Literature DB >> 25350910

Relationship between neural activation and electric field distribution during deep brain stimulation.

Mattias Astrom1, Elin Diczfalusy1, Hubert Martens2, Karin Wardell1.   

Abstract

Models and simulations are commonly used to study deep brain stimulation (DBS). Simulated stimulation fields are often defined and visualized by electric field isolevels or volumes of tissue activated (VTA). The aim of the present study was to evaluate the relationship between stimulation field strength as defined by the electric potential V, the electric field E, and the divergence of the electric field ∇(2) V, and neural activation. Axon cable models were developed and coupled to finite-element DBS models in three-dimensional (3-D). Field thresholds ( VT , ET, and ∇(2) VT ) were derived at the location of activation for various stimulation amplitudes (1 to 5 V), pulse widths (30 to 120 μs), and axon diameters (2.0 to 7.5 μm). Results showed that thresholds for VT and ∇(2) VT were highly dependent on the stimulation amplitude while ET were approximately independent of the amplitude for large axons. The activation field strength thresholds presented in this study may be used in future studies to approximate the VTA during model-based investigations of DBS without the need of computational axon models.

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Year:  2014        PMID: 25350910     DOI: 10.1109/TBME.2014.2363494

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  38 in total

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2.  Clinical deep brain stimulation strategies for orientation-selective pathway activation.

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Journal:  J Neural Eng       Date:  2018-08-10       Impact factor: 5.379

3.  Connectivity Predicts deep brain stimulation outcome in Parkinson disease.

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4.  Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

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Journal:  Neuroimage       Date:  2018-09-01       Impact factor: 6.556

Review 5.  A Comprehensive Review of Brain Connectomics and Imaging to Improve Deep Brain Stimulation Outcomes.

Authors:  Joshua K Wong; Erik H Middlebrooks; Sanjeet S Grewal; Leonardo Almeida; Christopher W Hess; Michael S Okun
Journal:  Mov Disord       Date:  2020-04-12       Impact factor: 10.338

6.  Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation.

Authors:  Kabilar Gunalan; Bryan Howell; Cameron C McIntyre
Journal:  Neuroimage       Date:  2018-01-10       Impact factor: 6.556

7.  The Quasi-uniform assumption for Spinal Cord Stimulation translational research.

Authors:  Niranjan Khadka; Dennis Q Truong; Preston Williams; John H Martin; Marom Bikson
Journal:  J Neurosci Methods       Date:  2019-10-04       Impact factor: 2.390

8.  A Driving-Force Predictor for Estimating Pathway Activation in Patient-Specific Models of Deep Brain Stimulation.

Authors:  Bryan Howell; Kabilar Gunalan; Cameron C McIntyre
Journal:  Neuromodulation       Date:  2019-02-18

9.  Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson's disease.

Authors:  Harith Akram; Stamatios N Sotiropoulos; Saad Jbabdi; Dejan Georgiev; Philipp Mahlknecht; Jonathan Hyam; Thomas Foltynie; Patricia Limousin; Enrico De Vita; Marjan Jahanshahi; Marwan Hariz; John Ashburner; Tim Behrens; Ludvic Zrinzo
Journal:  Neuroimage       Date:  2017-07-12       Impact factor: 6.556

10.  Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation.

Authors:  Bryan Howell; Faical Isbaine; Jon T Willie; Enrico Opri; Robert E Gross; Coralie De Hemptinne; Philip A Starr; Cameron C McIntyre; Svjetlana Miocinovic
Journal:  Brain Stimul       Date:  2021-03-20       Impact factor: 8.955

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