Spike-timing dependent plasticity and connectivity in primate sensorimotor cortex

Thesis (Ph.D.)--University of Washington, 2016-06

Specific connectivity between populations of neurons gives rise to network function. The plasticity of these connections allows for network learning and adaptation. While much work has explored plasticity and underlying connectivity, there is still much to learn about why particular connections are susceptible to manipulation. Hebbian plasticity has been shown in a variety of neural circuits, yet there are subtle differences in the mechanisms driving these effects at each synapse. Similarly, functional connectivity has been described utilizing varying methods. Ultimately, these bodies of work tile a broad spatiotemporal view of cortical function. Here, we explore plasticity between sensorimotor populations and the underlying connections which serve targeted changes. The variability we observed in our results further highlights that one size does not fit all, and perhaps only by looking at the collective may we begin to understand the complexities of cortical processing. I. Paired-stimulation for spike-timing dependent plasticity in primate sensorimotor cortex Classic studies in vitro have described spike-timing dependent plasticity (STDP) at a synapse: the connection from neuron A to neuron B is strengthened (or weakened) when A fires before (or after) B within an optimal time window. Accordingly, more recent in vivo works have demonstrated behavioral effects consistent with an STDP mechanism; however, many relied on invasive single-unit recordings. The ability to modify cortical connections becomes useful in the context of injury when connectivity, and associated behavior, is compromised. To avoid the need for long-term, stable isolation of single units, one could control timed activation of two cortical sites with paired, electrical stimulation. We tested the hypothesis that STDP could be induced via prolonged paired-stimulation as quantified by cortical evoked potentials (EPs) in sensorimotor cortex of awake, behaving monkeys. Paired-stimulation between two interconnected sites produced robust effects in EPs consistent with STDP; however, only at a subset of tested pairs (2/15). This protocol otherwise produced increases in global network excitability or depression of the conditioned pair. Taken together, these results suggest that paired-stimulation in vivo is a viable method to induce STDP between cortical populations, but that factors beyond activation timing must be kept in mind to produce a site-independent effect. II. Intrinsic functional connectivity of neural populations in forelimb sensorimotor cortex The structure of neocortex is defined by its anatomical and functional connections, from which processing and cognition arise. Functional connectivity exists and may be investigated along a wide spatiotemporal range utilizing a variety of electrophysiological techniques and analyses. Single- and multi-unit recordings can show connectivity of local micro-circuits whereas electrocorticography (ECoG) or functional imaging highlights macro-scale connections across the whole brain. Further, recordings of action potentials compared to local field potentials (LFPs) in ECoG are based in fundamentally different mechanisms generating neural activity and connectivity. We explored meso-scale connectivity in hand area of primate sensorimotor cortex via EPs and band-limited coherence in spontaneous LFP between cortical sites. The strength of both EPs and coherence showed an inverse relationship with inter-site distance as well as regionality between primary motor (M1) and somatosensory (S1) cortex. Despite these similarities, EP and coherence connectivity maps were not well correlated likely due to different underlying mechanisms driving the two signals. Taken together, these results show connectivity structure at a meso-scale, population level similar to that at other scales. Further exploration utilizing different recording techniques or functional connectivity metrics may elucidate additional network structure.