Internal KCN: Inferring Dynamics of Short-term Synaptic Plasticity During Deep Brain Stimulation from Recorded Postsynaptic Responses
Abstract: Parkinson’s disease (PD) is a debilitating neurological disorder that affects more than 6.2 million people globally. Although there is no proven disease-modifying treatment, several methods, including Deep Brain Stimulation (DBS), have been developed to control PD’s symptoms. In DBS, an electrode delivers electrical currents to subcortical regions of the brain to modulate neural activities. Despite successful outcomes of DBS in ameliorating symptoms of PD, its underlying mechanism remained unknown. Recent experimental studies showed that DBS induces short-term synaptic plasticity (STP) in the basal ganglia and thalamic neurons, the main targets of DBS for PD. To fully decipher the role of STP in the underlying mechanism of DBS, it is required to infer the dynamics of DBS-induced STP. Since it is impossible to simultaneously record pre- and postsynaptic spikes of a stimulated neuron in the human brain, we proposed a novel computational framework and fitting algorithms, based on the Tsodyks-Markram model of STP and linear-nonlinear Poisson model of neuronal firing to infer the STP dynamics from neuronal spike firing evoked by DBS-induced trans-synaptic inputs.