Deep Brain Stimulation restores information processing in parkinsonian cortical networks

Abstract

AbstractParkinson’s disease (PD) is a neurodegenerative disorder associated with alterations of neural activity and information processing primarily in the basal ganglia and cerebral cortex. Deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) is the most effective therapy when patients experience levodopa-induced motor complications. A growing body of evidence points towards a cortical effect of STN-DBS, restoring key electrophysiological markers, such as excessive beta band oscillations, commonly observed in PD. However, the mechanisms of STN-DBS remain elusive. Here, we aim to better characterize the cortical substrates underlying STN-DBS-induced improvement in motor symptoms. We recorded electroencephalograms (EEG) from PD patients and found that, although apparent EEG features were not different with or without therapy, EEG signals could more accurately predict limb movements under STN-DBS. To understand the origins of this enhanced information transmission under STN-DBS in the human EEG data, we investigated the information capacity and dynamics of a variety of computational models of cortical networks. The extent of improvement in decoding accuracy of complex naturalistic inputs under STN-DBS depended on the synaptic parameters of the network as well as its excitability and synchronization levels. Additionally, decoding accuracy could be optimized by adjusting STN-DBS parameters. Altogether, this work draws a comprehensive link between known alterations in cortical activity and the degradation of information processing capacity, as well as its restoration under DBS. These results also offer new perspectives for optimizing STN-DBS parameters based on clinically accessible measures of cortical information processing capacity.Significance statementParkinson’s disease, a neurodegenerative disorder associated with a variety of motor symptoms, is due to the progressive degeneration of dopaminergic neurons. Neuronal networks in turn display abnormal activity associated with high excitability and abnormal synchronization. Treatments based on the electrical stimulations of deep brain nuclei (DBS) provide major symptomatic improvement, but their mechanisms of action remain unknown. Here, using mathematical models of the corticalcircuits involved, we show that DBS restores neuronal ability to encode and transmit information. We further show that movements from human patients can be better predicted from brain signals under treatment. These new theory and metrics open the way to personalized and adaptive DBS allowing to personalize stimulation patterns to each patient.