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Neural masses and fields in dynamic causal modeling

Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This...

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Autores principales: Moran, Rosalyn, Pinotsis, Dimitris A., Friston, Karl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3664834/
https://www.ncbi.nlm.nih.gov/pubmed/23755005
http://dx.doi.org/10.3389/fncom.2013.00057
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author Moran, Rosalyn
Pinotsis, Dimitris A.
Friston, Karl
author_facet Moran, Rosalyn
Pinotsis, Dimitris A.
Friston, Karl
author_sort Moran, Rosalyn
collection PubMed
description Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity. We describe early developments using neural mass models, where convolution-based dynamics are used to generate responses in laminar-specific populations of excitatory and inhibitory cells. We show that these models, though resting on only two simple transforms, can recapitulate the characteristics of both evoked and spectral responses observed empirically. 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 between conductances and membrane potentials. We propose that conductance-based models may be more appropriate when spectra present with multiple resonances. Finally, we outline a third class of models, where each neuronal subpopulation is treated as a field; in other words, as a manifold on the cortical surface. By explicitly accounting for the spatial propagation of cortical activity through partial differential equations (PDEs), we show that the topology of connectivity—through local lateral interactions among cortical layers—may be inferred, even in the absence of spatially resolved data. We also show that these models allow for a detailed analysis of structure–function relationships in the cortex. Our review highlights the relationship among these models and how the hypothesis asked of empirical data suggests an appropriate model class.
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spelling pubmed-36648342013-06-10 Neural masses and fields in dynamic causal modeling Moran, Rosalyn Pinotsis, Dimitris A. Friston, Karl Front Comput Neurosci Neuroscience Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity. We describe early developments using neural mass models, where convolution-based dynamics are used to generate responses in laminar-specific populations of excitatory and inhibitory cells. We show that these models, though resting on only two simple transforms, can recapitulate the characteristics of both evoked and spectral responses observed empirically. 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 between conductances and membrane potentials. We propose that conductance-based models may be more appropriate when spectra present with multiple resonances. Finally, we outline a third class of models, where each neuronal subpopulation is treated as a field; in other words, as a manifold on the cortical surface. By explicitly accounting for the spatial propagation of cortical activity through partial differential equations (PDEs), we show that the topology of connectivity—through local lateral interactions among cortical layers—may be inferred, even in the absence of spatially resolved data. We also show that these models allow for a detailed analysis of structure–function relationships in the cortex. Our review highlights the relationship among these models and how the hypothesis asked of empirical data suggests an appropriate model class. Frontiers Media S.A. 2013-05-28 /pmc/articles/PMC3664834/ /pubmed/23755005 http://dx.doi.org/10.3389/fncom.2013.00057 Text en Copyright © 2013 Moran Pinotsis and Friston. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Neuroscience
Moran, Rosalyn
Pinotsis, Dimitris A.
Friston, Karl
Neural masses and fields in dynamic causal modeling
title Neural masses and fields in dynamic causal modeling
title_full Neural masses and fields in dynamic causal modeling
title_fullStr Neural masses and fields in dynamic causal modeling
title_full_unstemmed Neural masses and fields in dynamic causal modeling
title_short Neural masses and fields in dynamic causal modeling
title_sort neural masses and fields in dynamic causal modeling
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3664834/
https://www.ncbi.nlm.nih.gov/pubmed/23755005
http://dx.doi.org/10.3389/fncom.2013.00057
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