Subthalamic nucleus deep brain stimulation: accurate axonal threshold prediction with diffusion tensor based electric field models.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference · 2006 · Unclear · Very low evidence

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UnclearVery low evidencePaper 3468

Study snapshot

PublicationConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference · 2006

Study typeother

Reported directionUnclear

Evidence ratingVery low

Abstract

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the therapy of choice for medically intractable Parkinson's Disease. However, the physiological mechanisms responsible for the therapeutic effects of DBS remain unknown, and quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is lacking. Recently our group has developed various computational techniques to study the neural response to DBS. The goal of this study was to incrementally incorporate increasing levels of complexity into our computer models of STN DBS and address activation of the corticospinal tract (CST). Our model system was customized to an STN DBS patient and CST thresholds were calculated with electric field models that ranged from an electrostatic, homogeneous, isotropic model to one that explicitly incorporated the capacitance of the electrode-tissue interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to clinical CST thresholds defined from electromyographic recordings from eight muscle groups in the arm and leg of the STN DBS patient. Coupled evaluation of the model and clinical data showed that accurate prediction of axonal thresholds required our most detailed model. In addition, the simplifications and assumptions typically utilized in neurostimulation models substantially overestimate neural activation.

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AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeother

Effect directionunclear

PopulationParkinson's disease patient receiving subthalamic nucleus deep brain stimulation

Sample size1

ExposureNot supplied

Evidence strengthVery lowConfidence: 74% · Peer reviewed: yes

Main findings

In a patient-specific STN DBS model, predictions of CST/axonal activation thresholds were compared across electric field models of increasing complexity to clinical CST thresholds derived from EMG recordings. Accurate prediction of axonal thresholds required the most detailed model incorporating electrode-tissue interface capacitance, tissue encapsulation, and diffusion-tensor-based 3D tissue anisotropy/inhomogeneity; simpler models substantially overestimated neural activation.

Outcomes measured

corticospinal tract (CST) axonal activation thresholds
electromyographic (EMG)-defined clinical CST thresholds (8 muscle groups arm/leg)
accuracy of computational electric field model predictions vs clinical thresholds

Limitations

Single-patient customized model (case report)
Quantitative exposure parameters (e.g., stimulation waveform, amplitude, frequency) not provided in abstract

View raw extracted JSON

{
  "study_type": "other",
  "exposure": {
    "band": null,
    "source": "deep brain stimulation (DBS) implant",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": "Parkinson's disease patient receiving subthalamic nucleus deep brain stimulation",
  "sample_size": 1,
  "outcomes": [
    "corticospinal tract (CST) axonal activation thresholds",
    "electromyographic (EMG)-defined clinical CST thresholds (8 muscle groups arm/leg)",
    "accuracy of computational electric field model predictions vs clinical thresholds"
  ],
  "main_findings": "In a patient-specific STN DBS model, predictions of CST/axonal activation thresholds were compared across electric field models of increasing complexity to clinical CST thresholds derived from EMG recordings. Accurate prediction of axonal thresholds required the most detailed model incorporating electrode-tissue interface capacitance, tissue encapsulation, and diffusion-tensor-based 3D tissue anisotropy/inhomogeneity; simpler models substantially overestimated neural activation.",
  "effect_direction": "unclear",
  "limitations": [
    "Single-patient customized model (case report)",
    "Quantitative exposure parameters (e.g., stimulation waveform, amplitude, frequency) not provided in abstract"
  ],
  "evidence_strength": "very_low",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "deep brain stimulation",
    "subthalamic nucleus",
    "Parkinson's disease",
    "computational modeling",
    "electric field model",
    "diffusion tensor imaging",
    "tissue anisotropy",
    "corticospinal tract",
    "axonal threshold",
    "electromyography"
  ],
  "suggested_hubs": [
    {
      "slug": "engineering",
      "weight": 0.78,
      "reason": "Computational electric-field modeling of neurostimulation with diffusion-tensor-based tissue properties."
    },
    {
      "slug": "medical-implants",
      "weight": 0.64,
      "reason": "Deep brain stimulation implant and prediction of neural activation thresholds."
    }
  ]
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1109/iembs.2006.260502
PubMed: 17946452

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