Optogenetic dissection of motor cortex dynamics and pathways
Cortical computation develops as interplay between neuronal dynamics and structural connectivity which is used for simultaneous communications with several targets. Within a densely interconnected network, selectivity can be achieved only if neuronal inputs and outputs are functionally segmented and if only one segment is selected for a given time and neural population. We focus here on this process in the primary motor cortex (M1) which projects to a variety of brain structures involved in motor generation and suppression as well as somatosensory perception. We propose to investigate what kind of information is sent to two of M1’s main target brain areas – striatum and primary somatosensory cortex (S1) by separate or partially overlapping neural subpopulations.
To dissect the two pathways we will apply new optogenetic projection and stimulation strategies and combine them with controlled behavior and electrophysiological recordings conducted with advanced optoelectronic probes. Our goal is to neurophysiologically characterize the two populations in a specially designed Go/NoGo task with tactile component and understand their functional relevance for motor behavior. To define the most effective optical stimulation frequencies, we will capitalize on consistently reported oscillations in the sensorimotor system as well as our own recorded data which we will screen for naturally occurring oscillations in motor cortex and its targets.
In particular, we will investigate how manipulating ongoing beta and gamma band oscillations impact behavior. Both frequency bands have been assigned importance in complementary tasks: beta band activity has been mainly associated with the suppression of movements, with postural maintenance, and with sensorimotor integration and planning; elevated gamma band activity has been reported often during movement initiation and has been attributed a role in attention. In a double dissociation paradigm we aim to test whether we can manipulate the two neural populations differentially with the two frequency bands. We believe that it is important to interfere with the neural populations at their preferred frequency.
We hypothesize that the best suited resonance frequencies differ between the two neuron populations and might change across trial phases. For causally defining the optimal frequencies for a specific task period and neural population, we will make use of real-time feedback by measuring ongoing oscillatory patterns and enhance or phase shift the synchronized activity. Apart from the impact on basic science, finding out about the neural networks of sensorimotor integration and movement control as well as the role of synchronization in these contexts may lead to a better understanding of motor disorders, e.g. Parkinson’s disease.