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

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

doi:10.1371/journal.pone.0176132

Cited by 99 publications

(140 citation statements)

References 69 publications

Supporting

Mentioning

136

Contrasting

Order By: Relevance

“…The technical details and methodology for constructing the patient-specific DBS models are described in prior works [Howell and McIntyre, 2016; Howell and McIntyre, 2017; Gunalan et al, 2017] and summarized below. We hypothesized that the assumptions and simplifications inherent in DF and VTA PAMs would generate markedly different results when compared to FC PAMs.…”

Section: Methodsmentioning

confidence: 99%

“…We hypothesized that the assumptions and simplifications inherent in DF and VTA PAMs would generate markedly different results when compared to FC PAMs. We used the most detailed patient-specific foundation for DBS modeling currently available to perform our analysis [Gunalan et al, 2017], and attempted to minimize sources of variance between comparison of methods. We used the FC PAM, one DF PAM [Peterson et al, 2011] and three VTA PAMs [Chaturvedi et al, 2013; Madler and Coenen, 2012; Astrom et al, 2015] to estimate the activation thresholds of individual axons within three different pathways coursing through the subthalamic region.…”

Section: Methodsmentioning

confidence: 99%

“…Pre-operative T1-weighted (T1W), T2-weighted (T2W), susceptibility-weighted (SW), and diffusion-weighted (DW) images were obtained on a 7T magnetic resonance imaging (MRI) system (Magnex Scientific, UK) [Duchin et al, 2012; Gunalan et al, 2017]. A pre-operative T1W image was also acquired on a 1.5T Siemens Magnetom Espree.…”

Section: Methodsmentioning

confidence: 99%

“…In particular, subthalamic DBS has already been the focus of numerous PAM studies [e.g. Coenen et al, 2011; Chaturvedi et al, 2012; Groppa et al, 2014; Sweet et al, 2014; Accolla et al, 2016; Vanegas-Arroyave et al, 2016; Gunalan et al, 2017; Horn et al, 2017; Akram et al, 2017]. However, the various PAM analyses have employed a wide range of different methods without much in the way of documentation or validation of their accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…FC methods are the most detailed and technically demanding, explicitly modeling the electric field, axonal trajectories, and transmembrane ion channels responsible for action potential initiation in response to extracellular stimuli [McNeal, 1976; Gunalan et al, 2017]. DF methods represent a 1-degree simplification of FC methods, requiring calculation of the electric field but simplifying estimation of the transmembrane response to substantially speed up the simulations [Warman et al, 1992; Peterson et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

2018

Self Cite

View full text Add to dashboard Cite

Medical imaging has played a major role in defining the general anatomical targets for deep brain stimulation (DBS) therapies. However, specifics on the underlying brain circuitry that is directly modulated by DBS electric fields remain relatively undefined. Detailed biophysical modeling of DBS provides an approach to quantify the theoretical responses to stimulation at the cellular level, and has established a key role for axonal activation in the therapeutic mechanisms of DBS. Estimates of DBS-induced axonal activation can then be coupled with advances in defining the structural connectome of the human brain to provide insight into the modulated brain circuitry and possible correlations with clinical outcomes. These pathway-activation models (PAMs) represent powerful tools for DBS research, but the theoretical predictions are highly dependent upon the underlying assumptions of the particular modeling strategy used to create the PAM. In general, three types of PAMs are used to estimate activation: 1) field-cable (FC) models, 2) driving force (DF) models, and 3) volume of tissue activated (VTA) models. FC models represent the “gold standard” for analysis but at the cost of extreme technical demands and computational resources. Consequently, DF and VTA PAMs, derived from simplified FC models, are typically used in clinical research studies, but the relative accuracy of these implementations is unknown. Therefore, we performed a head-to-head comparison of the different PAMs, specifically evaluating DBS of three different axonal pathways in the subthalamic region. The DF PAM was markedly more accurate than the VTA PAMs, but none of these simplified models were able to match the results of the patient-specific FC PAM across all pathways and combinations of stimulus parameters. These results highlight the limitations of using simplified predictors to estimate axonal stimulation and emphasize the need for novel algorithms that are both biophysically realistic and computationally simple.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2018

Self Cite

View full text Add to dashboard Cite

show abstract

Using automated electrode localization to guide stimulation management in DBS

Petersen

Husch

Parsons

et al. 2018

Ann Clin Transl Neurol

View full text Add to dashboard Cite

Deep Brain Stimulation requires extensive postoperative testing of stimulation parameters to achieve optimal outcomes. Testing is typically not guided by neuroanatomical information on electrode contact locations. To address this, we present an automated reconstruction of electrode locations relative to the treatment target, the subthalamic nucleus, comparing different targeting methods: atlas‐, manual‐, or tractography‐based subthalamic nucleus segmentation. We found that most electrode contacts chosen to deliver stimulation were closest or second closest to the atlas‐based subthalamic nucleus target. We suggest that information on each electrode contact's location, which can be obtained using atlas‐based methods, might guide clinicians during postoperative stimulation testing.

show abstract

Probabilistic sweet spots predict motor outcome for deep brain stimulation in Parkinson disease

Dembek

Roediger

Horn

et al. 2019

Annals of Neurology

138

188

View full text Add to dashboard Cite

ObjectiveTo investigate whether functional sweet spots of deep brain stimulation (DBS) in the subthalamic nucleus (STN) can predict motor improvement in Parkinson disease (PD) patients.MethodsStimulation effects of 449 DBS settings in 21 PD patients were clinically and quantitatively assessed through standardized monopolar reviews and mapped into standard space. A sweet spot for best motor outcome was determined using voxelwise and nonparametric permutation statistics. Two independent cohorts were used to investigate whether stimulation overlap with the sweet spot could predict acute motor outcome (10 patients, 163 settings) and long‐term overall Unified Parkinson's Disease Rating Scale Part III (UPDRS‐III) improvement (63 patients).ResultsSignificant clusters for suppression of rigidity and akinesia, as well as for overall motor improvement, resided around the dorsolateral border of the STN. Overlap of the volume of tissue activated with the sweet spot for overall motor improvement explained R2 = 37% of the variance in acute motor improvement, more than triple what was explained by overlap with the STN (R2 = 9%) and its sensorimotor subpart (R2 = 10%). In the second independent cohort, sweet spot overlap explained R2 = 20% of the variance in long‐term UPDRS‐III improvement, which was equivalent to the variance explained by overlap with the STN (R2 = 21%) and sensorimotor STN (R2 = 19%).InterpretationThis study is the first to predict clinical improvement of parkinsonian motor symptoms across cohorts based on local DBS effects only. The new approach revealed a distinct sweet spot for STN DBS in PD. Stimulation overlap with the sweet spot can predict short‐ and long‐term motor outcome and may be used to guide DBS programming. ANN NEUROL 2019;86:527–538

show abstract

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

Cited by 99 publications

References 69 publications

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

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

Using automated electrode localization to guide stimulation management in DBS

Probabilistic sweet spots predict motor outcome for deep brain stimulation in Parkinson disease

Contact Info

Product

Resources

About