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A dynamic causal model for evoked and induced responses
Neuronal responses exhibit two stimulus or task-related components: evoked and induced. The functional role of induced responses has been ascribed to ‘top-down’ modulation through backward connections and lateral interactions; as opposed to the bottom-up driving processes that may predominate in evo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202632/ https://www.ncbi.nlm.nih.gov/pubmed/21835251 http://dx.doi.org/10.1016/j.neuroimage.2011.07.066 |
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author | Chen, Chun-Chuan Kiebel, Stefan J. Kilner, James M. Ward, Nick S. Stephan, Klaas E. Wang, Wei- Jen Friston, Karl J. |
author_facet | Chen, Chun-Chuan Kiebel, Stefan J. Kilner, James M. Ward, Nick S. Stephan, Klaas E. Wang, Wei- Jen Friston, Karl J. |
author_sort | Chen, Chun-Chuan |
collection | PubMed |
description | Neuronal responses exhibit two stimulus or task-related components: evoked and induced. The functional role of induced responses has been ascribed to ‘top-down’ modulation through backward connections and lateral interactions; as opposed to the bottom-up driving processes that may predominate in evoked components. The implication is that evoked and induced components may reflect different neuronal processes. The conventional way of separating evoked and induced responses assumes that they can be decomposed linearly; in that induced responses are the average of the power minus the power of the average (the evoked component). However, this decomposition may not hold if both components are generated by nonlinear processes. In this work, we propose a Dynamic Causal Model that models evoked and induced responses at the same time. This allows us to explain both components in terms of shared mechanisms (coupling) and changes in coupling that are necessary to explain any induced components. To establish the face validity of our approach, we used Bayesian Model Selection to show that the scheme can disambiguate between models of synthetic data that did and did not contain induced components. We then repeated the analysis using MEG data during a hand grip task to ask whether induced responses in motor control circuits are mediated by ‘top-down’ or backward connections. Our result provides empirical evidence that induced responses are more likely to reflect backward message passing in the brain, while evoked and induced components share certain characteristics and mechanisms. |
format | Online Article Text |
id | pubmed-3202632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-32026322012-01-02 A dynamic causal model for evoked and induced responses Chen, Chun-Chuan Kiebel, Stefan J. Kilner, James M. Ward, Nick S. Stephan, Klaas E. Wang, Wei- Jen Friston, Karl J. Neuroimage Article Neuronal responses exhibit two stimulus or task-related components: evoked and induced. The functional role of induced responses has been ascribed to ‘top-down’ modulation through backward connections and lateral interactions; as opposed to the bottom-up driving processes that may predominate in evoked components. The implication is that evoked and induced components may reflect different neuronal processes. The conventional way of separating evoked and induced responses assumes that they can be decomposed linearly; in that induced responses are the average of the power minus the power of the average (the evoked component). However, this decomposition may not hold if both components are generated by nonlinear processes. In this work, we propose a Dynamic Causal Model that models evoked and induced responses at the same time. This allows us to explain both components in terms of shared mechanisms (coupling) and changes in coupling that are necessary to explain any induced components. To establish the face validity of our approach, we used Bayesian Model Selection to show that the scheme can disambiguate between models of synthetic data that did and did not contain induced components. We then repeated the analysis using MEG data during a hand grip task to ask whether induced responses in motor control circuits are mediated by ‘top-down’ or backward connections. Our result provides empirical evidence that induced responses are more likely to reflect backward message passing in the brain, while evoked and induced components share certain characteristics and mechanisms. Academic Press 2012-01-02 /pmc/articles/PMC3202632/ /pubmed/21835251 http://dx.doi.org/10.1016/j.neuroimage.2011.07.066 Text en © 2012 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license |
spellingShingle | Article Chen, Chun-Chuan Kiebel, Stefan J. Kilner, James M. Ward, Nick S. Stephan, Klaas E. Wang, Wei- Jen Friston, Karl J. A dynamic causal model for evoked and induced responses |
title | A dynamic causal model for evoked and induced responses |
title_full | A dynamic causal model for evoked and induced responses |
title_fullStr | A dynamic causal model for evoked and induced responses |
title_full_unstemmed | A dynamic causal model for evoked and induced responses |
title_short | A dynamic causal model for evoked and induced responses |
title_sort | dynamic causal model for evoked and induced responses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202632/ https://www.ncbi.nlm.nih.gov/pubmed/21835251 http://dx.doi.org/10.1016/j.neuroimage.2011.07.066 |
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