Probing and restoring disrupted thalamocortical interactions during Parkinson's disease and essential tremor

Parkinson’s disease (PD) and Essential Tremor (ET) are two movement disorders where impaired motor function can lead to severe disability. In conjunction with motor impairments, both disorders display augmented frequency-specific activity patterns across brain networks that converge at the thalamocortical (TC) level. In treating motor symptoms, the available therapies for PD and ET modulate these pathologically increased activity patterns. However, therapeutic interventions can also lead to the disruption of physiological brain activity and cause undesirable secondary effects. A major challenge delaying the development of selective neuromodulatory therapies is our limited understanding of the impact of neural activity on function and dysfunction. This thesis contributes to addressing this knowledge gap by probing the TC network dynamics that underlie aberrant synchrony in PD, and by exploring ways of restoring motor function in ET through temporally-specific perturbations of tremor-related TC interactions. By using a combination of tools that span from computational modelling, signal analysis and closed-loop stimulation algorithms we have found that: 1) transient events of high-power beta synchrony in PD are promoted by synaptic dynamics across the TC circuit; 2) the temporal patterning of beta activity in PD is mediated by unit and ensemble dynamic changes in TC temporal alignment; 3) phase-specific stimulation (Cagnan et al., 2017a) can bring kinetic and postural tremor control in ET patients with stable tremor properties, and 4) closed-loop algorithms using online estimates of phase and amplitude to trigger median nerve stimulation might be more effective in yielding tremor suppression in ET patients. Overall, the findings of this thesis contribute to the fields of neuroscience and neurotechnology by deepening our understanding on the dynamics of pathological neural synchrony and shedding light on the key features underlying the efficacy of selective neuromodulatory therapies