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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
Published on: August 12, 2018
Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation
Kabilar Gunalan1, Bryan Howell1, Cameron C McIntyre1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Deep brain stimulation (DBS) pathway-activation models (PAMs) predict brain circuitry modulation. Driving force (DF) models are more accurate than volume of tissue activated (VTA) models, but neither fully replicate field-cable (FC) models.
Area of Science:
- Neuroscience
- Biophysics
- Computational modeling
Background:
- Medical imaging guides deep brain stimulation (DBS) targets, but precise brain circuitry modulation by DBS electric fields is unclear.
- Axonal activation is crucial for DBS therapeutic mechanisms, necessitating accurate modeling of DBS-induced effects.
- Pathway-activation models (PAMs) integrate DBS biophysical modeling with human brain connectome data to study modulated circuitry and clinical outcomes.
Purpose of the Study:
- To compare the accuracy of different PAMs for estimating DBS-induced axonal activation.
- To evaluate field-cable (FC), driving force (DF), and volume of tissue activated (VTA) models in the subthalamic region.
- To identify limitations of current simplified PAMs and the need for improved algorithms.
Main Methods:
- Performed a head-to-head comparison of FC, DF, and VTA PAMs.
- Evaluated DBS of three distinct axonal pathways in the subthalamic region.
- Assessed model accuracy across various stimulus parameters using patient-specific FC models as the gold standard.
Main Results:
- The DF PAM demonstrated significantly higher accuracy than VTA PAMs.
- No simplified PAM (DF or VTA) accurately replicated the patient-specific FC PAM results for all pathways and stimulus parameters.
- Results highlight discrepancies between simplified models and gold-standard biophysical simulations.
Conclusions:
- Simplified PAMs like DF and VTA have limitations in accurately predicting DBS-induced axonal activation.
- Current simplified models do not fully capture the complexity of DBS modulation compared to detailed biophysical models.
- Novel algorithms are needed that balance biophysical realism with computational efficiency for DBS research.
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