Dopamine drives left-hemispheric lateralization of neural networks during human speech.

Although the concept of left-hemispheric lateralization of neural processes during speech production has been known since the times of Broca, its physiological underpinnings still remain elusive. We sought to assess the modulatory influences of a major neurotransmitter, dopamine, on hemispheric lateralization during real-life speaking using a multimodal analysis of functional MRI, intracranial EEG recordings, and large-scale neural population simulations based on diffusion-weighted MRI. We demonstrate that speech-induced phasic dopamine release into the dorsal striatum and speech motor cortex exerts direct modulation of neuronal activity in these regions and drives left-hemispheric lateralization of speech production network. Dopamine-induced lateralization of functional activity and networks during speaking is not dependent on lateralization of structural nigro-striatal and nigro-motocortical pathways. Our findings provide the first mechanistic explanation for left-hemispheric lateralization of human speech that is due to left-lateralized dopaminergic modulation of brain activity and functional networks.

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

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.