Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912758924050432 |
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| author | Prieto, Fernando Galaz Lassila, Antti Samavaki, Maryam Pursiainen, Sampsa |
| author_facet | Prieto, Fernando Galaz Lassila, Antti Samavaki, Maryam Pursiainen, Sampsa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach Prieto, Fernando Galaz Lassila, Antti Samavaki, Maryam Pursiainen, Sampsa Optimization and Control Machine Learning 90C08 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. |
| title | Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach |
| topic | Optimization and Control Machine Learning 90C08 |
| url | https://arxiv.org/abs/2506.13452 |