Τίτλος:
Algorithmic design of a noise-resistant and efficient closed-loop deep
brain stimulation system: A computational approach
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Advances in the field of closed-loop neuromodulation call for analysis
and modeling approaches capable of confronting challenges related to the
complex neuronal response to stimulation and the presence of strong
internal and measurement noise in neural recordings. Here we elaborate
on the algorithmic aspects of a noise-resistant closed-loop subthalamic
nucleus deep brain stimulation system for advanced Parkinson's disease
and treatment-refractory obsessive-compulsive disorder, ensuring
remarkable performance in terms of both efficiency and selectivity of
stimulation, as well as in terms of computational speed. First, we
propose an efficient method drawn from dynamical systems theory, for the
reliable assessment of significant nonlinear coupling between beta and
high-frequency subthalamic neuronal activity, as a biomarker for
feedback control. Further, we present a model-based strategy through
which optimal parameters of stimulation for minimum energy
desynchronizing control of neuronal activity are being identified. The
strategy integrates stochastic modeling and derivative-free optimization
of neural dynamics based on quadratic modeling. On the basis of
numerical simulations, we demonstrate the potential of the presented
modeling approach to identify, at a relatively low computational cost,
stimulation settings potentially associated with a significantly higher
degree of efficiency and selectivity compared with stimulation settings
determined post-operatively. Our data reinforce the hypothesis that
model-based control strategies are crucial for the design of novel
stimulation protocols at the backstage of clinical applications.
Συγγραφείς:
Karamintziou, Sofia D.
Custodio, Ana Luisa
Piallat, Brigitte and
Polosan, Mircea
Chabardes, Stephan
Stathis, Pantelis G. and
Tagaris, George A.
Sakas, Damianos E.
Polychronaki, Georgia E.
and Tsirogiannis, George L.
David, Olivier
Nikita, Konstantina
S.
Εκδότης:
Public Library of Science
DOI:
10.1371/journal.pone.0171458
