Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials

Abstract

Electrophysiological biomarkers reflecting the pathological activities in the basal ganglia are essential to gain an etiological understanding of Parkinsons disease (PD) and develop a method of diagnosing and treating the disease. Previous studies that explored electrophysiological biomarkers in PD have focused mainly on oscillatory or periodic activities such as beta and gamma oscillations. Emerging evidence has suggested that the nonoscillatory, aperiodic component reflects the firing rate and synaptic current changes corresponding to cognitive and pathological states. Nevertheless, it has never been thoroughly examined whether the aperiodic component can be used as a biomarker that reflects pathological activities in the basal ganglia in PD. In this study, we examined the parameters of the aperiodic component and tested its practicality as an electrophysiological biomarker of pathological activity in PD. We found that a set of aperiodic parameters, aperiodic offset and exponent, were significantly decreased by the nigrostriatal lesion. To further prove the usefulness of the parameters as biomarkers, acute levodopa treatment reverted the aperiodic offset. We then compared the aperiodic parameters with a previously established periodic biomarker of PD, beta frequency oscillation. We found a significantly low negative correlation with beta power. We showed that the performance of the machine learning-based prediction of pathological activities in the basal ganglia can be improved by using the lowly correlated parameters, beta power and aperiodic component. We suggest that the aperiodic component will provide a more sensitive measurement to early diagnosis PD and have the potential to use as the feedback parameter for the adaptive deep brain stimulation.