Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example

Kabilar Gunalan, Ashutosh Chaturvedi, Bryan Howell, Yuval Duchin, Scott F. Lempka, Remi Patriat, Guillermo Sapiro, Noam Harel, Cameron C. McIntyre

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Background Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports. Objective Provide a detailed description of the assembly workflow and parameterization of a patientspecific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation. Methods Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution. Results Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings. Conclusion Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

Original languageEnglish (US)
Article numbere0176132
JournalPloS one
Volume12
Issue number4
DOIs
StatePublished - Apr 2017

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health (NIH) (R01 NS085188, P41 EB015894, P30 NS076408, U54 MH091657). KG was supported by training grants from the NIH (T32 GM007250, TL1 TR000441, T32 EB004314) and the U.S. Department of Education (GAANN P200A100112).

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