Optimizing deep brain stimulation based on isostable amplitude in essential tremor patient models.

Duchet B
Weerasinghe G
Bick C
Bogacz R

The size (‘amplitude’) of pathological brain activity can be quantified using the mathematical concept of ‘isostable amplitude’. Here, we show that using this definition of amplitude could help improve deep brain stimulation for patients with essential tremor.

Scientific Abstract

Deep brain stimulation is a treatment for medically refractory essential tremor. To improve the therapy, closed-loop approaches are designed to deliver stimulation according to the system's state, which is constantly monitored by recording a pathological signal associated with symptoms (e.g. brain signal or limb tremor). Since the space of possible closed-loop stimulation strategies is vast and cannot be fully explored experimentally, how to stimulate according to the state should be informed by modeling. A typical modeling goal is to design a stimulation strategy that aims to maximally reduce the Hilbert amplitude of the pathological signal in order to minimize symptoms. Isostables provide a notion of amplitude related to convergence time to the attractor, which can be beneficial in model-based control problems. However, how isostable and Hilbert amplitudes compare when optimizing the amplitude response to stimulation in models constrained by data is unknown.

This figure is a map of the response to deep brain stimulation predicted by isostable amplitude for one patient.
This is a map of the response to deep brain stimulation predicted by isostable amplitude for one patient. The colour scale corresponds to the change in tremor amplitude, and the position in the map is the brain state. Stimulation provided in zones represented in blue is beneficial. The darker the blue, the more beneficial stimulation is. Stimulation provided in zones represented in yellow to red would not be beneficial. The map can be used to devise optimal ways of stimulating to maximally reduce patient symptoms.
Citation

2021. J Neural Eng, 18(4):046023.

DOI
10.1088/1741-2552/abd90d
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