Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced by a particular stimulation setting have been developed. However, the medical specialist still has to search, by trial and error, for a DBS set-up that generates the desired VTA. Therefore, our goal is developing a DBS parameter tuning strategy for current clinical devices that allows defining a target VTA under biophysically viable constraints. We propose a machine learning approach that allows estimating the DBS parameter values for a given VTA, which comprises two main stages: i) A K-nearest neighbors-based deformation to define a target VTA preserving biophysically viable constraints. ii) A parameter estimation stage that consists of a data projection using metric learning to highlight relevant VTA properties, and a regression/classification algorithm to estimate the DBS parameters that generate the target VTA. Our methodology allows setting a biophysically compliant target VTA and accurately predicts the required configuration of stimulation parameters. Also, the performance of our approach is stable for both isotropic and anisotropic tissue conductivities. Furthermore, the computational cost of the trained system is acceptable for real-world implementations.
Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s α entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.
The volume of tissue activated (VTA) is a well-established approach to model the direct effect of deep brain stimulation (DBS) on neural tissue. Previous studies have pointed to its potential clinical applications. However, the elevated computational runtime required to estimate the VTA with standard techniques used in biological neural modeling limits its suitability for practical use. The goal of this study was to develop a novel methodology to reduce the computation time of VTA estimation. To that end, we built a Gaussian process emulator. It combines multicompartment axon models coupled to the stimulating electric field with a Gaussian process classifier (GPC), following the premise that computing the VTA from a field of axons is in essence a binary classification problem. We achieved a considerable reduction in the average time required to estimate the VTA, under both ideal isotropic and realistic anisotropic brain tissue conductive conditions, limiting the loss of accuracy and overcoming other drawbacks entailed by alternative methods. RESUMEN:El volumen de tejido activo (VTA) es un enfoque bien establecido para modelar los efectos directos de la estimulación cerebral profunda en el tejido neuronal. Estudios previos han señalado sus posibles aplicaciones clínicas. Sin embargo, el elevado costo computacional requerido para estimar el VTA con las técnicas estándar utilizadas en el modelado neuronal biológico limita su usabilidad a nivel práctico. El objetivo de este estudio fue desarrollar una metodología novedosa para reducir el tiempo de cálculo en la estimación del VTA. Con ese fin, se construyó un emulador basado en procesos gaussianos. Este combina modelos axonales de múltiples compartimientos acoplados al campo de estimulación eléctrica con un sistema de clasificación basado en procesos gaussianos, siguiendo la premisa de que calcular el VTA a partir de un campo axonal es en esencia un problema de clasificación binaria. Se logró una reducción considerable en el tiempo promedio requerido para estimar el VTA, tanto bajo condiciones de conductividad isotrópica idealizada como bajo condiciones realistas de conductividad anisotrópica, limitando la perdida de precisión y superando otros inconvenientes presentes en métodos alternativos.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.