017 · Cited by 26
Kodama, K., Takamoto, K., Nishimaru, H., Matsumoto, J., Takamura, Y., Sakai, S., Ono, T., & Nishijo, H. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00068 · Cited by 19
Dondé, C., Brevet-Aeby, C., Poulet, E., Mondino, M., & Brunelin, J. (2019) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2019.03.002 · Cited by 19
Moretti, D. V., Paternicò, D., Binetti, G. G., Zanetti, O. O., & Frisoni, G. B. (2014) · Neurodegenerative Diseases · doi:10.1159/000354863 · Cited by 19
Moretti, D. V. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00152 · Cited by 18
Fuglø, D., Pedersen, H., Rostrup, E., Hansen, A. E., & Larsson, H. B. W. (2011) · Magnetic Resonance in Medicine · doi:10.1002/mrm.23227 · Cited by 17
Davis, B., Jovicich, J., Iacovella, V., & Hasson, U. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs416 · Cited by 17
Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics
Lamoš, M., Mareček, R., Slavicek, T., Mikl, M., Rektor, I., & Jan, J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab66b · Cited by 17
Nangini, C., Tam, F., & Graham, S. J. (2007) · Human Brain Mapping · doi:10.1002/hbm.20361 · Cited by 17
Prokopiou, P. C., Xifra‐Porxas, A., Kassinopoulos, M., Boudrias, M., & Mitsis, G. D. (2022) · Brain Topography · doi:10.1007/s10548-022-00898-w · Cited by 15
Evaluating Models of the Ageing BOLD Response
Henson, R. N., Olszowy, W., Tsvetanov, K. A., Yadav, P. S., Cam‐CAN, & Zeidman, P. (2024) · Human Brain Mapping · doi:10.1002/hbm.70043 · Cited by 16
What can be found in scalp EEG spectrum beyond common frequency bands. EEG–fMRI study
Mareček, R., Lamoš, M., Mikl, M., Barton, M., Fajkus, J., Rektor, & Brázdil, M. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046026 · Cited by 16
Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA
Labounek, R., Bridwell, D. A., Mareček, R., Lamoš, M., Mikl, M., Slavicek, T., Bednařík, P., Baštinec, J., Hluštı́k, P., Brázdil, M., et al. (2017) · Brain Topography · doi:10.1007/s10548-017-0585-8 · Cited by 16
Moretti, D. V. (2016) · Clinical Interventions in Aging · doi:10.2147/cia.s103313 · Cited by 15
Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain
Carmichael, D. W., Vulliémoz, S., Murta, T., Chaudhary, U. J., Perani, S., Rodionov, R., Rosa, M. J., Friston, K. J., & Lemieux, L. (2024) · Bioengineering · doi:10.3390/bioengineering11030224 · Cited by 16
Fang, G., Xue, F., Yang, P., Cui, J., Brauth, S. E., & Tang, Y. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.02.042 · Cited by 15
Labounek, R., Lamoš, M., Mareček, R., Brázdil, M., & Jan, J. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.02.016 · Cited by 15
Cortical oscillatory activity associated with the perception of illusory and real visual contours
Kinsey, K., Anderson, S. J., Hadjipapas, A., Nevado, Á., Hillebrand, A., & Holliday, I. E. (2009) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2009.04.004 · Cited by 15
Moretti, D. V. (2015) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s93253 · Cited by 15
Understanding and supporting anonymity policies in peer review
Nobarany, S., & Booth, K. S. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23711 · Cited by 15
Getzmann, S., Reiser, J. E., Karthaus, M., Rudinger, G., & Wascher, E. (2021) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2021.729197 · Cited by 14
Neural signatures of heterogeneity in risk‐taking and strategic consistency
Leota, J., Kleinert, T., Tran, A., & Nash, K. A. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15476 · Cited by 14
Labounek, R., Bridwell, D. A., Mareček, R., Lamoš, M., Mikl, M., Bednařík, P., Baštinec, J., Slavicek, T., Hluštı́k, P., Brázdil, M., et al. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.02.012 · Cited by 13
Miladinović, A., Barbaro, A., Valvason, E., Ajčević, M., Accardo, A., Battaglini, P. P., & Jarmolowska, J. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-31635-8_137 · Cited by 12
Right-lateralized intrinsic brain dynamics predict monitoring abilities
Ambrosini, E., Capizzi, M., Arbula, S., & Vallesi, A. (2020) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-020-00769-6 · Cited by 12
Neurophysiology: The Brain at Work
Friston, K. J. (2008) · Current Biology · doi:10.1016/j.cub.2008.03.042 · Cited by 12
MEG Responses to the Perception of Global Structure within Glass Patterns
Swettenham, J. B., Anderson, S. J., & Thai, N. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013865 · Cited by 12
Bridging the gap – Spontaneous fluctuations shape stimulus-evoked spectral powerPreprint
Wainio‐Theberge, S., Wolff, A., & Northoff, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.23.166058 · Cited by 12
Ghouse, A., Faes, L., & Valenza, G. (2021) · Physical review. E · doi:10.1103/physreve.104.064208 · Cited by 11
Mareček, R., Lamoš, M., Labounek, R., Bartoň, M., Slavicek, T., Mikl, M., Rektor, I., & Brázdil, M. (2017) · Neural Computation · doi:10.1162/neco_a_00933 · Cited by 10
Understanding early dementia: EEG, MRI, SPECT and memory evaluation
Moretti, D. V. (2015) · Translational Neuroscience · doi:10.1515/tnsci-2015-0005 · Cited by 9
Reality-Based Brain-Computer Interaction
Sjölie, D., Eriksson, J., Bodin, K., & Janlert, L.-E. (2010) · doi:10.13140/2.1.1565.2800 · Cited by 9
EEG spectral attractors identify a geometric core of brain dynamics
Pourdavood, P., & Jacob, M. (2024) · Patterns · doi:10.1016/j.patter.2024.101025 · Cited by 12
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
Mullinger, K. J., Castellone, P., & Bowtell, R. W. (2013) · Journal of Visualized Experiments · doi:10.3791/50283-v · Cited by 8
Decoding speed of imagined hand movement from EEG
Yuan, H., Perdoni, C., & He, B. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627414 · Cited by 8
Generalized EEG-FMRI spectral and spatiospectral heuristic models
Labounek, R., Janeček, D., Mareček, R., Lamoš, M., Slavicek, T., Mikl, M., Baštinec, J., Bednařík, P., Bridwell, D. A., Brázdil, M., et al. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493379 · Cited by 7
Using multi-modal neuroimaging to characterise social brain specialisation in infants
SIDDIQUI, M., Pinti, P., Brigadoi, S., Lloyd‐Fox, S., Elwell, C. E., Johnson, M. H., Tachtsidis, I., & Jones, E. J. H. (2023) · eLife · doi:10.7554/elife.84122 · Cited by 7
Electroencephalographic Changes Following Direct Current Deep Brain Stimulation of Auditory Cortex
Rojas, J. O. D., Saunders, J. A., Luminais, C., Hamilton, R. H., & Siegel, S. J. (2012) · Neurosurgery · doi:10.1227/neu.0b013e31827b93c0 · Cited by 7
The neural dynamics of positive and negative expectations of pain
Wittkamp, C. A., Wolf, M., & Rose, M. (2024) · eLife · doi:10.7554/elife.97793.3 · Cited by 9
fMRI-EEG Fingerprint Regression Model for Motor Cortex
Rudnev, V., Мельников, М. Е., Melnikov, M., Savelov, A. A., Shtark, M. B., & Sokhadze, E. M. (2021) · NeuroRegulation · doi:10.15540/nr.8.3.162 · Cited by 6
Goncalves, S. I., Bijma, F., Pouwels, P. J. W., Jonker, M. A., Kuijer, J. P. A., Heethaar, R. M., Lopes, F. H. D. S., & de Munck, J. C. (2008) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2008.2009082 · Cited by 6
B, B. G. F. G. (2013) · Journal of Neurology & Neurophysiology · doi:10.4172/2155-9562.1000160 · Cited by 5
Local Field Potential, Relationship to BOLD Signal
Logothetis, N. K., & Panzeri, S. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_726-1 · Cited by 5
EEG-informed fMRI analysis during a hand grip task
Sclocco, R., Tana, M. G., Visani, E., Gilioli, I., Panzica, F., Franceschetti, S., Cerutti, S., & Bianchi, A. M. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347019 · Cited by 4
Moretti, D. V. (2015) · PubMed Central · Cited by 4
Experimental Design and Data Analysis Strategies
Bénar, C., Bagshaw, A. P., & Lemieux, L. (2009) · Springer eBooks · doi:10.1007/978-3-540-87919-0_12 · Cited by 4
Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities
Ward, T. E. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_2 · Cited by 4
Semenkov, I., Rudych, P. D., & Ossadtchi, A. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.20.624528 · Cited by 4
Azzazy, S., Ghaffarianhoseini, A., GhaffarianHoseini, A., Naismith, N., & Omrany, H. (2024) · Architectural Science Review · doi:10.1080/00038628.2024.2424861 · Cited by 3
Labounek, R., Wu, Z., Bridwell, D. A., Brázdil, M., Jan, J., & Nestrašil, I. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.644874 · Cited by 3
Stable EEG Spatiospectral Sources Using Relative Power as Group-ICA Input
Labounek, R., Bridwell, D. A., Mareček, R., Lamoš, M., Mikl, M., Brázdil, M., Jan, J., & Hluštı́k, P. (2018) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-9038-7_22 · Cited by 3
Multimodal neuroimaging of fatigability development
Bédard, P., Knutson, K. M., McGurrin, P. M., Felipe Vial, U., Popa, T., Horovitz, S. G., Hallett, M., Nath, A., & Walitt, B. T. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.132 · Cited by 4
Timinkul, A., Omori, T., Wanpen, S., Yamada, Y., & Soya, H. (2010) · Archives of Allied Health Sciences · Cited by 2
EEG–fMRI Information Fusion: Biophysics and Data Analysis
Trujillo‐Barreto, N. J., Jean Daunizeau, A., Laufs, H., & Friston, K. J. (2022) · Springer eBooks · doi:10.1007/978-3-031-07121-8_28 · Cited by 2
Target Detection Modulates Electroencephalographic Spectral Correlates of Memory Encoding
Broitman, A. W., & Swallow, K. M. (2025) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02328 · Cited by 4
Methodology of combined EEG and fMRI
Laufs, H., & Krakow, K. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088268.012 · Cited by 2
Rule-base wearable embedded platform for seizure detection from real EEG data in ambulatory state
Shakir, M., Malik, A. S., Kamel, N., & Qidwai, U. A. (2014) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tenconspring.2014.6862995 · Cited by 2
Different LFP frequency bands convey complementary information about the BOLD signal
Magri, C., Schridde, U., Panzeri, S., Murayama, Y., & Logothetis, N. K. (2011) · BMC Neuroscience · doi:10.1186/1471-2202-12-s1-p204 · Cited by 2
Simultaneous EEG-fMRI in Epilepsy
Wiest, R., Abela, E., & Rummel, C. (2014) · Medical radiology · doi:10.1007/978-3-662-45123-6_6 · Cited by 2
Integrating EEG–fMRI Through Brain Simulation
Schirner, M., & Ritter, P. (2022) · Springer eBooks · doi:10.1007/978-3-031-07121-8_30 · 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
Deneux, T. (2011) · InTech eBooks · doi:10.5772/16490 · Cited by 1
Guseynov, A. G. (2021) · Journal of Evolutionary Biochemistry and Physiology · doi:10.1134/s0022093021060089 · Cited by 1
Qin, L., Dou, M., Niu, L., Huang, L., Li, F., Bao, S., Deng, X., Li, G., & Geng, Y. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3493925 · Cited by 1
Estimation of the haemodynamic response to epileptic activity in EEG-fMRI data
Leite, M., Figueiredo, P., & Leal, A. (2012) · IEEE Portuguese Meeting on Bioengineering · doi:10.1109/enbeng.2012.6331398 · Cited by 1
Combining fMRI with Other Modalities: Multimodal Neuroimaging
Lemieux, L., Whittingstall, K., & Uludaǧ, K. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_25 · Cited by 1
An information theoretic approach to EEG-fMRI integration
Ostwald, D. (2010) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 1
Target Detection Modulates EEG Spectral Correlates of Memory EncodingPreprint
Broitman, A. W., & Swallow, K. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.24.538090 · Cited by 1
EEG spectral attractors identify a geometric core of resting brain activityPreprint
Pourdavood, P., & Jacob, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.13.562264 · Cited by 1
EEG-fMRI signatures of spontaneous brain activity in healthy volunteers and epilepsy patients
Laufs, H. (2014) · UCL Discovery (University College London) · Cited by 1
Development and application of model selection methodsfor investigating brain function
Rosa, M. J. (2012) · UCL Discovery (University College London)
Multimodality in Brain Imaging: Methodologic Aspects and Applications
Goncalves, S. I. (2008) · Springer eBooks · doi:10.1007/978-1-4020-9086-8_5
Nofzinger, E. A., Maquet, P., & Thorpy, M. J. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088268.003
Hemington, K. S. (2013) · QSpace (Queen's University Library)
Simultaneous EEG-fMRI Study of Audiovisual Sensory Processing
Shams, N. (2017) · TSpace (University of Toronto)
Local Field Potential, Relationship to BOLD Signal
Logothetis, N. K., & Panzeri, S. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_726
Proceedings of the 2022 ISNR Annual Conference: Poster Presentations
International Society of NeuroRegulation and Research (2022) · NeuroRegulation · doi:10.15540/nr.9.4.198
Evaluating models of the ageing BOLD responsePreprint
Henson, R. N., Olszowy, W., Tsvetanov, K. A., Yadav, P. S., CamCAN, & Zeidman, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.24.554634
The neural dynamics of positive and negative expectations of painPreprint
Wittkamp, C. A., Wolf, M., & Rose, M. (2024) · eLife · doi:10.7554/elife.97793.2
Metadata
- Authors
- Kilner, J. M., Mattout, J., Henson, R., & Friston, K. J.
- Affiliations
- National Hospital for Neurology and Neurosurgery (GB)
- Corresponding author
- Kilner, J.M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 28
- Issue
- 1
- Pages
- 280-286
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2153427235
- Cited by
- 206
- References
- 36
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 10 · 2023: 4 · 2022: 7 · 2021: 9 · 2020: 5 · 2019: 7 · 2018: 9 · 2017: 10 · 2016: 9 · 2015: 12 · 2014: 11 · 2013: 16 · 2012: 16
- FWCI
- 5.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Keywords
- neuronal activation, dimensional analysis, energy dissipation, membrane time constant, neuronal ensembles, hemodynamic response
- MeSH terms
- Algorithms, Cerebrovascular Circulation, Electroencephalography, Energy Metabolism, Hemodynamics, Humans, Magnetic Resonance Imaging, Models, Neurological, Oxygen
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