ArticleNeuromodulation : journal of the International Neuromodulation Society2019
A Driving-Force Predictor for Estimating Pathway Activation in Patient-Specific Models of Deep Brain Stimulation.
Article in Neuromodulation : journal of the International Neuromodulation Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A neural network for tics: insights from causal brain lesions and deep brain stimulation.Brain : a journal of neurology · 2022Pooled it
- Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model.PloS one · 2021Trial
- The optimization of neuroprosthetic interfaces relying on biophysical and surrogate digital twins.npj biomedical innovations · 2026Review
- Asymmetric Cingulum Bundle Connectivity Is Modulated by Paracingulate Sulcus Morphology.Human brain mapping · 2025Article
- Comparison of structural connectomes for modeling deep brain stimulation pathway activation.NeuroImage · 2025Article
- Activation metrics for structural connectivity recruitment in deep brain stimulation.Brain communications · 2025Article
- Prospective Connectomic-Based Deep Brain Stimulation Programming for Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2024Article
- Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.Nature communications · 2024Article
- Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation.Scientific reports · 2024Article
- A biophysically constrained brain connectivity model based on stimulation-evoked potentials.Journal of neuroscience methods · 2024Article
- Subcallosal cingulate deep brain stimulation evokes two distinct cortical responses via differential white matter activation.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review.Frontiers in human neuroscience · 2024Review
- Focused stimulation of dorsal versus ventral subthalamic nucleus enhances action-outcome learning in patients with Parkinson's disease.Brain communications · 2024Article
- A biophysically constrained brain connectivity model based on stimulation-evoked potentials.bioRxiv : the preprint server for biology · 2023Article
- Influence of charges deposited on membranes of human hyperdirect pathway axons on depolarization during subthalamic deep brain stimulation.Journal of neural engineering · 2023Article
- Article
- A Huygens' surface approach to rapid characterization of peripheral nerve stimulation.Magnetic resonance in medicine · 2022Article
- Somatotopic Organization of Hyperdirect Pathway Projections From the Primary Motor Cortex in the Human Brain.Frontiers in neurology · 2022Article
- StimVision v2: Examples and Applications in Subthalamic Deep Brain Stimulation for Parkinson's Disease.Neuromodulation : journal of the International Neuromodulation Society · 2021Article
- Anatomical and technical factors affecting the neural response to epidural spinal cord stimulation.Journal of neural engineering · 2020Article
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3 authors.
Funding
Abstract
objectiveDetailed biophysical modeling of deep brain stimulation (DBS) provides a theoretical approach to quantify the cellular response to the applied electric field. However, the most accurate models for performing such analyses, patient-specific field-cable (FC) pathway-activation models (PAMs), are so technically demanding to implement that their use in clinical research is greatly limited. Predictive algorithms can simplify PAM calculations, but they generally fail to reproduce the output of FC models when evaluated over a wide range of clinically relevant stimulation parameters. Therefore, we set out to develop a novel driving-force (DF) predictive algorithm (DF-Howell), customized to the study of DBS, which can better match FC results.
methodsWe developed the DF-Howell algorithm and compared its predictions to FC PAM results, as well as to the DF-Peterson algorithm, which is currently the most accurate and generalizable DF-based method. Comparison of the various methods was quantified within the context of subthalamic DBS using activation thresholds of axons representing the internal capsule, hyperdirect pathway, and cerebellothalamic tract for various combinations of fiber diameters, stimulus pulse widths, and electrode configurations.
resultsThe DF-Howell predictor estimated activation of the three axonal pathways with less than a 6.2% mean error with respect to the FC PAM for all 21 cases tested. In 15 of the 21 cases, DF-Howell outperformed DF-Peterson in estimating pathway activation, reducing mean-errors up to 22.5%.
conclusionsDF-Howell represents an accurate predictor for estimating axonal pathway activation in patient-specific DBS models, but errors still exist relative to FC PAM calculations. Nonetheless, the tractability of DF algorithms helps to reduce the technical barriers for performing accurate biophysical modeling in clinical DBS research studies.
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