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

Fürtinger 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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(A) Empirical and simulated functional networks in the resting state and during speech production and (B) nodal strength for experimental (left column) and simulated (right column) functional networks in resting state (gray) and during speech production (red).(A) 3D visualizations of data- and model-based NMI networks (top and bottom rows, respectively) during rest (left column) and speech production (right column). Edge colors represent NMI coefficient values and nodal color illustrates strength (normalized to the interval ). (B) Nodal strength of data- and model-based NMI networks. The top row shows the nodal strength per node, the bottom row illustrates the distribution of si-values. The 3D networks were visualized with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/). Abbreviations: MFG  =  middle frontal gyrus, Cu  =  cuneus, FP  =  frontal pole, FG  =  fusiform gyrus, IPC/SPC  =  inferior/superior parietal cortex, LMC  =  laryngeal motor cortex, OC  =  occipital cortex, PreCG  =  precentral gyrus, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, PostCG  =  postcentral gyrus, STC  =  superior temporal cortex, mFG  =  medial frontal gyrus, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus.
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pcbi-1003924-g003: (A) Empirical and simulated functional networks in the resting state and during speech production and (B) nodal strength for experimental (left column) and simulated (right column) functional networks in resting state (gray) and during speech production (red).(A) 3D visualizations of data- and model-based NMI networks (top and bottom rows, respectively) during rest (left column) and speech production (right column). Edge colors represent NMI coefficient values and nodal color illustrates strength (normalized to the interval ). (B) Nodal strength of data- and model-based NMI networks. The top row shows the nodal strength per node, the bottom row illustrates the distribution of si-values. The 3D networks were visualized with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/). Abbreviations: MFG  =  middle frontal gyrus, Cu  =  cuneus, FP  =  frontal pole, FG  =  fusiform gyrus, IPC/SPC  =  inferior/superior parietal cortex, LMC  =  laryngeal motor cortex, OC  =  occipital cortex, PreCG  =  precentral gyrus, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, PostCG  =  postcentral gyrus, STC  =  superior temporal cortex, mFG  =  medial frontal gyrus, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus.

Mentions: Figure 3 shows nodal strengths of the networks. We found a significant increase in strength when comparing resting state to task production in both data and model (both ). While the simulated networks showed a higher average strength than the data in the resting state (model: ; data: ), the difference was less pronounced during task production (model: , data: ). Examining the distribution of nodal strengths in the data, we observed a marked right-shift of the distribution during speech as compared to the resting state, clearly reflecting overall elevated strength in the speech production network. This right-shift was seen in the simulated networks too, although to a lesser extent. The data showed a narrower strength distribution than the model in the resting state, reflecting higher variability of NMI coefficients for the simulated BOLD signal without dopamine modulation (compare also the corresponding NMI matrices shown in Fig. 2C,D). Nodal degrees of the networks did not reveal any particular structure. All networks (model and data) were maximally connected, i.e., all nodes had maximum degree , which means all pairwise NMI coefficients were non-zero. Note that, unlike Pearson's correlation coefficient (PCC), the mutual information does not only reflect linear correlation, but also dependencies in higher moments [60]. While a zero PCC only indicates that there is no linear relationship between the observed quantities, two time-series have to be approximately statistically independent for the NMI to be zero (compare, e.g., [61]). In other words, two signals have to show a stronger kind of independence to yield an NMI coefficient of zero. Given that fMRI-based functional networks are largely composed of high-strength nodes, are fully-connected, and may be indistinguishable from random networks if unthresholded [62], it was not surprising to see overall positive NMI coefficients for the data. It was also expected that simulated BOLD signals generated by a system of coupled but structurally identical equations show large NMI coefficients.


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

Fürtinger S, Zinn JC, Simonyan K - PLoS Comput. Biol. (2014)

(A) Empirical and simulated functional networks in the resting state and during speech production and (B) nodal strength for experimental (left column) and simulated (right column) functional networks in resting state (gray) and during speech production (red).(A) 3D visualizations of data- and model-based NMI networks (top and bottom rows, respectively) during rest (left column) and speech production (right column). Edge colors represent NMI coefficient values and nodal color illustrates strength (normalized to the interval ). (B) Nodal strength of data- and model-based NMI networks. The top row shows the nodal strength per node, the bottom row illustrates the distribution of si-values. The 3D networks were visualized with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/). Abbreviations: MFG  =  middle frontal gyrus, Cu  =  cuneus, FP  =  frontal pole, FG  =  fusiform gyrus, IPC/SPC  =  inferior/superior parietal cortex, LMC  =  laryngeal motor cortex, OC  =  occipital cortex, PreCG  =  precentral gyrus, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, PostCG  =  postcentral gyrus, STC  =  superior temporal cortex, mFG  =  medial frontal gyrus, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus.
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pcbi-1003924-g003: (A) Empirical and simulated functional networks in the resting state and during speech production and (B) nodal strength for experimental (left column) and simulated (right column) functional networks in resting state (gray) and during speech production (red).(A) 3D visualizations of data- and model-based NMI networks (top and bottom rows, respectively) during rest (left column) and speech production (right column). Edge colors represent NMI coefficient values and nodal color illustrates strength (normalized to the interval ). (B) Nodal strength of data- and model-based NMI networks. The top row shows the nodal strength per node, the bottom row illustrates the distribution of si-values. The 3D networks were visualized with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/). Abbreviations: MFG  =  middle frontal gyrus, Cu  =  cuneus, FP  =  frontal pole, FG  =  fusiform gyrus, IPC/SPC  =  inferior/superior parietal cortex, LMC  =  laryngeal motor cortex, OC  =  occipital cortex, PreCG  =  precentral gyrus, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, PostCG  =  postcentral gyrus, STC  =  superior temporal cortex, mFG  =  medial frontal gyrus, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus.
Mentions: Figure 3 shows nodal strengths of the networks. We found a significant increase in strength when comparing resting state to task production in both data and model (both ). While the simulated networks showed a higher average strength than the data in the resting state (model: ; data: ), the difference was less pronounced during task production (model: , data: ). Examining the distribution of nodal strengths in the data, we observed a marked right-shift of the distribution during speech as compared to the resting state, clearly reflecting overall elevated strength in the speech production network. This right-shift was seen in the simulated networks too, although to a lesser extent. The data showed a narrower strength distribution than the model in the resting state, reflecting higher variability of NMI coefficients for the simulated BOLD signal without dopamine modulation (compare also the corresponding NMI matrices shown in Fig. 2C,D). Nodal degrees of the networks did not reveal any particular structure. All networks (model and data) were maximally connected, i.e., all nodes had maximum degree , which means all pairwise NMI coefficients were non-zero. Note that, unlike Pearson's correlation coefficient (PCC), the mutual information does not only reflect linear correlation, but also dependencies in higher moments [60]. While a zero PCC only indicates that there is no linear relationship between the observed quantities, two time-series have to be approximately statistically independent for the NMI to be zero (compare, e.g., [61]). In other words, two signals have to show a stronger kind of independence to yield an NMI coefficient of zero. Given that fMRI-based functional networks are largely composed of high-strength nodes, are fully-connected, and may be indistinguishable from random networks if unthresholded [62], it was not surprising to see overall positive NMI coefficients for the data. It was also expected that simulated BOLD signals generated by a system of coupled but structurally identical equations show large NMI coefficients.

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