Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach

TL;DR

This study introduces an L1L1 regularization method for optimizing directional DBS electrode configurations, effectively incorporating lead field uncertainty to improve focality, robustness, and target specificity in stimulation.

Contribution

The paper presents a novel L1L1-based optimization framework that explicitly accounts for lead field uncertainty, enhancing electrode configuration selection for directional DBS.

Findings

L1L1 method effectively fits and regularizes current distributions.

Optimized configurations maximize focal stimulation and minimize spread.

Method demonstrates robustness under noisy lead field conditions.

Abstract

As DBS technology advances toward directional leads and optimization-based current steering, this study aims to improve the selection of electrode contact configurations using the recently developed L1-norm regularized L1-norm fitting (L1L1) method. The focus is in particular on L1L1's capability to incorporate a priori lead field uncertainty, offering a potential advantage over conventional approaches that do not account for such variability. Our optimization framework incorporates uncertainty by constraining the solution space based on lead field attenuation. This reflects physiological expectations about the VTA and serves to avoid overfitting. By applying this method to 8- and 40-contact electrode configurations, we optimize current distributions within a discretized finite element (FE) model, focusing on the lead field's characteristics. The model accounts for uncertainty through these explicit constraints, enhancing the feasibility, focality, and robustness of the resulting solutions. The L1L1 method was validated through a series of numerical experiments using both noiseless and noisy lead fields, where the noise level was selected to reflect attenuation within VTA. It successfully fits and regularizes the current distribution across target structures, with hyperparameter optimization extracting either bipolar or multipolar electrode configurations. These configurations aim to maximize focused current density or prioritize a high gain field ratio in a discretized FE model. Compared to traditional methods, the L1L1 approach showed competitive performance in concentrating stimulation within the target region while minimizing unintended current spread, particularly under noisy conditions. By incorporating uncertainty directly into the optimization process, we obtain a noise-robust framework for current steering, allowing for variations in lead field models and simulation parameters.