Dynamic causal models and physiological inference: a validation study using isoflurane anaesthesia in rodents
Open accessAbstract
Generative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABA(A) receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.
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Validation studies in humans and animals show that DCM for EEG can capture short-term changes in synaptic efficacy, such as neuromodulation of glutamatergic receptor conductances, and distinguish different types of synaptic plasticity in cortical microcircuits.
“Validation studies in humans and animals have shown that DCM for EEG is capable of capturing short-term changes in synaptic efficacy, such as neuromodulation of glutamatergic receptor conductances, and distinguish different types of synaptic plasticity in cortical microcircuits (66, 132, 138–140)”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [139]
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Cites
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · cited 3× · see citing works
“For this, we examined a hierarchical sensory structure using invasive recordings and inferred a connectivity architecture that is predicted by anatomical data [42].”
MethodInfluentialPreface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,412 · see citing works
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423 · cited 1× · see citing works
“fixed effects analysis of the models was performed using the group Bayes factor [47,50], with log-evidences averaged over hemi-”
MethodSelf-citationJansen, B. H., & Rit, V. G. (1995) · Biological Cybernetics · doi:10.1007/bf00199471 · Cited by 1,159 · cited 1× · see citing works
“kernel model [14] is often used within DCMs of M/EEG and LFP”
MethodDysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring
Stephan, K. E., Friston, K. J., & Frith, C. D. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn176 · Cited by 1,134 · see citing works
Self-citationVariational free energy and the Laplace approximation
Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.035 · Cited by 880 · cited 2× · see citing works
“Variational Bayesian techniques allow us to invert this generative model given real data and provide posterior densities over the parameters and the model evidence [35].”
BackgroundSelf-citationComparing dynamic causal models.
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.026 · Cited by 848 · cited 3× · see citing works
“This hypothesis was tested using Bayesian model selection (BMS) based on the evidence for competing models [47].”
BackgroundMethodSelf-citationMechanisms of Actions of Inhaled Anesthetics
Campagna, J. A., Miller, K. W., & Forman, S. A. (2003) · New England Journal of Medicine · doi:10.1056/nejmra021261 · Cited by 805 · cited 3× · see citing works
“Concerning inhibitory neurotransmission, increased inhibition due to isoflurane has been attributed to a sensitisation of GABAA receptors [18] and increased presynaptic release of GABA [19].”
BackgroundTen simple rules for dynamic causal modeling
Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E. M., Daunizeau, J., & Friston, K. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.015 · Cited by 803 · cited 2× · see citing works
“Several validation studies have shown been performed previously [31].”
BackgroundSelf-citationDynamic causal modeling of evoked responses in EEG and MEG
David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., & Friston, K. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.10.045 · Cited by 620 · see citing works
Self-citationMultiple sparse priors for the M/EEG inverse problem.
Friston, K., Harrison, L., Daunizeau, J., Kiebel, S., Phillips, C., Trujillo-Barreto, N., Henson, R., Flandin, G., & Mattout, J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.048 · Cited by 589 · cited 2× · see citing works
“both evoked and ongoing steady-state activity [55,56].”
BackgroundSelf-citationIdentifying neural drivers with functional MRI: an electrophysiological validation
David, O., Guillemain, I., Saillet, S., Reyt, S., Deransart, C., Segebarth, C., & Depaulis, A. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060315 · Cited by 533 · cited 1× · see citing works
“For example, simultaneous electrophysiological recordings and fMRI showed that DCM for fMRI could infer the origin of epileptic activation spread [57].”
BackgroundPreserved Feedforward But Impaired Top-Down Processes in the Vegetative State
Boly, M., Garrido, M. I., Gosseries, O., Bruno, M., Boveroux, P., Caroline Schnakers, A., Massimini, M., Vladimir Litvak, A., Laureys, S., & Friston, K. J. (2011) · Science · doi:10.1126/science.1202043 · Cited by 499 · cited 1× · see citing works
“[30].”
Self-citationFrequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex
Whittingstall, K., & Logothetis, N. K. (2009) · Neuron · doi:10.1016/j.neuron.2009.08.016 · Cited by 364 · see citing works
Evaluation of different measures of functional connectivity using a neural mass model
Olivier David, A., Cosmelli, D. J., & Friston, K. J. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.10.006 · Cited by 339 · cited 1× · see citing works
“Crucially, these extrinsic connections can be of a forward, backward or lateral type, depending on the subpopulations targeted by afferents from the pyramidal population of each source [34].”
BackgroundSelf-citationInterpretation of action potentials evoked in the cerebral cortex
Eccles, J. C. (1951) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(51)90033-8 · Cited by 289 · see citing works
Friston, K. J., Li, B., Daunizeau, J., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.039 · Cited by 283 · see citing works
Self-citationSuffczyński, P., Kalitzin, S., Pfurtscheller, G., & Lopes, F. H. D. S. (2001) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(01)00177-5 · Cited by 266 · cited 2× · see citing works
“tions of detailed physiological processes including thalamic burst firing [1], spike frequency adaptation [10], neuronal noise [11],”
BackgroundContrasting Synaptic Actions of the Inhalational General Anesthetics Isoflurane and Xenon
de Sousa, S. L. M., Dickinson, R., Lieb, W. R., & Franks, N. P. (2000) · Anesthesiology · doi:10.1097/00000542-200004000-00024 · Cited by 244 · see citing works
Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization
Kiebel, S. J., David, O., & Friston, K. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.055 · Cited by 233 · see citing works
Self-citationDynamic causal modelling of evoked potentials: A reproducibility study
Garrido, M. I., Kilner, J. M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.03.014 · Cited by 233 · cited 1× · see citing works
“For MEG, EEG or LFP data, detailed biophysical neural mass models serve as generative models for both evoked, time domain data [4,32] and steady-state, frequency domain data [29].”
MethodSelf-citationAnatomy of the auditory cortex.
Pandya, D. Ν. (1995) · PubMed · Cited by 231 · see citing works
Flood, P. D., Ramírez-Latorre, J. A., & Role, L. W. (1997) · Anesthesiology · doi:10.1097/00000542-199704000-00016 · Cited by 223 · see citing works
SELECTIVE ACTIONS OF VOLATILE GENERAL ANAESTHETICS AT MOLECULAR AND CELLULAR LEVELS
Franks, N. P., & Lieb, W. R. (1993) · British Journal of Anaesthesia · doi:10.1093/bja/71.1.65 · Cited by 218 · cited 1× · see citing works
“predictions: Here, we use isoflurane, a volatile anaesthetic agent that is used commonly in animal laboratory studies [16].”
MethodA neural mass model of spectral responses in electrophysiology
Moran, R., Kiebel, S. J., Stephan, K. E., Reilly, R. B., Jean Daunizeau, A., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.032 · Cited by 214 · cited 3× · see citing works
“tions of detailed physiological processes including thalamic burst firing [1], spike frequency adaptation [10], neuronal noise [11],”
BackgroundMethodSelf-citationModel driven EEG/fMRI fusion of brain oscillations
Valdes-Sosa, P. A., Sánchez‐Bornot, J. M., Sotero, R. C., Iturria‐Medina, Y., Alemán‐Gómez, Y., Bosch‐Bayard, J., Carbonell, F., & Ozaki, T. (2008) · Human Brain Mapping · doi:10.1002/hbm.20704 · Cited by 207 · cited 1× · see citing works
“as generative models for empirical M/EEG and LFP data [1,2,3,4,5,6,7,8,9].”
BackgroundRedefining the tonotopic core of rat auditory cortex: Physiological evidence for a posterior field
Doron, N. N., LeDoux, J. E., & Semple, M. N. (2002) · The Journal of Comparative Neurology · doi:10.1002/cne.10412 · Cited by 197 · cited 2× · see citing works
“The anatomical labelling of auditory fields was taken from [38] and matched to a rat brain from our animals.”
BackgroundMethodDynamic causal models of steady-state responses
Moran, R. J., Stephan, K. E., Seidenbecher, T., Pape, H.-C., Dolan, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.048 · Cited by 195 · cited 3× · see citing works
“More sophisticated neural state equations were used in subsequent DCM implementations for non-invasive electrophysiological data (M/EEG) [4,5] and invasive (LFP) recordings [29].”
MethodSelf-citationBanks, M. I., & Pearce, R. (1998) · Anesthesiology · doi:10.1097/00000542-199901000-00018 · Cited by 182 · cited 1× · see citing works
“In some studies of spontaneous IPSPs, evidence of a paradoxical reduction of IPSP amplitude has been observed [58], however when including both evoked and spontaneous IPSPs, findings show a net increase in the transfer of negative charge to the postsynaptic cells [59].”
ResultSteyn‐Ross, M. L., Steyn‐Ross, D. A., Sleigh, J. W., & Liley, D. T. J. (1999) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.60.7299 · Cited by 178 · see citing works
Origin, structure, and role of background EEG activity. Part 4: Neural frame simulation
Freeman, W. J. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.10.025 · Cited by 177 · cited 1× · see citing works
“These are an input (synaptic) convolution kernel, which converts presynaptic firing to a postsynaptic membrane potential, and an output sigmoidal function that relates mean postsynaptic potential to an average firing rate [33].”
BackgroundRealistically Coupled Neural Mass Models Can Generate EEG Rhythms
Sotero, R. C., Trujillo‐Barreto, N. J., Iturria‐Medina, Y., Carbonell, F., & Jiménez, J. C. (2007) · Neural Computation · doi:10.1162/neco.2007.19.2.478 · Cited by 161 · cited 1× · see citing works
“as generative models for empirical M/EEG and LFP data [1,2,3,4,5,6,7,8,9].”
BackgroundVariational Bayes for generalized autoregressive models
Roberts, S. J., & Penny, W. D. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.801921 · Cited by 157 · cited 1× · see citing works
“, the log probability of observing the data given the model) which trades-off model accuracy and complexity in a principled way [39,45,47].”
BackgroundLaminar connections of the cat's auditory cortex
Kelly, J. P., & Wong, D. (1981) · Brain Research · doi:10.1016/0006-8993(81)90027-5 · Cited by 142 · see citing works
Miao, N., Frazer, M. J., & Lynch, C. I. (1995) · Anesthesiology · doi:10.1097/00000542-199509000-00019 · Cited by 126 · cited 2× · see citing works
“Isoflurane reduces the strength of synaptic signalling following activation of both nonNMDA [20,21] and in some cases to a greater [22] or equal [23] degree of NMDA receptors, as well as leading to its diminished release [21,24].”
BackgroundInfluentialBayesian estimation of synaptic physiology from the spectral responses of neural masses
Moran, R. J., Stephan, K. E., Kiebel, S. J., Rombach, N., O'Connor, W. T., Murphy, K. J., Reilly, R. B., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.025 · Cited by 126 · cited 5× · see citing works
“Other work demonstrated that DCM for SSR could detect known changes in synaptic transmission following a developmental perturbation of extracellular glutamate levels [15].”
BackgroundMethodInfluentialSelf-citationda Silva, F. L. (2004) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2004.10.010 · Cited by 118 · see citing works
1653 and Isoflurane Produce Similar Dose-related Changes in the Electroencephalogram of Pigs
Rampil, I. J., Welskopf, R. B., Brown, J. G., Eger, E. I., Johnson, B. H., Holmes, M. A., & Donegan, J. H. (1988) · Anesthesiology · doi:10.1097/00000542-198809000-00002 · Cited by 115 · cited 1× · see citing works
“An examination of our time series data revealed burst activity at low doses of anaesthetic, which dissipated progressively with higher doses in line with the known burst suppression effects of isoflurane (Figure 3A) [51].”
BackgroundA Parametric Empirical Bayesian framework for fMRI‐constrained MEG/EEG source reconstruction
Henson, R. N., Flandin, G., Friston, K. J., & Mattout, J. (2010) · Human Brain Mapping · doi:10.1002/hbm.20956 · Cited by 113 · see citing works
Self-citationNishikawa, K., & MacIver, M. B. (2000) · Anesthesiology · doi:10.1097/00000542-200001000-00035 · Cited by 112 · see citing works
Automatic Analysis and Monitoring of Burst Suppression in Anesthesia
Särkelä, M. O. K., Mustola, S. T., Seppänen, T., Koskinen, M., Lepola, P., Suominen, K., Juvonen, T. S., Tolvanen-Laakso, H., & Jäntti, V. (2002) · Journal of Clinical Monitoring and Computing · doi:10.1023/a:1016393904439 · Cited by 112 · see citing works
Cortical entropy changes with general anaesthesia: theory and experiment
Sleigh, J. W., Steyn‐Ross, D. A., Steyn‐Ross, M. L., Grant, C. M., & Ludbrook, G. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/4/011 · Cited by 107 · cited 1× · see citing works
“Sophisticated biophysical models of anaesthesia have been developed to explain the theoretical properties they induce, such as phase transitions and hysteresis at transitions of consciousness [25,26] and to examine observed side effects such as epilepsy [27,28].”
BackgroundNishikawa, K., & MacIver, M. B. (2001) · Anesthesiology · doi:10.1097/00000542-200102000-00025 · Cited by 103 · cited 1× · see citing works
“In some studies of spontaneous IPSPs, evidence of a paradoxical reduction of IPSP amplitude has been observed [58], however when including both evoked and spontaneous IPSPs, findings show a net increase in the transfer of negative charge to the postsynaptic cells [59].”
ResultWinguth, S. D., & Winer, J. A. (1986) · The Journal of Comparative Neurology · doi:10.1002/cne.902480104 · Cited by 101 · cited 2× · see citing works
“, the log probability of observing the data given the model) which trades-off model accuracy and complexity in a principled way [39,45,47].”
BackgroundA disturbance of nonlinear interdependence in scalp EEG of subjects with first episode schizophrenia
Breakspear, M. J., Terry, J. R., Friston, K. J., Harris, A. W. F., Williams, L. M., Brown, K. J., Brennan, J., & Gordon, E. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00332-x · Cited by 99 · cited 1× · see citing works
“nonlinear channel conductances [12] and neuromodulation [13].”
Self-citationLiley, D. T. J., & Bojak, I. (2005) · PubMed · Cited by 98 · see citing works
Zavaglia, M., Laura Astolfi, A., Babiloni, F., & Ursino, M. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.04.022 · Cited by 94 · cited 1× · see citing works
“as generative models for empirical M/EEG and LFP data [1,2,3,4,5,6,7,8,9].”
BackgroundLukatch, H. S., Kiddoo, C. E., & MacIver, M. B. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhi015 · Cited by 92 · cited 1× · see citing works
“Across increasing dose levels, these spectra reflected the burst suppression as a decrease in lowfrequency power [52,53].”
BackgroundIsoflurane induces dose-dependent changes of thalamic somatosensory information transfer
Detsch, O., Vahle-Hinz, C., Kochs, E. F., Siemers, M., & Bromm, B. (1999) · Brain Research · doi:10.1016/s0006-8993(99)01341-4 · Cited by 90 · cited 1× · see citing works
“Studies of isoflurane at similar doses in rats have reported a nonlinear (saturating) increase in GABAergic synaptic transmission with increasing isoflurane dose [19,36], and a linear decrease in glutamatergic transmission [37].”
BackgroundPopulation dynamics under the Laplace assumption
Marreiros, A. C., Kiebel, S. J., Jean Daunizeau, A., Harrison, L. M., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.008 · Cited by 85 · cited 1× · see citing works
“tions of detailed physiological processes including thalamic burst firing [1], spike frequency adaptation [10], neuronal noise [11],”
BackgroundSelf-citationIsaev, D. S., Isaeva, E., Shatskikh, T. N., Zhao, Q., Smits, N. C., Shworak, N. W., Khazipov, R., & Holmes, G. L. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2033-07.2007 · Cited by 85 · cited 2× · see citing works
“Isoflurane reduces the strength of synaptic signalling following activation of both nonNMDA [20,21] and in some cases to a greater [22] or equal [23] degree of NMDA receptors, as well as leading to its diminished release [21,24].”
BackgroundInfluentialBayesian Model Selection for Group Studies
Stephan, K., Penny, W. D., Jean Daunizeau, A., Moran, R., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/s1053-8119(09)71793-8 · Cited by 83 · see citing works
Self-citationConsistent spectral predictors for dynamic causal models of steady-state responses
Moran, R. J., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.012 · Cited by 75 · see citing works
Self-citationThe effect of isoflurane on excitatory synaptic transmission in the rat hippocampus
Berg–Johnsen, J., & Langmoen, I. A. (1992) · Acta Anaesthesiologica Scandinavica · doi:10.1111/j.1399-6576.1992.tb03480.x · Cited by 68 · cited 2× · see citing works
“has been found [17,37].”
BackgroundRiera, J. J., Aubert, E., Iwata, K., Kawashima, R., Wan, X., & Ozaki, T. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1646 · Cited by 62 · cited 1× · see citing works
“as generative models for empirical M/EEG and LFP data [1,2,3,4,5,6,7,8,9].”
BackgroundIsoflurane modulates glutamatergic and GABAergic neurotransmission in the amygdala
Ranft, A., Kurz, J., Deuringer, M., Haseneder, R., Dodt, H., Zieglgänsberger, W., Kochs, E. F., Eder, M., & Hapfelmeier, G. (2004) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2004.03603.x · Cited by 58 · cited 2× · see citing works
“Isoflurane reduces the strength of synaptic signalling following activation of both nonNMDA [20,21] and in some cases to a greater [22] or equal [23] degree of NMDA receptors, as well as leading to its diminished release [21,24].”
BackgroundInfluentialPopulation based models of cortical drug response: insights from anaesthesia
Foster, B. L., Bojak, I., & Liley, D. T. J. (2008) · Cognitive Neurodynamics · doi:10.1007/s11571-008-9063-z · Cited by 52 · see citing works
Comparing dynamic causal models
Penny, W. D. (2004) · NeuroImage · doi:10.1016/s1053-8119(04)00164-8 · Cited by 50 · see citing works
Volatile anaesthetics: cellular mechanisms of action.
Langmoen, I. A., Larsen, M., & Berg–Johnsen, J. (1995) · PubMed · Cited by 40 · see citing works
Spectral and Temporal Processing in Rat Posterior Auditory Cortex
Pandya, P. K., Rathbun, D. L., Moucha, R., Engineer, N. D., & Kilgard, M. P. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm055 · Cited by 40 · see citing works
The dual pattern of corticothalamic projection of the primary auditory cortex in macaque monkey
Rouiller, E. M., & Durif, C. (2004) · Neuroscience Letters · doi:10.1016/j.neulet.2004.01.008 · Cited by 36 · cited 2× · see citing works
“In contrast, reciprocal backward connections terminate primarily outside of layer IV [43,45,46] and lateral connections impinge on all cell layers.”
BackgroundUrsino, M., Zavaglia, M., Laura Astolfi, A., & Babiloni, F. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0122-4 · Cited by 26 · cited 1× · see citing works
“as generative models for empirical M/EEG and LFP data [1,2,3,4,5,6,7,8,9].”
BackgroundEffect of isoflurane on release and uptake of gamma-aminobutyric acid from rat cortical synaptosomes
Larsen, M., Haugstad, T. S., Berg–Johnsen, J., & Langmoen, I. A. (1998) · British Journal of Anaesthesia · doi:10.1093/bja/80.5.634 · Cited by 24 · cited 4× · see citing works
“Notably, the increase in inhibition with higher isoflurane levels has been found to show a nonlinear (saturating) form [19].”
BackgroundInfluentialMean field model of acetylcholine mediated dynamics in the thalamocortical system
Clearwater, J. M., RENNIE, C. J., & Robinson, P. A. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.08.010 · Cited by 6 · cited 1× · see citing works
“nonlinear channel conductances [12] and neuromodulation [13].”
Flagged first, then by citations
Research works printed but not linked: 1
- [14]Jansen B Rit V 1995 Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics 73 357 366 7578475
Cited by
Sorted by citations
Analysing connectivity with Granger causality and dynamic causal modelling
Friston, K., Moran, R., & Seth, A. K. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.010 · Cited by 684 · see citing works
“DCM for evoked electrophysiological responses is introduced, using neural mass models with multiple neuronal populations [7] Reciprocal connections are shown to be necessary for generating late responses in EEG [8] DCM for steady state responses applied to intracranial EEG from rodents: synaptic measures validated usi…”
MethodSelf-citationComputational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531 · see citing works
Self-citationFree Energy, Precision and Learning: The Role of Cholinergic Neuromodulation
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4255-12.2013 · Cited by 312 · see citing works
“These models have been validated using animal electrophysiological recordings (Moran et al., 2008, 2011).”
MethodSelf-citationComputational approaches to psychiatry
Stephan, K. E., & Mathys, C. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.12.007 · Cited by 260 · see citing works
“, [66]), a recent proof-of-concept study in humans employed a dopaminergic drug challenge to demonstrate the feasibility of inferring DA-induced changes in NMDA and AMPA conductances in a prefrontal microcircuit [67 ].”
BackgroundSelf-citationRestoring brain function after stroke — bridging the gap between animals and humans
Ward, N. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.34 · Cited by 242 · see citing works
Electrophysiological Brain Connectivity: Theory and Implementation
He, B., Laura Astolfi, A., Valdes-Sosa, P. A., Marinazzo, D., Palva, S., Bénar, C., Michel, C. M., & Koenig, T. W. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913928 · Cited by 236 · see citing works
“Some illustrative applications range from LFP rodent data under anesthesia [94], ERPs to probe consciousness [94], [95] or auditory processing [96], and recently fluctuations at rest.”
BackgroundLFP and oscillations—what do they tell us?
Friston, K. J., Bastos, A. M., Pinotsis, D. A., & Vladimir Litvak, A. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.05.004 · Cited by 187 · see citing works
“There is an extensive literature on the validation of DCM ranging from face validation studies [16] to construct validation in terms of multimodal measurements [17], pharmacological manipulations [8,18] and psychophysical constructs [19]; for example, predictive coding.”
Self-citationTranslational perspectives for computational neuroimaging
Stephan, K. E., Iglesias, S., Heinzle, J., & Diaconescu, A. O. (2015) · Neuron · doi:10.1016/j.neuron.2015.07.008 · Cited by 169 · see citing works
Self-citationDissecting psychiatric spectrum disorders by generative embedding
Brodersen, K. H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W. D., Buhmann, J. M., & Stephan, K. E. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.11.002 · Cited by 173 · see citing works
“Furthermore, a conductance-based DCM of a prefrontal cortical column succeeded in detecting experimentally induced influences of dopamine on NMDA and AMPA receptor-mediated postsynaptic responses at glutamatergic synapses (Moran et al., 2011a,b).”
BackgroundInfluentialSelf-citationComputational neurorehabilitation: modeling plasticity and learning to predict recovery
Reinkensmeyer, D. J., Burdet, E., Casadio, M., Krakauer, J. W., Kwakkel, G., Lang, C. E., Swinnen, S. P., Ward, N., & Schweighofer, N. (2016) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-016-0148-3 · Cited by 181 · see citing works
TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169 · see citing works
“In particular, mathematical models of neuroimaging data, as obtained using functional magnetic resonance imaging (fMRI) and electro/magnetoencephalography (EEG/MEG), hold great promise as they might offer readouts of the symptom-producing physiological processes underlying brain disorders (9–14).”
Self-citationSladky, R., Höflich, A., Küblböck, M., Kraus, C., Baldinger, P., Moser, E., Lanzenberger, R., & Windischberger, C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht279 · Cited by 152 · see citing works
“2003) is a validated state of the art research method (Daunizeau et al. 2009; Schuyler et al. 2010; Moran et al. 2011) that has already provided very promising results in the context of clinical disorders (see Seghier et al.”
MethodA hemodynamic model for layered BOLD signals
Heinzle, J., Koopmans, P. J., den Ouden, H. E. M., Raman, S., & Stephan, K. E. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.025 · Cited by 144 · see citing works
“This source of information is exploited, for example, in DCMs of electrophysiological data (David et al., 2006; Moran et al., 2011), and themodel presented heremight allow to enable this application in fMRI, too.”
MethodSelf-citationCan Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
Haker, H., Schneebeli, M., & Stephan, K. E. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00107 · Cited by 130 · see citing works
Self-citationMoran, R. J., Symmonds, M., Dolan, R. J., & Friston, K. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003422 · Cited by 136 · see citing works
“These models have been previously validated using animal [30] and human recordings [31], and provide subject-specific measures of intrinsic (within source) and extrinsic (between source) synaptic coupling.”
Self-citationModeling ketamine effects on synaptic plasticity during the mismatch negativity
Schmidt, A., Diaconescu, A. O., Kometer, M., Friston, K. J., Stephan, K. E., & Vollenweider, F. X. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs238 · Cited by 109 · see citing works
Self-citationA short history of causal modeling of fMRI data
Stephan, K. E., & Roebroeck, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.034 · Cited by 112 · see citing works
Self-citationDynamic causal modeling with neural fields
Pinotsis, D. A., Moran, R., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.020 · Cited by 101 · see citing works
“Local fi eld potentials were recorded from primary (A1) and secondary (A2) auditory cortex in the Lister hooded rat, following the application of the anesthetic agent Iso fl urane; 1.4 mg (see Moran et al., 2011 for details).”
InfluentialSelf-citationAuksztulewicz, R., Schwiedrzik, C. M., Thesen, T., Doyle, W., Devinsky, O., Nobre, A. C., Schroeder, C. E., Friston, K. J., & Melloni, L. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0369-18.2018 · Cited by 83 · see citing works
“…used in several other DCM studies of evoked responses (Brown and Friston, 2012; Moran et al., 2013; Auksztulewicz and Friston, 2015), and neurophysiological inference using DCM has been validated in animal models (Moran et al., 2011) and invasive recordings in humans (Papadopoulou et al., 2015).”
BackgroundResultInfluentialSelf-citationClinical Concepts Emerging from fMRI Functional Connectomics
Matthews, P. M., & Hampshire, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.031 · Cited by 82 · see citing works
“Attractive complementary methods employ generative forward models based on prior information (Moran et al., 2011).”
Rosch, R. E., Hunter, P. R., Baldeweg, T., Friston, K. J., & Meyer, M. P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006375 · Cited by 74 · see citing works
Self-citationPhillips, H. N., Blenkmann, A. O., Hughes, L. E., Kochen, S. S., Bekinschtein, T. A., Cam‐CAN, & Rowe, J. B. (2016) · Cortex · doi:10.1016/j.cortex.2016.05.001 · Cited by 76 · see citing works
“…method correctly identifies the models behind simulated data (David et al., 2006; Friston et al., 2003; Razi, Kahan, Rees, & Friston, 2014), known connectivity and neural drivers in a rodent epilepsy model (David et al., 2008) and known connectivity changes due to anaesthesia (Moran et al., 2011).”
MethodGeneralized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex
Głąbska, H., Norheim, E. S., Devor, A., Dale, A. M., Einevoll, G. T., & Wojcik, D. K. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00001 · Cited by 70 · see citing works
“However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in theMUA (multi-unit activity; high-frequency part of the signal).”
BackgroundThe Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream
Auksztulewicz, R., Barascud, N., Cooray, G., Nobre, A. C., Chait, M., & Friston, K. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.0291-17.2017 · Cited by 55 · see citing works
“Neurophysiological inference using DCM has been validated in animal models (Moran et al., 2011) and invasive recordings in humans (Papadopoulou et al., 2015).”
MethodSelf-citationRehme, A. K., Eickhoff, S. B., & Grefkes, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.027 · Cited by 60 · see citing works
“Furthermore, layer-specific DCM of electrophysiological responses can infer changes in synaptic physiology (Moran et al., 2008; Moran et al., 2011).”
Stimulus Dependence of Local Field Potential Spectra: Experiment versus Theory
Barbieri, F., Mazzoni, A., Logothetis, N. K., Panzeri, S., & Brunel, N. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5365-13.2014 · Cited by 53 · see citing works
“Our attempt to fit network models to field potential data bears similarities to work described previously by Moran et al. (Moran et al., 2007; Moran et al., 2009; Moran et al., 2011).”
ResultNeural mass model-based tracking of anesthetic brain states
Kuhlmann, L., Freestone, D. R., Manton, J. H., Heyse, B., Vereecke, H. E. M., Lipping, T., Struys, M. M. R. F., & Liley, D. T. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.039 · Cited by 52 · see citing works
“In a validation study of dynamical causal modelling applied to isoflurane anesthesia and involving a networked variation of the JR model (Moran et al., 2011) it was found that the parameter estimates of the EPSP and IPSP amplitude decreased and increased, respectively, as was observed in rats who…”
MethodNMDA-receptor antibodies alter cortical microcircuit dynamics
Rosch, R. E., Wright, S. K., Cooray, G., Papadopoulou, M., Goyal, S., Lim, M. J., Vincent, A. C., Upton, A. L., Baldeweg, T., & Friston, K. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804846115 · Cited by 48 · see citing works
Self-citationDynamic causal modelling of electrographic seizure activity using Bayesian belief updating
Cooray, G., Sengupta, B., Douglas, P. K., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.063 · Cited by 44 · see citing works
“Variational Laplace Formore details, see Friston et al. (2007) andMathys (201)).”
BackgroundMethodInfluentialSelf-citationShaw, A. D., Moran, R., Muthukumaraswamy, S. D., Brealy, J., Linden, D. E. J., Friston, K. J., & Singh, K. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.034 · Cited by 48 · see citing works
“This has been done in preclinical (20, 21), pharmacological (22) and genetic (identified channelopathies (23)) studies using 4population, mean-field CMCs, as used in the present study.”
Self-citationDiwadkar, V. A., Bakshi, N. K., Gupta, G., Pruitt, P. J., White, R. T., & Eickhoff, S. B. (2014) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2014.00050 · Cited by 46 · see citing works
“It has hence repeatedly been proposed that effective connectivity is an important aspect of research on high-risk samples, not only because of its more realistic interactional model but also because of its reliance on Bayesian statistics (38, 92).”
BackgroundCharacterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling
Cooray, G., Sengupta, B., Douglas, P. K., Englund, M., Wickström, R., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.064 · Cited by 43 · see citing works
“The analysis described in the following sections used standard procedures for the Dynamic Causal modelling of spectral density (Moran et al., 2011a; Friston et al., 2012).”
MethodResultSelf-citationTracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy
Papadopoulou, M., Leite, M., van Mierlo, P., Vonck, K. E. J., Lemieux, L., Friston, K. J., & Marinazzo, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.007 · Cited by 41 · see citing works
“…is an extensive literature on the validation of DCM ranging from face validation studies (David et al., 2006) to validation in terms of multimodal measurements (David et al., 2008a), pharmacological manipulations (Moran et al., 2011a, 2011b) and psychophysical constructs (Brown and Friston, 2012).”
BackgroundSelf-citationEstimating Directed Connectivity from Cortical Recordings and Reconstructed Sources
Papadopoulou, M., Friston, K., & Marinazzo, D. (2019) · Brain Topography · doi:10.1007/s10548-015-0450-6 · Cited by 40 · see citing works
“It has also been applied to LFP recordings in rodents (Moran et al. 2009; Moran et al. 2011; Moran et al. 2015) and intracranial electroencephalographic (iEEG) in humans (Papadopoulou et al.”
MethodSelf-citationIdentifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference
Vattikonda, A. N., Hashemi, M., Sip, V., Woodman, M., Bartolomei, F., & Jirsa, V. (2021) · Communications Biology · doi:10.1038/s42003-021-02751-5 · Cited by 40 · see citing works
Generative models for clinical applications in computational psychiatry
Frässle, S., Yao, Y., Schöbi, D., Aponte, E. A., Heinzle, J., & Stephan, K. E. (2018) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1460 · Cited by 36 · see citing works
Self-citationShaw, A. D., Hughes, L. E., Moran, R., Coyle-Gilchrist, I., Rittman, T., & Rowe, J. B. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhz024 · Cited by 30 · see citing works
Self-citationDynamic causal modelling of seizure activity in a rat model
Papadopoulou, M., Cooray, G., Rosch, R. E., Moran, R., Marinazzo, D., & Friston, K. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.062 · Cited by 28 · see citing works
“When subpopulation responses are examined, we tend to see similar spectral behaviour across the spiny stellate cells, pyramidal cells and inhibitory interneurons (Moran et al. 2011).”
Self-citationThe maturation of mismatch negativity networks in normal adolescence
Cooray, G., Garrido, M. I., Brismar, T., & Hyllienmark, L. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.06.026 · Cited by 30 · see citing works
Changes in Effective Connectivity by Propofol Sedation
Gómez, F. A., Phillips, C. L. M., Soddu, A., Boly, M., Boveroux, P., Vanhaudenhuyse, A., Bruno, M., Gosseries, O., Bonhomme, V. L., Laureys, S., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071370 · Cited by 28 · see citing works
“In this case, probably the architecture is not fundamentally changed, it is rather the intensity of the connection the one that is modulated (increased/decreased) by the propofol dose [66].”
BackgroundSonkusare, S., Nguyen, V. T., Moran, R., van der Meer, J. N., Ren, Y., Koussis, N. C., Dionisio, S. A., Breakspear, M. J., & Guo, C. C. (2020) · Cortex · doi:10.1016/j.cortex.2020.05.018 · Cited by 28 · see citing works
Self-citationModels of neuromodulation for computational psychiatry
Iglesias, S., Tomiello, S., Schneebeli, M., & Stephan, K. E. (2016) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1420 · Cited by 26 · see citing works
Self-citationShaw, A. D., Muthukumaraswamy, S. D., Saxena, N., Sumner, R. L., Adams, N., Moran, R., & Singh, K. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117189 · Cited by 26 · see citing works
Self-citationOn conductance-based neural field models
Pinotsis, D. A., Leite, M., & Friston, K. J. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00158 · Cited by 24 · see citing works
“This means that conductance-based models may be more appropriate candidates to study synaptic function at the level of specific neurotransmitter systems (Faulkner et al., 2009; Moran et al., 2011a).”
Self-citationZhuang, Q., Qiao, L., Xu, L., Yao, S., Chen, S., Zheng, X., Li, J., Fu, M., Li, K., Vatansever, D., et al. (2023) · Psychoradiology · doi:10.1093/psyrad/kkad016 · Cited by 21 · see citing works
“…h has also been validated based on electrophysiological time series with respect to estimating activity on the synaptic or neuronal level in both animal models (Moran et al. , 2011 ; Papadopoulou et al. , 2017 ; Rosch et al. , 2018 ) and clinical studies in humans (P a padopoulou et al. , 2015 ).”
MethodAn 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 · see citing works
“In this context, the approach has proven successful in exploiting the realism of these biophysical models to capture experimental effects of cognitive manipulations in terms of network plasticity (e.g., Garrido et al., 2007 or Moran et al., 2011).”
MethodSelf-citationFacial Expressions Evoke Differential Neural Coupling in Macaques
Liu, N., Hadj‐Bouziane, F., Moran, R., Ungerleider, L. G., & Ishai, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv345 · Cited by 19 · see citing works
Self-citationUsing Bayesian analysis in repeated preclinical in vivo studies for a more effective use of animals
Walley, R. J., Sherington, J., Rastrick, J., Detrait, E., Hanon, É., & Watt, G. (2016) · Pharmaceutical Statistics · doi:10.1002/pst.1748 · Cited by 19 · see citing works
Pinotsis, D. A., Perry, G., Vladimir Litvak, A., Singh, K. D., & Friston, K. J. (2016) · Human Brain Mapping · doi:10.1002/hbm.23331 · Cited by 20 · see citing works
“…that is expressed in terms of aberrant connectivity and synaptic plasticity [Dima et al., 2010, 2012; Roiser et al., 2013], or changes in consciousness level due to drug effects; e.g., [Boly et al., 2011, 2012; Moran et al., 2011; Muthukumaraswamy et al., 2013; Schmidt et al., 2013].”
BackgroundSelf-citationDynamic causal modelling of phase-amplitude interactions
Fagerholm, E. D., Moran, R., Violante, I. R., Leech, R., & Friston, K. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116452 · Cited by 14 · see citing works
Self-citationDynamic Causal Modeling of the Prefrontal/Amygdala Network During Processing of Emotional Faces
Sladky, R., Hahn, A., Karl, I.-L., Geissberger, N., Kranz, G. S., Tik, M., Kraus, C., Pfabigan, D. M., Gartus, A., Lanzenberger, R. R., et al. (2021) · Brain Connectivity · doi:10.1089/brain.2021.0073 · Cited by 13 · see citing works
What Models and Tools can Contribute to a Better Understanding of Brain Activity?
Goodfellow, M., Andrzejak, R. G., Masoller, C., & Lehnertz, K. (2022) · Frontiers in Network Physiology · doi:10.3389/fnetp.2022.907995 · Cited by 13 · see citing works
“Examples of this kind of research are surprisingly lacking (Freeman, 1975; Moran et al., 2011), particularly studies making use of current methodologies like optogenetics (BernalCasas et al.”
BackgroundAnnealed Importance Sampling for Neural Mass Models
Penny, W. D., & Sengupta, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004797 · Cited by 10 · see citing works
“DCM can be applied to fMRI [3], EEG and MEG [4] and invasive electrophysiological data [5].”
BackgroundTodorović, A., & Auksztulewicz, R. (2019) · Hearing Research · doi:10.1016/j.heares.2019.107871 · Cited by 10 · see citing works
Probabilistic delay differential equation modeling of event-related potentials
Ostwald, D., & Starke, L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.025 · Cited by 11 · see citing works
Deep brain stimulation for neurodegenerative disease
Moran, R. (2015) · Progress in brain research · doi:10.1016/bs.pbr.2015.07.002 · Cited by 10 · see citing works
Self-citationDynamic Causal Modeling and Its Application to Psychiatric Disorders
Heinzle, J., & Stephan, K. E. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809825-7.00005-5 · Cited by 10 · see citing works
Self-citationDaun, S., Mantziaris, C., Toth, T. I., Büschges, A., & Rosjat, N. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220767 · Cited by 10 · see citing works
“This approach was developed for data recorded from the human brain and is widely used in the analysis of couplings in M/EEG and fMRI data [24–26], and in the analysis of local field potentials [18].”
MethodGamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure
Pinotsis, D. A., & Friston, K. J. (2014) · AIMS neuroscience · doi:10.3934/neuroscience.2014.1.18 · Cited by 9 · see citing works
“This means that conductance-based models allow one to perform a detailed study of synaptic function at the level of specific neurotransmitter systems [39,40].”
Self-citationHat Computational Psychiatry Relevanz für die klinische Praxis der Psychiatrie?
Stephan, K. E., Siemerkus, J., Bischof, M., & Haker, H. (2017) · Zeitschrift für Psychiatrie Psychologie und Psychotherapie · doi:10.1024/1661-4747/a000296 · Cited by 9 · see citing works
Self-citationJovellar, D. B., & Doudet, D. J. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00973 · Cited by 8 · see citing works
Schöbi, D., Tri, C., Frässle, S., Tittgemeyer, M., Heinzle, J., & Stephan, K. E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118567 · Cited by 7 · see citing works
Self-citationExtracting novel information from neuroimaging data using neural fields
Pinotsis, D. A., & Friston, K. J. (2014) · EPJ Nonlinear Biomedical Physics · doi:10.1140/epjnbp18 · Cited by 7 · see citing works
“Local field potentials were recorded from primary (A1) auditory cortex in the Lister hooded rat, following the application of the anaesthetic agent Isoflurane; 1.4 mg (see [22] for details).”
MethodInfluentialSelf-citationDCM, Conductance Based Models and Clinical Applications
Marreiros, A. C., Pinotsis, D. A., Brown, P., & Friston, K. J. (2015) · Springer series in computational neuroscience · doi:10.1007/978-3-319-20037-8_3 · Cited by 7 · see citing works
Self-citationShaw, A. D., Hughes, L. E., Moran, R., Coyle-Gilchrist, I. T. S., Rittman, T., & Rowe, J. B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/416388 · Cited by 8 · see citing works
“Such models can also assess the generators of MEG and EEG signals at rest and in more complex task (Moran, Symmonds, et al. 2011 ), optimized by inverting to evoked responses as we did in this study, or the spectral density (Moran et al. 2009; Moran, Stephan, et al. 2011).”
MethodSelf-citationNeural Fields, Masses and Bayesian Modelling
Pinotsis, D. A., & Friston, K. J. (2014) · Springer eBooks · doi:10.1007/978-3-642-54593-1_17 · Cited by 6 · see citing works
“We then consider particular applications, in the context of LFP (Moran et al., 2011) and MEG data (Schwarzkopf et al., 2012) and show how DCM allows one to adjudicate between alternative models of brain imaging data, such as neural masses and fields.”
MethodInfluentialSelf-citationPaparella, I., Cardone, P., Zanichelli, B., Lamalle, L., Collette, F., Sherif, S., Zubkov, M. A., Clarke, W. T., Stagg, C. J., Maquet, P., et al. (2025) · The Journal of Physiology · doi:10.1113/jp287311 · Cited by 5 · see citing works
Synaptic inhibitory dynamics drive benzodiazepine response in pediatric status epilepticus
Fedele, T., Burman, R. J., Steinberg, A., Selmin, G., Ramantani, G., & Rosch, R. E. (2025) · Epilepsia · doi:10.1111/epi.18398 · Cited by 5 · see citing works
“38 Here, we assume that the single EEG trace representative of regional activity is generated by the combined activity of four recurrently coupled neural masses—the canonical microcircuit (CMC) 39,40 —describing layered cortical populations as recurrently coupled sets of excitatory and inhibitory…”
Hofmann, D., Chesebro, A. G., Rackauckas, C., Mujica-Parodi, L. R., Friston, K. J., Edelman, A. S., & Strey, H. H. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.88 · Cited by 3 · see citing works
Self-citationComputational Models of Dysconnectivity in Large-Scale Resting-State Networks
Demirtaş, M., & Deco, G. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809825-7.00004-3 · Cited by 3 · see citing works
Synaptic assays: using biophysical models to infer neuronal dysfunction from non-invasive EEG
Roberts, J. A., & Breakspear, M. J. (2018) · Brain · doi:10.1093/brain/awy136 · Cited by 3 · see citing works
“controlled pharmacological manipulations can be made and accurate inference of corresponding changes then tested (Moran et al., 2011).”
BackgroundModeling cortical synaptic effects of anesthesia and their cholinergic reversal
Eniwaye, B. P., Booth, V., Hudetz, A. G., & Żochowski, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009743 · Cited by 2 · see citing works
Pizarro, R., Richner, T., Brodnick, S. K., Thongpang, S., Williams, J. C., & Veen, B. D. V. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.043 · Cited by 2 · see citing works
“In a different experiment, Moran et al. (2011) demonstrated how a network model of neurons, as suggested by Buzsaki et al., might be used to undertake the second step and use an established forward model to gain insight into local activity from global LFPs. Moran et al. applied increasing levels of…”
BackgroundMethodInfluentialNeural Oscillation as a Selective Modulatory Mechanism on Decision Confidence, Speed, and Accuracy
Azimi, A., & Wong‐Lin, K. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0880-25.2025 · Cited by 1 · see citing works
State space models and their spectral decomposition in dynamic causal modeling
Moran, R. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139941433.006 · Cited by 1 · see citing works
Self-citationBreaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudesPreprint
Fagerholm, E. D., Moran, R., Violante, I. R., Leech, R., & Friston, K. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.06315 · Cited by 1 · see citing works
Self-citationDynamic Causal Models for fMRI
Stephan, K. E. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00339-0
Self-citationAnesthesia, Neural Population Models of
Steyn‐Ross, D. A., Steyn‐Ross, M. L., & SLEIGH, J. W. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_52
Dynamics and network structure in neuroimaging data
Papadopoulou, M. (2015) · Ghent University Academic Bibliography (Ghent University)
Structural and functional neuroimaging of schizophrenia
Diaconescu, A. O., Iglesias, S., & Stephan, K. E. (2020) · doi:10.1093/med/9780198713005.003.0060
Self-citationModeling Synaptic Effects of Anesthesia and its Cortical Cholinergic ReversalPreprint
Eniwaye, B. P., Booth, V., Hudetz, A. G., & Żochowski, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.13.472343
Computational Psychiatry and the Bayesian Brain
Friston, K. J., & Dolan, R. J. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190681425.003.0072
Self-citationAnesthesia, Neural Population Models of
Steyn‐Ross, D. A., Steyn‐Ross, M. L., & SLEIGH, J. W. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_52
Anesthesia, Neural Population Models of
Steyn‐Ross, D. A., Steyn‐Ross, M. L., & SLEIGH, J. W. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_52-1
Paparella, I., Cardone, P., Zanichelli, B., Lamalle, L., Collette, F., Sherif, S., Zubkov, M. A., Clarke, W. T., Stagg, C. J., Maquet, P., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.12.603209
“NMM estimation of GABA activity has been validated by pharmacological interventions in humans and is considered equivalent to invasive interventions in animals 13,14,15,16 .”
BackgroundMethodCausal Analysis of Graph Signals for Brain Effectome Inference
Mutnuri, S., Adiga, A., Venkatramanan, S., & MARATHE, M. V. (2025) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf67917.2025.11443323
Zheng, W., & Geng, D. (2026) · iScience · doi:10.1016/j.isci.2026.115683
- Authors
- Moran, R. J., Jung, F., Kumagai, T., Endepols, H., Graf, R., Dolan, R. J., Friston, K. J., Stephan, K. E., & Tittgemeyer, M.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Max Planck Institute for Metabolism Research (DE)
- Corresponding author
- Moran, Rosalyn J.
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-08-02
- Volume
- 6
- Issue
- 8
- Pages
- e22790
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3149050
- OpenAlex
- W2105927641
- Cited by
- 88
- References
- 64
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 3 · 2019: 6 · 2018: 7 · 2017: 8 · 2016: 13 · 2015: 12 · 2014: 11 · 2013: 6 · 2012: 4
- FWCI
- 3.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- Explore Bristol Research (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Max Planck Institute for Plasma Physics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- VTechWorks (Virginia Tech) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (088130)
- Max-Planck-Gesellschaft
- SystemsX.ch
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- dynamic causal modeling, isoflurane anesthesia, synaptic transmission, excitatory postsynaptic potentials, inhibitory postsynaptic potentials, AMPA receptors, GABAA receptor, local field potentials, primary auditory cortex, neural mass model, reciprocal connections, model validation
- MeSH terms
- Animals, Bayes Theorem, Isoflurane, Models, Theoretical, Synaptic Transmission, Anesthetics, Inhalation
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