Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Gunalan K

PROVIDER: S-EPMC5910209 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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

Gunalan Kabilar K Howell Bryan B McIntyre Cameron C CC

NeuroImage 20180110

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 ...[more]

PMID: 29331449

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Project description:Deep brain stimulation therapy is an effective symptomatic treatment for Parkinson's disease, yet the precise mechanisms responsible for its therapeutic effects remain unclear. Although the targets of deep brain stimulation are grey matter structures, axonal modulation is known to play an important role in deep brain stimulation's therapeutic mechanism. Several white matter structures in proximity to the subthalamic nucleus have been implicated in the clinical benefits of deep brain stimulation for Parkinson's disease. We assessed the connectivity patterns that characterize clinically beneficial electrodes in Parkinson's disease patients, after deep brain stimulation of the subthalamic nucleus. We evaluated 22 patients with Parkinson's disease (11 females, age 57 ± 9.1 years, disease duration 13.3 ± 6.3 years) who received bilateral deep brain stimulation of the subthalamic nucleus at the National Institutes of Health. During an initial electrode screening session, one month after deep brain stimulation implantation, the clinical benefits of each contact were determined. The electrode was localized by coregistering preoperative magnetic resonance imaging and postoperative computer tomography images and the volume of tissue activated was estimated from stimulation voltage and impedance. Brain connectivity for the volume of tissue activated of deep brain stimulation contacts was assessed using probabilistic tractography with diffusion-tensor data. Areas most frequently connected to clinically effective contacts included the thalamus, substantia nigra, brainstem and superior frontal gyrus. A series of discriminant analyses demonstrated that the strength of connectivity to the superior frontal gyrus and the thalamus were positively associated with clinical effectiveness. The connectivity patterns observed in our study suggest that the modulation of white matter tracts directed to the superior frontal gyrus and the thalamus is associated with favourable clinical outcomes and may contribute to the therapeutic effects of deep brain stimulation. Our method can be further developed to reliably identify effective deep brain stimulation contacts and aid in the programming process.

Project description:ObjectiveCurrent understanding of the neuromodulatory effects of deep brain stimulation (DBS) on large-scale brain networks remains elusive, largely due to the lack of techniques that can reveal DBS-induced activity at the whole-brain level. Using a novel 3T magnetic resonance imaging (MRI)-compatible stimulator, we investigated whole-brain effects of subthalamic nucleus (STN) stimulation in patients with Parkinson disease.MethodsFourteen patients received STN-DBS treatment and participated in a block-design functional MRI (fMRI) experiment, wherein stimulations were delivered during "ON" blocks interleaved with "OFF" blocks. fMRI responses to low-frequency (60Hz) and high-frequency(130Hz) STN-DBS were measured 1, 3, 6, and 12 months postsurgery. To ensure reliability, multiple runs (48 minutes) of fMRI data were acquired at each postsurgical visit. Presurgical resting-state fMRI (30 minutes) data were also acquired.ResultsTwo neurocircuits showed highly replicable, but distinct responses to STN-DBS. A circuit involving the globus pallidus internus (GPi), thalamus, and deep cerebellar nuclei was significantly activated, whereas another circuit involving the primary motor cortex (M1), putamen, and cerebellum showed DBS-induced deactivation. These 2 circuits were dissociable in terms of their DBS-induced responses and resting-state functional connectivity. The GPi circuit was frequency-dependent, selectively responding to high-frequency stimulation, whereas the M1 circuit was responsive in a time-dependent manner, showing enhanced deactivation over time. Finally, activation of the GPi circuit was associated with overall motor improvement, whereas M1 circuit deactivation was related to reduced bradykinesia.InterpretationConcurrent DBS-fMRI using 3T revealed 2 distinct circuits that responded differentially to STN-DBS and were related to divergent symptoms, a finding that may provide novel insights into the neural mechanisms underlying DBS. ANN NEUROL 2020;88:1178-1193.

Dataset Information

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

Publications

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

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