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A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

F├╝rtinger S, Zinn JC, Simonyan K - PLoS Comput. Biol. (2014)

Bottom Line: We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model.These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control.Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of the proposed methodology building upon the current model.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of the proposed methodology building upon the current model.

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Non-normalized and normalized segregation and integration metrics for experimental and simulated functional networks in resting state (gray) and during speech production (red).Distributions of (A) non-normalized clustering coefficient, (B) non-normalized local efficiency, (C) normalized clustering coefficient, and (D) normalized local efficiency in the data- and model-based NMI networks.
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pcbi-1003924-g004: Non-normalized and normalized segregation and integration metrics for experimental and simulated functional networks in resting state (gray) and during speech production (red).Distributions of (A) non-normalized clustering coefficient, (B) non-normalized local efficiency, (C) normalized clustering coefficient, and (D) normalized local efficiency in the data- and model-based NMI networks.

Mentions: As mentioned above, the local clustering coefficient quantifies the average weight of connected neighbors of the node . The networks considered here had maximal connection density, i.e., each node was connected to all other nodes in the graph. In this case, is not influenced by the presence or absence of edges and is thus given by the geometric mean of edge weights adjacent to . Hence, the local clustering coefficient is solely dependent on the nodal strength. Thus, (Fig. 4A) exhibited qualitatively the same characteristics as (compare to Fig. 3B). In both data and model, we observed a significant increase in clustering during task production as compared to rest () (data: rest: 0.560.01, speech: 0.810.01; model: rest: 0.630.01, speech: 0.830.01) Interestingly, compared to the data, the model showed on average higher values of in the resting-state simulation, while the dopamine-modulated run exhibited very similar clustering characteristics. To assess differences in network topologies in contrast to random graphs, we compared to the corresponding random network values and computed the normalized clustering coefficient (Fig. 4C). We found to be greater than one in the dopamine modulated simulation and the empirical speech production networks, while both data and model failed to show values larger than one during rest (data: rest: 0.810.01, speech: 1.160.01; model: rest: 0.910.02, speech: 1.190.01). This indicated an overall elevated segregation of simulated as well as empirical speech production networks in relation to random networks. Furthermore, for both simulated and empirical networks the difference in values of between rest and task was found to be significant (). Interestingly, with and without dopamine modulation the model showed a very similar variability in both and compared to the empirical networks. However, while the data exhibited similar peak frequencies during rest and speech, a decrease in the most prevalent values of both and was found in the simulated networks (Fig. 4A,C). This was indicative of a slightly lower variability of and in the dopamine modulated simulation.


A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

F├╝rtinger S, Zinn JC, Simonyan K - PLoS Comput. Biol. (2014)

Non-normalized and normalized segregation and integration metrics for experimental and simulated functional networks in resting state (gray) and during speech production (red).Distributions of (A) non-normalized clustering coefficient, (B) non-normalized local efficiency, (C) normalized clustering coefficient, and (D) normalized local efficiency in the data- and model-based NMI networks.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4230744&req=5

pcbi-1003924-g004: Non-normalized and normalized segregation and integration metrics for experimental and simulated functional networks in resting state (gray) and during speech production (red).Distributions of (A) non-normalized clustering coefficient, (B) non-normalized local efficiency, (C) normalized clustering coefficient, and (D) normalized local efficiency in the data- and model-based NMI networks.
Mentions: As mentioned above, the local clustering coefficient quantifies the average weight of connected neighbors of the node . The networks considered here had maximal connection density, i.e., each node was connected to all other nodes in the graph. In this case, is not influenced by the presence or absence of edges and is thus given by the geometric mean of edge weights adjacent to . Hence, the local clustering coefficient is solely dependent on the nodal strength. Thus, (Fig. 4A) exhibited qualitatively the same characteristics as (compare to Fig. 3B). In both data and model, we observed a significant increase in clustering during task production as compared to rest () (data: rest: 0.560.01, speech: 0.810.01; model: rest: 0.630.01, speech: 0.830.01) Interestingly, compared to the data, the model showed on average higher values of in the resting-state simulation, while the dopamine-modulated run exhibited very similar clustering characteristics. To assess differences in network topologies in contrast to random graphs, we compared to the corresponding random network values and computed the normalized clustering coefficient (Fig. 4C). We found to be greater than one in the dopamine modulated simulation and the empirical speech production networks, while both data and model failed to show values larger than one during rest (data: rest: 0.810.01, speech: 1.160.01; model: rest: 0.910.02, speech: 1.190.01). This indicated an overall elevated segregation of simulated as well as empirical speech production networks in relation to random networks. Furthermore, for both simulated and empirical networks the difference in values of between rest and task was found to be significant (). Interestingly, with and without dopamine modulation the model showed a very similar variability in both and compared to the empirical networks. However, while the data exhibited similar peak frequencies during rest and speech, a decrease in the most prevalent values of both and was found in the simulated networks (Fig. 4A,C). This was indicative of a slightly lower variability of and in the dopamine modulated simulation.

Bottom Line: We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model.These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control.Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of the proposed methodology building upon the current model.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

ABSTRACT
Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of the proposed methodology building upon the current model.

Show MeSH
Related in: MedlinePlus