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Conductance-based Dynamic Causal Modeling: A mathematical review of its application to cross-power spectral densities
[article]
2021
arXiv
pre-print
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Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. ...
In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. ...
Acknowledgements This paper is based on the MSc thesis "DCM for anti-NMDAR encephalitis revisited -detailed model testing and simplification" by Inês Pereira at the University of Zurich and ETH Zurich ...
arXiv:2104.02957v1
fatcat:n2rhpi4o6jclnknlbw4rb7wpyi
Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities
2021
Preserved Fulltext
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Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. ...
In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. ...
Acknowledgments This paper is based on the MSc thesis "DCM for anti-NMDAR encephalitis revisited -detailed model testing and simplification "by Inês Pereira at the University of Zurich and ETH Zurich ...
doi:10.5167/uzh-211733
fatcat:wv52yhccvbeq7kfow2uzaulvey
Adiabatic dynamic causal modelling
2021
NeuroImage
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In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity ...
This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. ...
We conclude with a discussion of the limitations and potential applications of adiabatic DCM.
Theory This section provides a brief review of dynamic causal modelling. ...
doi:10.1016/j.neuroimage.2021.118243
pmid:34116151
pmcid:PMC8350149
fatcat:rgmthiuvojffzow7pr3r5jhk4e
Neural masses and fields in dynamic causal modeling
2013
Frontiers in Computational Neuroscience
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Using an identical neuronal architecture, we show that a set of conductance based models-that consider the dynamics of specific ion-channels-present a richer space of responses; owing to non-linear interactions ...
This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. ...
It therefore only makes sense to consider mean field treatments of conductance-based models. ...
doi:10.3389/fncom.2013.00057
pmid:23755005
pmcid:PMC3664834
fatcat:zvjhx44ylbdh3daetqky6lypji
Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy
2015
NeuroImage
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Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory-inhibitory balance. ...
Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. ...
Dynamic causal modelling of cross spectral density can be implemented using the DCM Toolbox. ...
doi:10.1016/j.neuroimage.2014.12.007
pmid:25498428
pmcid:PMC4306529
fatcat:q7yss2ng2vhw7egfaoo37tdh44
Dynamic causal modelling revisited
2017
NeuroImage
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This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. ...
In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. ...
DCM for ERP or cross spectral density responses. ...
doi:10.1016/j.neuroimage.2017.02.045
pmid:28219774
pmcid:PMC6693530
fatcat:dpkawcek4nbndkwsq5pehtle4a
Bayesian Modelling of Induced Responses and Neuronal Rhythms
2016
Brain Topography
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of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. ...
We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s10548-016-0526-y
pmid:27718099
fatcat:pswqqxexfjbw7k46qbjxauz2ry
On conductance-based neural field models
2013
Frontiers in Computational Neuroscience
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This technical note introduces a conductance-based neural field model that combines biologically realistic synaptic dynamics-based on transmembrane currents-with neural field equations, describing the ...
However, convolution and conductance-based models showed qualitatively different changes in power, with convolution models showing decreases with increasing inhibition, while conductance models show the ...
Conductance-based models have a long history in mathematical neuroscience; for a detailed review, see (Tuckwell, 2005) . ...
doi:10.3389/fncom.2013.00158
pmid:24273508
pmcid:PMC3824089
fatcat:55e7ucz4ufewnnrafh4zddfvna
Dynamic causal modeling with neural fields
2012
NeuroImage
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Specifically, we consider neural field models of cortical activity as generative models in the context of dynamic causal modeling (DCM). ...
This allows us to formulate a neural field model that can be reduced to a neural mass model using appropriate constraints on its spatial parameters. ...
We are most grateful to Professor Peter Dayan, Dr Jean Daunizeau and Dr Vladimir Litvak for useful discussions. ...
doi:10.1016/j.neuroimage.2011.08.020
pmid:21924363
pmcid:PMC3236998
fatcat:eeuk47vtafc6hj5yzizk2yjfoa
Computational Models in Electroencephalography
2021
Brain Topography
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The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. ...
In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level ...
Acknowledgements The authors would like to thank Ana Hernando ...
doi:10.1007/s10548-021-00828-2
pmid:33779888
pmcid:PMC8813814
fatcat:2gbjkwizhndxvksrt7fvirilia
E-pile model of self-organized criticality
[article]
2007
arXiv
pre-print
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Variations of the model apply to a number of other physical problems, such as electric plasma discharges, dielectric relaxation, and the dynamics of the Earth's magnetotail. ...
We introduce a new metaphor, the e-pile model, and a formalism for electric conduction in random media to compute critical exponents for such a system. ...
The SOC state features no intrinsic length or time scale, its fluctuations are scale-free and are characterized by an inverse powerlaw power spectral density (PSD). ...
arXiv:0711.4571v1
fatcat:josiobmvrjb3vmwqtarezvsi5m
Consistent spectral predictors for dynamic causal models of steady-state responses
2011
NeuroImage
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In this paper, we examine the dynamic repertoires of nonlinear conductancebased neural population models and propose a generative model of their power spectra. ...
models of neuronal dynamics. ...
In conclusion, this paper makes two contributions to the ongoing development of dynamic causal models of electrophysiological data: it augments recent conductance-based models with an NMDA channel, and ...
doi:10.1016/j.neuroimage.2011.01.012
pmid:21238593
pmcid:PMC3093618
fatcat:2pv2znuexrerxfwnsynyzbwxgy
Computational models in Electroencephalography
[article]
2020
arXiv
pre-print
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The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. ...
In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level ...
Conflict of interest The authors declare that they have no conflict of interest.
Authors contribution K.G. and B.F. conceived the review. J.C. and A.R. contributed to section 2.2. ...
arXiv:2009.08385v1
fatcat:ajypakrxxndnvlosmxu2rai4cm
Dynamic causal modelling of lateral interactions in the visual cortex
2013
NeuroImage
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This paper presents a dynamic causal model based upon neural field models of the Amari type. ...
We consider the application of these models to non-invasive data, with a special focus on the mapping from source activity on the cortical surface to a single channel. ...
Penny and A. Bastos for useful discussions. ...
doi:10.1016/j.neuroimage.2012.10.078
pmid:23128079
pmcid:PMC3547173
fatcat:ghi7v5kyybfjhjq4hix2uiklie
Computational and dynamic models in neuroimaging
2010
NeuroImage
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It builds on the distinction between models of the brain as a computational machine and computational models of neuronal dynamics per se; i.e., models of brain function and biophysics. ...
In terms of biophysical modelling, we focus on dynamic causal modelling, with a special emphasis on recent advances in neural-mass models for hemodynamic and electrophysiological time series. ...
We are particularly grateful to Michael Breakspear for several ideas and references. ...
doi:10.1016/j.neuroimage.2009.12.068
pmid:20036335
pmcid:PMC2910283
fatcat:5ntuy7bdfvau3afpxpp254romq
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