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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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Simulated and empirical BOLD signal during (A) rest and (B) speech and NMI matrices of (C) data and (D) model in resting state and during speech production.The colored lines show time courses of simulated BOLD signals during resting state (A) and for dopamine modulation (B) for regions of the brain associated with speech production. Experimental BOLD time courses are shown in gray. The labels ‘left’ and ‘right’ indicate left and right hemispheres respectively. Pairwise interactions within the signals were quantified by computing NMI coefficients for each pair of regional time-series corresponding to the simulated and real BOLD time-courses. This gave rise to four  NMI-matrices (pairwise interactions of data (C) and model (D) in the resting state and during speech production). Because a normalized variant of the mutual information was employed, all matrix entries were bounded by zero and one. The parcellated brain regions used for the construction of matrices are provided in top (C) for both left and right hemispheres; the magnified inset shows the brain regions per hemisphere. Abbreviations: ACC/ICC/MCC/PCC  =  anterior/isthmus/middle/posterior cingulate cortex, Cu/PCu  =  cuneus/precuneus, ETC  =  entorhinal cortex, FG  =  fusiform gyrus, FP  =  frontal pole, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, IPC/SPC  =  inferior/superior parietal cortex, ITC/STC  =  inferior/superior temporacl cortex, LG  =  lingual gyrus, LMC  =  laryngeal motor cortex, LOFC/MOFC  =  lateral/medial orbitofrontal cortex, MFG  =  middle frontal gyrus, mFG  =  medial frontal gyrus, MTG  =  middle temporal gyrus, OC  =  occipital cortex, PCAC  =  pericalcerine cortex, PHip  =  parahippocampal cortex, PreCG/PostCG  =  pre/postcentral gyrus, Put  =  putamen, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus, SNc  =  substantia nigra pars compacta, TP  =  temporal pole, TTC  =  transverse temporal cortex, Th  =  thalamus.
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pcbi-1003924-g002: Simulated and empirical BOLD signal during (A) rest and (B) speech and NMI matrices of (C) data and (D) model in resting state and during speech production.The colored lines show time courses of simulated BOLD signals during resting state (A) and for dopamine modulation (B) for regions of the brain associated with speech production. Experimental BOLD time courses are shown in gray. The labels ‘left’ and ‘right’ indicate left and right hemispheres respectively. Pairwise interactions within the signals were quantified by computing NMI coefficients for each pair of regional time-series corresponding to the simulated and real BOLD time-courses. This gave rise to four NMI-matrices (pairwise interactions of data (C) and model (D) in the resting state and during speech production). Because a normalized variant of the mutual information was employed, all matrix entries were bounded by zero and one. The parcellated brain regions used for the construction of matrices are provided in top (C) for both left and right hemispheres; the magnified inset shows the brain regions per hemisphere. Abbreviations: ACC/ICC/MCC/PCC  =  anterior/isthmus/middle/posterior cingulate cortex, Cu/PCu  =  cuneus/precuneus, ETC  =  entorhinal cortex, FG  =  fusiform gyrus, FP  =  frontal pole, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, IPC/SPC  =  inferior/superior parietal cortex, ITC/STC  =  inferior/superior temporacl cortex, LG  =  lingual gyrus, LMC  =  laryngeal motor cortex, LOFC/MOFC  =  lateral/medial orbitofrontal cortex, MFG  =  middle frontal gyrus, mFG  =  medial frontal gyrus, MTG  =  middle temporal gyrus, OC  =  occipital cortex, PCAC  =  pericalcerine cortex, PHip  =  parahippocampal cortex, PreCG/PostCG  =  pre/postcentral gyrus, Put  =  putamen, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus, SNc  =  substantia nigra pars compacta, TP  =  temporal pole, TTC  =  transverse temporal cortex, Th  =  thalamus.

Mentions: The raw model output was converted to BOLD signals as detailed above. Fig. 2 shows simulated and real BOLD signals for a selection of speech-related ROIs (Fig. 2A,B). Simulated BOLD signals with and without dopamine modulation were compared to empirical resting-state and speech production fMRI data, respectively, in order to assess the global effects of dopamine modulation on the entire simulated neural population. To do so, we employed graph theory analysis to quantify variations in functional connectivity between the resting state and speech production. Thus, we first had to quantify statistical similarity between two time-series. We chose the normalized mutual information (NMI) [47] as statistical metric. Hence, for two random variables and , let and denote their respective Shannon entropies [48] and define


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

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

Simulated and empirical BOLD signal during (A) rest and (B) speech and NMI matrices of (C) data and (D) model in resting state and during speech production.The colored lines show time courses of simulated BOLD signals during resting state (A) and for dopamine modulation (B) for regions of the brain associated with speech production. Experimental BOLD time courses are shown in gray. The labels ‘left’ and ‘right’ indicate left and right hemispheres respectively. Pairwise interactions within the signals were quantified by computing NMI coefficients for each pair of regional time-series corresponding to the simulated and real BOLD time-courses. This gave rise to four  NMI-matrices (pairwise interactions of data (C) and model (D) in the resting state and during speech production). Because a normalized variant of the mutual information was employed, all matrix entries were bounded by zero and one. The parcellated brain regions used for the construction of matrices are provided in top (C) for both left and right hemispheres; the magnified inset shows the brain regions per hemisphere. Abbreviations: ACC/ICC/MCC/PCC  =  anterior/isthmus/middle/posterior cingulate cortex, Cu/PCu  =  cuneus/precuneus, ETC  =  entorhinal cortex, FG  =  fusiform gyrus, FP  =  frontal pole, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, IPC/SPC  =  inferior/superior parietal cortex, ITC/STC  =  inferior/superior temporacl cortex, LG  =  lingual gyrus, LMC  =  laryngeal motor cortex, LOFC/MOFC  =  lateral/medial orbitofrontal cortex, MFG  =  middle frontal gyrus, mFG  =  medial frontal gyrus, MTG  =  middle temporal gyrus, OC  =  occipital cortex, PCAC  =  pericalcerine cortex, PHip  =  parahippocampal cortex, PreCG/PostCG  =  pre/postcentral gyrus, Put  =  putamen, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus, SNc  =  substantia nigra pars compacta, TP  =  temporal pole, TTC  =  transverse temporal cortex, Th  =  thalamus.
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pcbi-1003924-g002: Simulated and empirical BOLD signal during (A) rest and (B) speech and NMI matrices of (C) data and (D) model in resting state and during speech production.The colored lines show time courses of simulated BOLD signals during resting state (A) and for dopamine modulation (B) for regions of the brain associated with speech production. Experimental BOLD time courses are shown in gray. The labels ‘left’ and ‘right’ indicate left and right hemispheres respectively. Pairwise interactions within the signals were quantified by computing NMI coefficients for each pair of regional time-series corresponding to the simulated and real BOLD time-courses. This gave rise to four NMI-matrices (pairwise interactions of data (C) and model (D) in the resting state and during speech production). Because a normalized variant of the mutual information was employed, all matrix entries were bounded by zero and one. The parcellated brain regions used for the construction of matrices are provided in top (C) for both left and right hemispheres; the magnified inset shows the brain regions per hemisphere. Abbreviations: ACC/ICC/MCC/PCC  =  anterior/isthmus/middle/posterior cingulate cortex, Cu/PCu  =  cuneus/precuneus, ETC  =  entorhinal cortex, FG  =  fusiform gyrus, FP  =  frontal pole, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, IPC/SPC  =  inferior/superior parietal cortex, ITC/STC  =  inferior/superior temporacl cortex, LG  =  lingual gyrus, LMC  =  laryngeal motor cortex, LOFC/MOFC  =  lateral/medial orbitofrontal cortex, MFG  =  middle frontal gyrus, mFG  =  medial frontal gyrus, MTG  =  middle temporal gyrus, OC  =  occipital cortex, PCAC  =  pericalcerine cortex, PHip  =  parahippocampal cortex, PreCG/PostCG  =  pre/postcentral gyrus, Put  =  putamen, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus, SNc  =  substantia nigra pars compacta, TP  =  temporal pole, TTC  =  transverse temporal cortex, Th  =  thalamus.
Mentions: The raw model output was converted to BOLD signals as detailed above. Fig. 2 shows simulated and real BOLD signals for a selection of speech-related ROIs (Fig. 2A,B). Simulated BOLD signals with and without dopamine modulation were compared to empirical resting-state and speech production fMRI data, respectively, in order to assess the global effects of dopamine modulation on the entire simulated neural population. To do so, we employed graph theory analysis to quantify variations in functional connectivity between the resting state and speech production. Thus, we first had to quantify statistical similarity between two time-series. We chose the normalized mutual information (NMI) [47] as statistical metric. Hence, for two random variables and , let and denote their respective Shannon entropies [48] and define

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