Patient-specific analysis of the volume of tissue activated during deep brain stimulation

Abstract

Despite the clinical success of deep brain stimulation (DBS) for the treatment of movement disorders, many questions remain about its effects on the nervous system. We have developed a methodology to predict the volume of tissue activated (VTA) by DBS on a patient-specific basis. Our goals are to identify the intersection between the VTA and surrounding anatomical structures and to compare activation of these structures with clinical outcomes. The model system consists of three fundamental components: 1) a 3D anatomical model of the subcortical nuclei and DBS electrode position in the brain, each derived from magnetic resonance imaging (MRI); 2) a finite element model of the DBS electrode and electric field transmitted to the brain, with tissue conductivity properties derived from diffusion tensor MRI; 3) VTA prediction derived from the response of myelinated axons to the applied electric field, which is a function of the stimulation parameters (contact, impedance, voltage, pulse width, frequency). We used this model system to analyze the effects of subthalamic nucleus (STN) DBS in a patient with Parkinson's disease. Quantitative measurements of bradykinesia, rigidity, and corticospinal tract (CST) motor thresholds were evaluated over a range of stimulation parameter settings. Our model predictions showed good agreement with CST thresholds. Additionally, stimulation through electrode contacts that improved bradykinesia and rigidity generated VTAs that overlapped the zona incerta / fields of Forel (ZI/H2). Application of DBS technology to various neurological disorders has preceded scientific characterization of the volume of tissue directly affected by the stimulation. Synergistic integration of clinical analysis, neuroimaging, neuroanatomy, and neurostimulation modeling provides the opportunity to address wide ranging questions on the factors linked with the therapeutic benefits and side effects of DBS.

Mechanisms and Targets of Deep Brain Stimulation in Movement Disorders

“…157,158 A recent computer modeling study described promising results in the prediction of current spread during DBS. 159 In these experiments, the locations of electrode contacts were reconstructed from postoperative MRI data, and a three-dimensional brain atlas was warped to the patient's MRI to identify anatomical structures and their position with respect to the stimulated electrode or electrodes. The patient-specific volume of tissue activated (VTA) was constructed using theoretical models of the DBS voltage field and axonal responses to extracellular stimulation.…”

Method for patient-specific finite element modeling and simulation of deep brain stimulation

“…More recently, Butson et al [17] presented a three dimensional (3D) patient-specific method to predict the volume of tissue activated by DBS based on diffusion tensor images (DTI). However, the spatial resolution of DTI is still rather poor and DTI is not an available technique in many clinical centres.…”

Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation

“…We used the monophasic waveform to illustrate the differences between the expected waveform parameters, as indicated by the programming device, and the actual waveforms produced by the IPGs (Figure 2). In our experience, explicit representation of the actual IPG waveform is an important component of accurate models of the neural response to DBS (Butson and McIntyre, 2005;Butson et al, 2006b;Miocinovic et al, 2006;Butson et al, 2007).…”

“…Hence, the time-and space-dependent voltage distribution generated in the tissue medium was interpolated onto multi-compartment cable-model axons to determine their activation thresholds (Butson et al, 2006a) (Figure 1C). This general modeling approach has been experimentally validated by examining stimulation side effects due to spillover into oculomotor nerve (Butson et al, 2006b) and corticospinal tract (Miocinovic et al, 2006;Butson et al, 2007).…”