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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) Schematic overview of the whole-brain parcellations and (B) temporal evolution of LMC membrane potentials and firing rates.(A) The whole brain was parcellated into 70 regions of interest, including 64 cortical and 6 subcortical areas. (B) The left panel shows the time-course of excitatory membrane potentials  with (red) and without (blue) dopamine modulation overlayed with the corresponding time-evolution of  (orange) during one dopamine release cycle. The right panel illustrates the evolution of  with (red) and without (blue) dopamine modulation for fifty simulated cycles (each 10.6 s). Boxes indicate mean firing rate values averaged across a cycle, errorbars show corresponding standard deviations.
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pcbi-1003924-g001: (A) Schematic overview of the whole-brain parcellations and (B) temporal evolution of LMC membrane potentials and firing rates.(A) The whole brain was parcellated into 70 regions of interest, including 64 cortical and 6 subcortical areas. (B) The left panel shows the time-course of excitatory membrane potentials with (red) and without (blue) dopamine modulation overlayed with the corresponding time-evolution of (orange) during one dopamine release cycle. The right panel illustrates the evolution of with (red) and without (blue) dopamine modulation for fifty simulated cycles (each 10.6 s). Boxes indicate mean firing rate values averaged across a cycle, errorbars show corresponding standard deviations.

Mentions: The raw model output was converted to a blood oxygen level-dependent (BOLD) signal and compared to functional brain activity data in healthy volunteers. We used fMRI data of 20 right-handed monolingual English speaking subjects with no history of neurological, psychiatric, voice, or respiratory problems (13 females, 7 males, age years [meanSD]) as reported earlier [20]. Right-handed volunteers were recruited in order to control for brain activity lateralization differences between right- and left-handed people. All scanning sessions were performed on a 3.0 Tesla GE scanner equipped with a quadrature birdcage radio frequency head coil (Milwaukee, WI). Data were acquired under two conditions: 1) a resting state, during which the subjects fixated on a cross, and 2) a task production, during which subjects were asked to produce meaningful, grammatically-correct, short sentences. Whole-brain resting-state (rs-fMRI) images were acquired using gradient-weighted echo planar imaging (EPI) (150 contiguous volumes with TR 2 s, TE 30 ms, FA 90 degrees, 33 sagittal slices, slice thickness 4 mm, matrix 64×64 mm, FOV 240 mm, in-plane resolution 3.75 mm, duration 5 min). To assure the resting condition, these images were acquired before the task-production fMRI within the same scanning session. Physiological recordings were carried out using a respiratory belt to measure respiration volume and a pulse oximeter to monitor heart rhythm and were sampled at 50 Hz with the recording onset triggered by the scanner's selection trigger pulse. For speech-production fMRI, whole brain images were acquired using gradient-weighted EPI pulse sequences (TE 30ms, TR 10.6 s (8.6 s task production, 2 s image acquisition), FA 90 degrees, FOV 240×240 mm, matrix 64×64 mm, in-plane resolution 3.75 mm, 33 sagittal slices, slice thickness 4.0 mm) with BOLD contrast and a sparse-sampling event-related design. The subjects were instructed to produce short meaningful grammatically correct English sentences (e.g., “We are always away”, “Tom is in the army”) after listening to an auditory sample. The auditory stimuli were delivered within a 3.6 s-period and the subjects reproduced the sentences within 5 s, followed by a 2-s image acquisition. A total of 36 trials per task (i.e., sentences, resting fixation) were acquired over the five scanning sessions with the tasks pseudorandomized between sessions and participants. All fMRI data was pre-processed using AFNI software package [41]. For rs-fMRI, the anatomy-based correlation corrections (ANATICOR) model [42] was applied to remove hardware-related noise; respiratory and cardiac signals synchronized with the EPI data were used to remove physiological noise based on the retrospective image correction (RETROICOR) model [43]. The resting-state residual time series were spatially smoothed by a 6-mm Gaussian kernel within the gray matter and normalized to the standard Talairach-Tournoux space. Task-production fMRI. For speech-production fMRI, the first two volumes were discarded, the EPI datasets were registered to the volume collected closest in time to the high-resolution anatomical scan using heptic polynomial interpolation, spatially smoothed with a 6-mm Gaussian filter, normalized to the percent signal change and the standard Talairach-Tournoux space. The task-related responses were analyzed using multiple linear regression with a single regressor for the task convolved with a canonical hemodynamic response function. Based on empirical studies [44], [45], the whole brain was parcellated into 70 regions of interest (ROIs), including 64 cortical and 6 subcortical areas (Fig. 1A).


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

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

(A) Schematic overview of the whole-brain parcellations and (B) temporal evolution of LMC membrane potentials and firing rates.(A) The whole brain was parcellated into 70 regions of interest, including 64 cortical and 6 subcortical areas. (B) The left panel shows the time-course of excitatory membrane potentials  with (red) and without (blue) dopamine modulation overlayed with the corresponding time-evolution of  (orange) during one dopamine release cycle. The right panel illustrates the evolution of  with (red) and without (blue) dopamine modulation for fifty simulated cycles (each 10.6 s). Boxes indicate mean firing rate values averaged across a cycle, errorbars show corresponding standard deviations.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4230744&req=5

pcbi-1003924-g001: (A) Schematic overview of the whole-brain parcellations and (B) temporal evolution of LMC membrane potentials and firing rates.(A) The whole brain was parcellated into 70 regions of interest, including 64 cortical and 6 subcortical areas. (B) The left panel shows the time-course of excitatory membrane potentials with (red) and without (blue) dopamine modulation overlayed with the corresponding time-evolution of (orange) during one dopamine release cycle. The right panel illustrates the evolution of with (red) and without (blue) dopamine modulation for fifty simulated cycles (each 10.6 s). Boxes indicate mean firing rate values averaged across a cycle, errorbars show corresponding standard deviations.
Mentions: The raw model output was converted to a blood oxygen level-dependent (BOLD) signal and compared to functional brain activity data in healthy volunteers. We used fMRI data of 20 right-handed monolingual English speaking subjects with no history of neurological, psychiatric, voice, or respiratory problems (13 females, 7 males, age years [meanSD]) as reported earlier [20]. Right-handed volunteers were recruited in order to control for brain activity lateralization differences between right- and left-handed people. All scanning sessions were performed on a 3.0 Tesla GE scanner equipped with a quadrature birdcage radio frequency head coil (Milwaukee, WI). Data were acquired under two conditions: 1) a resting state, during which the subjects fixated on a cross, and 2) a task production, during which subjects were asked to produce meaningful, grammatically-correct, short sentences. Whole-brain resting-state (rs-fMRI) images were acquired using gradient-weighted echo planar imaging (EPI) (150 contiguous volumes with TR 2 s, TE 30 ms, FA 90 degrees, 33 sagittal slices, slice thickness 4 mm, matrix 64×64 mm, FOV 240 mm, in-plane resolution 3.75 mm, duration 5 min). To assure the resting condition, these images were acquired before the task-production fMRI within the same scanning session. Physiological recordings were carried out using a respiratory belt to measure respiration volume and a pulse oximeter to monitor heart rhythm and were sampled at 50 Hz with the recording onset triggered by the scanner's selection trigger pulse. For speech-production fMRI, whole brain images were acquired using gradient-weighted EPI pulse sequences (TE 30ms, TR 10.6 s (8.6 s task production, 2 s image acquisition), FA 90 degrees, FOV 240×240 mm, matrix 64×64 mm, in-plane resolution 3.75 mm, 33 sagittal slices, slice thickness 4.0 mm) with BOLD contrast and a sparse-sampling event-related design. The subjects were instructed to produce short meaningful grammatically correct English sentences (e.g., “We are always away”, “Tom is in the army”) after listening to an auditory sample. The auditory stimuli were delivered within a 3.6 s-period and the subjects reproduced the sentences within 5 s, followed by a 2-s image acquisition. A total of 36 trials per task (i.e., sentences, resting fixation) were acquired over the five scanning sessions with the tasks pseudorandomized between sessions and participants. All fMRI data was pre-processed using AFNI software package [41]. For rs-fMRI, the anatomy-based correlation corrections (ANATICOR) model [42] was applied to remove hardware-related noise; respiratory and cardiac signals synchronized with the EPI data were used to remove physiological noise based on the retrospective image correction (RETROICOR) model [43]. The resting-state residual time series were spatially smoothed by a 6-mm Gaussian kernel within the gray matter and normalized to the standard Talairach-Tournoux space. Task-production fMRI. For speech-production fMRI, the first two volumes were discarded, the EPI datasets were registered to the volume collected closest in time to the high-resolution anatomical scan using heptic polynomial interpolation, spatially smoothed with a 6-mm Gaussian filter, normalized to the percent signal change and the standard Talairach-Tournoux space. The task-related responses were analyzed using multiple linear regression with a single regressor for the task convolved with a canonical hemodynamic response function. Based on empirical studies [44], [45], the whole brain was parcellated into 70 regions of interest (ROIs), including 64 cortical and 6 subcortical areas (Fig. 1A).

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