Optimizing electrode placement and information capacity for local field potentials in cortex

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The paper develops subject-specific, in silico modeling tools to optimize intracranial electrode placement and maximize the Shannon-Hartley information capacity of local field potentials (LFPs). Using subject MRI data and finite element modeling to generate sensitivity/lead-field maps, it compares electrode placements, contact sizes, contact configurations, and substrate properties across subdural and intracortical devices, including using a genetic algorithm for placement optimization and SEPIO for selecting sensor subsets for source classification. It reports that optimized placements improve information capacity and signal quality and that sensor-based classification can enhance data quality without increasing electrode cost. The paper’s main limitation is that the results are demonstrated through computational/simulation-based modeling rather than direct clinical outcome validation. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Recent neurosurgery advancements include improved stereotactic targeting and increased electrode contacts. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the inherent information patterns of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools offer a quantitative framework to juxtapose devices in one′s neurosurgical armament or optimize device and contact placement. It may help users refine electrode coverage with low channel count devices and minimize invasive surgery burden. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of LFP recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants.
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Abstract

Recent neurosurgery advancements include improved stereotactic targeting and increased density and specificity of electrophysiological evaluation. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the Shannon-Hartley information capacity of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One key tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools provide a quantitative framework to select devices from a neurosurgical armament and to optimize device and contact placement. Using these tools may help refine electrode coverage with low channel count devices while minimizing the burden of invasive surgery. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of local field potential (LFP) recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants. Highlights: tool for simulating subject-specific local field potentials and electrode sensitivity. ptimized electrode placement enhances ROI source coverage, and signal quality. sensor-based classification boosts data quality without extra electrode cost. comparisons of devices and contact arrangements. Competing Interest Statement The authors have declared no competing interest. Footnotes

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update (Figure 15 and Table 1), author/funding correction.

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last seen: 2026-05-20T01:45:00.602351+00:00