State Dependency of Beta Oscillations in the Cortico-Basal-Ganglia Circuit and their Neuromodulation under Phase Locked Inputs

Currently employed strategies for therapeutic brain stimulation take a static approach to determining stimulation parameters. However, it is well understood that brain states fluctuate over time, depending for instance upon differing behavioural or disease states. Here, we characterize the impact of changes in connectivity upon the emergence of rhythmic neural activity in the circuits formed by the cortex, basal-ganglia, and thalamus. Importantly, we show how the efficacy of interaction with these rhythms via phase-specific stimulation is highly dependent upon the current network state. We take a computational approach to do this, modelling the population activity of the cortico-basal ganglia-thalamic circuit and fitting model parameters to match the spectral features of empirical data obtained from a 6-OHDA lesioned rat model of Parkinson’s disease. Using this fitted model, we then dissect the role of the circuit’s multiple loops in the maintenance of subcortical beta rhythms and their synchronization. We show that a competition of cortical and striato-pallidal inputs to the subthalamic nucleus, a main input hub of the basal-ganglia, determines the frequency, amplitude, and timing of beta band (14-30 Hz) activity. In addition, we demonstrate how the efficacy of cortical inputs in modulating ongoing subthalamic beta activity is dependent upon their relative phase alignment- with their precise effects in turn determined by the connectivity state of the network. These results inform our understanding of: (a) how alterations in circuit connectivity can lead to the emergence of pathologically amplified rhythms; (b) how precisely timed phasic stimulation can be leveraged to modulate aberrant brain activity; and (c) how effective stimulation parameters depend on the “connectivity state” of the circuit; highlighting the importance of incorporating an estimation of brain state in the determination of optimum stimulation parameters.