P240 Deep brain stimulation restores information processing in parkinsonian cortical networks
Charlotte Piette1,2, Sophie Ng Wing Tin3,4, Astrid De Liège5, Coralie Bloch-Queyrat6, Bertrand Degos1,5#, Laurent Venance1#, Jonathan Touboul2#
1Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, PSL University, 75005 Paris, France
2Department of Mathematics and Volen National Center for Complex Systems, Brandeis University, MA Waltham, USA
3Service de Physiologie, Explorations Fonctionnelles et Médecine du Sport,Assistance Publique-Hôpitaux de Paris(AP-HP), Avicenne University Hospital, Sorbonne Paris Nord University, 93009 Bobigny, France
4Inserm UMR 1272,Sorbonne Paris Nord University, 93009 Bobigny, France
5Department of Neurology, Avicenne University Hospital, Sorbonne Paris Nord University, 93009 Bobigny, France
6Department of Clinical Research, Avicenne University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), 93009, Bobigny, France
Corresponding authors: jtouboul@brandeis.edu ; charlotte_piette@hms.harvard.edu
Introduction
Parkinson’s disease (PD) is characterized by alterations of neural activity and information processing in the basal ganglia and cerebral cortex, including changes in excitability (Lindenbach and Bishop, 2013; Valverde et al., 2020) and abnormal synchronization (Goldberg et al., 2002) in the motor cortex of PD patients and PD animal models. Deep Brain Stimulation (DBS) provides an effective symptomatic treatment in PD but its mechanisms of action, enabling the restoration of efficient information transmission through cortico-basal ganglia circuits, remain elusive. Here, we developed a computational framework to test DBS impact on cortical network dynamics and information encoding depending on the network’s initial levels of excitability and synchronization.
Methods
We extended a computational model initially developed in our previous work (Valverde et al., 2020) to analyze the responses of a spectrum of cortical pathological networks, characterized by their level of activity and synchronization, to various input patterns.This way, we could compare their capacity of encoding and transmitting information, before and after DBS stimulation.To further test the hypothesis that DBS positively impacts cortical information transmission in the clinics, we investigated whether PD treatment could improve the ability to predict movement from electroencephalograms collected in human parkinsonian patients(collected in the Neurology Department of Avicenne Hospital, Bobigny).
Results
We observed thatDBS efficiently reduces the firing rate in a large spectrum of parkinsonian networks, and in doing so can decrease abnormal synchronization levels. In addition, DBS-mediated improvements of information processing were most exacerbated in synchronized regimes. Interestingly, DBS efficiency was modulated by the configuration of the cortical circuit such that optimal DBS parameters varied depending on the pathological cortical activity and connectivity profile. We further validated our hypothesis in the clinics and found that the accuracy of decoding movement identity from cortical dynamics was worse when DBS was turned off and correlated with the extent of drug treatment.
Discussion
Overall, this work highlights how DBS improves information encoding by resetting cortical networks into highly responsive states. Cortical networks therefore stand as a privileged target for alternative therapies and adaptive DBS. Our final experiments on human electrophysiology open newperspectives for adaptively tuning DBS parameters, based on clinically accessible measures of cortical information processing capacity.
Acknowledgements
We thank J.E. Rubin, P. Miller, the members of the LV and JT laboratory for their helpful suggestions and critical comments. We thank theService de Physiologie, Explorations Fonctionnelles et Médecine du Sport, Avicenne University Hospital, and the Clinical Research Unit of Avicenne University Hospital, for making the EEG recordings possible.
References
1. Lindenbach, D., & Bishop, C. (2013). Critical involvement of the motor cortex in the pathophysiology and treatment of Parkinson’s disease.Neurosci. & Biobehavioral Rev.,37(10), 2737–2750.
2. Valverde, S., et al.(2020). Deep brain stimulation-guided optogenetic rescue of parkinsonian symptoms.Nat. Comm.,11(1), 2388.
3.Goldberg, J. A., et al. (2002). Enhanced synchrony among primary motor cortex neurons in the MPTP primate model of Parkinson’s disease.J. Neurosci.,22(11), 4639–4653.
