Frequency-dependent spike-pattern changes in motor cortex during thalamic deep brain stimulation.

The cerebellar-receiving area of the motor thalamus is the primary anatomical target for treating essential tremor with deep brain stimulation (DBS). Although neuroimaging studies have shown that higher stimulation frequencies in this target correlate with increased cortical metabolic activity, less is known about the cellular-level functional changes that occur in the primary motor cortex (M1) with thalamic stimulation and how these changes depend on the frequency of DBS. In this study, we used a preclinical animal model of DBS to collect single-unit spike recordings in M1 before, during, and after DBS targeting the cerebellar-receiving area of the motor thalamus (VPLo, nucleus ventralis posterior lateralis pars oralis). The effects of VPLo-DBS on M1 spike rates, interspike interval entropy, and peristimulus phase-locking were compared across stimulus pulse train frequencies ranging from 10 to 130 Hz. Although VPLo-DBS modulated the spike rates of 20-50% of individual M1 cells in a frequency-dependent manner, the population-level average spike rate only weakly depended on stimulation frequency. In contrast, the population-level entropy measure showed a pronounced decrease with high-frequency stimulation, caused by a subpopulation of cells that exhibited strong phase-locking and general spike-pattern regularization. Contrarily, low-frequency stimulation induced an entropy increase (spike-pattern disordering) in a relatively large portion of the recorded population, which diminished with higher stimulation frequencies. These results also suggest that changes in phase-locking and spike-pattern entropy are not necessarily equivalent pattern phenomena, but rather that they should both be weighed when quantifying stimulation-induced spike-pattern changes.NEW & NOTEWORTHY The network mechanisms of thalamic deep brain stimulation (DBS) are not well understood at the cellular level. This study investigated the neuronal firing rate and pattern changes in the motor cortex resulting from stimulation of the cerebellar-receiving area of the motor thalamus. We showed that there is a nonintuitive relationship between general entropy-based spike-pattern measures and phase-locked regularization to DBS.

Deep brain stimulation induces sparse distributions of locally modulated neuronal activity.

Deep brain stimulation (DBS) therapy is a potent tool for treating a range of brain disorders. High frequency stimulation (HFS) patterns used in DBS therapy are known to modulate neuronal spike rates and patterns in the stimulated nucleus; however, the spatial distribution of these modulated responses are not well understood. Computational models suggest that HFS modulates a volume of tissue spatially concentrated around the active electrode. Here, we tested this theory by investigating modulation of spike rates and patterns in non-human primate motor thalamus while stimulating the cerebellar-receiving area of motor thalamus, the primary DBS target for treating Essential Tremor. HFS inhibited spike activity in the majority of recorded cells, but increasing stimulation amplitude also shifted the response to a greater degree of spike pattern modulation. Modulated responses in both categories exhibited a sparse and long-range spatial distribution within motor thalamus, suggesting that stimulation preferentially affects afferent and efferent axonal processes traversing near the active electrode and that the resulting modulated volume strongly depends on the local connectome of these axonal processes. Such findings have important implications for current clinical efforts building predictive computational models of DBS therapy, developing directional DBS lead technology, and formulating closed-loop DBS strategies.

Particle swarm optimization for programming deep brain stimulation arrays.

OBJECTIVE: Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. APPROACH: Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. MAIN RESULTS: The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. SIGNIFICANCE: The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

Multimodal 7T Imaging of Thalamic Nuclei for Preclinical Deep Brain Stimulation Applications.

Precise neurosurgical targeting of electrode arrays within the brain is essential to the successful treatment of a range of brain disorders with deep brain stimulation (DBS) therapy. Here, we describe a set of computational tools to generate in vivo, subject-specific atlases of individual thalamic nuclei thus improving the ability to visualize thalamic targets for preclinical DBS applications on a subject-specific basis. A sequential nonlinear atlas warping technique and a Bayesian estimation technique for probabilistic crossing fiber tractography were applied to high field (7T) susceptibility-weighted and diffusion-weighted imaging, respectively, in seven rhesus macaques. Image contrast, including contrast within thalamus from the susceptibility-weighted images, informed the atlas warping process and guided the seed point placement for fiber tractography. The susceptibility-weighted imaging resulted in relative hyperintensity of the intralaminar nuclei and relative hypointensity in the medial dorsal nucleus, pulvinar, and the medial/ventral border of the ventral posterior nuclei, providing context to demarcate borders of the ventral nuclei of thalamus, which are often targeted for DBS applications. Additionally, ascending fiber tractography of the medial lemniscus, superior cerebellar peduncle, and pallidofugal pathways into thalamus provided structural demarcation of the ventral nuclei of thalamus. The thalamic substructure boundaries were validated through in vivo electrophysiological recordings and post-mortem blockface tissue sectioning. Together, these imaging tools for visualizing and segmenting thalamus have the potential to improve the neurosurgical targeting of DBS implants and enhance the selection of stimulation settings through more accurate computational models of DBS.

Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets.

Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial electrodes minimally improved in the ability to steer, shift, and sculpt axonal activation around a DBS lead and a simple feature set consisting of the RoA center of mass and orientation enabled robust machine learning classification. These results provide important design constraints for future development of high-density DBS arrays.

Theoretical Optimization of Stimulation Strategies for a Directionally Segmented Deep Brain Stimulation Electrode Array.

Programming deep brain stimulation (DBS) systems currently involves a clinician manually sweeping through a range of stimulus parameter settings to identify the setting that delivers the most robust therapy for a patient. With the advent of DBS arrays with a higher number and density of electrodes, this trial and error process becomes unmanageable in a clinical setting. This study developed a computationally efficient, model-based algorithm to estimate an electrode configuration that will most strongly activate tissue within a volume of interest. The cerebellar-receiving area of motor thalamus, the target for treating essential tremor with DBS, was rendered from imaging data and discretized into grid points aligned in approximate afferent and efferent axonal pathway orientations. A finite-element model (FEM) was constructed to simulate the volumetric tissue voltage during DBS. We leveraged the principle of voltage superposition to formulate a convex optimization-based approach to maximize activating function (AF) values at each grid point (via three different criteria), hence increasing the overall probability of action potential initiation and neuronal entrainment within the target volume. For both efferent and afferent pathways, this approach achieved global optima within several seconds. The optimal electrode configuration and resulting AF values differed across each optimization criteria and between axonal orientations. This approach only required a set of FEM simulations equal to the number of DBS array electrodes, and could readily accommodate anisotropic-inhomogeneous tissue conductances or other axonal orientations. The algorithm provides an efficient, flexible determination of optimal electrode configurations for programming DBS arrays.

Spherical statistics for characterizing the spatial distribution of deep brain stimulation effects on neuronal activity.

BACKGROUND: Computational models of deep brain stimulation (DBS) have played a key role in understanding its physiological mechanisms. By estimating a volume of tissue directly modulated by DBS, one can relate the neuronal pathways within those volumes to the therapeutic efficacy of a particular DBS setting. NEW METHOD: A spherical statistical framework is described to quantify and determine salient features of such morphologies using visualization techniques, empirical shape analysis, and formal hypothesis testing. This framework is shown using a 3D model of thalamocortical neurons surrounding a radially-segmented DBS array. RESULTS: We show that neuronal population volumes modulated by various DBS electrode configurations can be characterized by parametric distribution models, such as Kent and Watson girdle models. Distribution parameters were found to change with stimulus settings, including amplitude and radial distance from the DBS array. Increasing stimulation amplitude through a single electrode resulted in more diffuse neuronal activation and increased rotational symmetry about the mean direction of the activated population. When stimulation amplitude was held constant, the activated neuronal population distribution was more concentrated with distance from the DBS array and was also more rotationally asymmetric. We also show how data representation (e.g. stimulus-entrained cell body vs. axon node) can significantly alter model distribution shape. COMPARISON TO EXISTING METHODS: This statistical framework provides a quantitative method to analyze the spatial morphologies of DBS-induced effects on neuronal activity. CONCLUSIONS: The application of spherical statistics to assess spatial distributions of neuronal activity has potential usefulness for numerous other recording, labeling, and stimulation modalities.

In Vivo 7T MRI of the Non-Human Primate Brainstem.

Structural brain imaging provides a critical framework for performing stereotactic and intraoperative MRI-guided surgical procedures, with procedural efficacy often dependent upon visualization of the target with which to operate. Here, we describe tools for in vivo, subject-specific visualization and demarcation of regions within the brainstem. High-field 7T susceptibility-weighted imaging and diffusion-weighted imaging of the brain were collected using a customized head coil from eight rhesus macaques. Fiber tracts including the superior cerebellar peduncle, medial lemniscus, and lateral lemniscus were identified using high-resolution probabilistic diffusion tractography, which resulted in three-dimensional fiber tract reconstructions that were comparable to those extracted from sequential application of a two-dimensional nonlinear brain atlas warping algorithm. In the susceptibility-weighted imaging, white matter tracts within the brainstem were also identified as hypointense regions, and the degree of hypointensity was age-dependent. This combination of imaging modalities also enabled identifying the location and extent of several brainstem nuclei, including the periaqueductal gray, pedunculopontine nucleus, and inferior colliculus. These clinically-relevant high-field imaging approaches have potential to enable more accurate and comprehensive subject-specific visualization of the brainstem and to ultimately improve patient-specific neurosurgical targeting procedures, including deep brain stimulation lead implantation.