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

Evaluation of Interactive Visualization on Mobile Computing Platforms for Selection of Deep Brain Stimulation Parameters

In recent years, there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this paper, we deploy the interactive visualization system ImageVis3D Mobile, which has been designed for mobile computing devices such as the iPhone or iPad, in an evaluation environment to visualize models of Parkinson's disease patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires repeated revisions to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independent of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system, available directly at the point of care on a device familiar to the clinician, can be used to guide clinical decision making for selection of DBS settings. In our view, the positive impact of the system could also translate to areas other than DBS.

Computational modeling of deep brain stimulation

Deep brain stimulation (DBS) is an effective clinical treatment for several medically refractory neurological disorders. However, even after decades of clinical success, explicit understanding of the response of neurons to applied electric fields remains limited, and scientific definition of the therapeutic mechanisms of DBS remains elusive. In addition, it is presently unclear which electrode designs and stimulation paradigms are optimal for maximal therapeutic benefit and minimal side-effects with DBS. Detailed computer modeling of DBS has emerged recently as a powerful technique to enhance our understanding of the effects of DBS and to create a virtual testing ground for new stimulation strategies. This chapter summarizes the fundamentals of neurostimulation modeling, presents some scientific contributions of computer models to the field of DBS, and demonstrates the application of DBS modeling tools to augment the clinical utility of DBS.

Mood response to deep brain stimulation of the subthalamic nucleus in Parkinson's disease

Deep brain stimulation of the subthalamic nucleus (STN DBS) in Parkinson's disease (PD) improves motor functioning but has variable effects on mood. Little is known about the relationship between electrode contact location and mood response. The authors identified the anatomical location of electrode contacts and measured mood response to stimulation with the Visual Analog Scale in 24 STN DBS PD patients. Participants reported greater positive mood and decreased anxiety and apathy with bilateral and unilateral stimulation. Left DBS improved mood more than right DBS. Right DBS-induced increase in positive mood was related to more medial and dorsal contact locations. These results highlight the functional heterogeneity of the STN.

Dorsal column stimulator applications

Background:

Spinal cord stimulation (SCS) has been used to treat neuropathic pain since 1967. Following that, technological progress, among other advances, helped SCS become an effective tool to reduce pain.

Methods:

This article is a non-systematic review of the mechanism of action, indications, results, programming parameters, complications, and cost-effectiveness of SCS.

Results:

In spite of the existence of several studies that try to prove the mechanism of action of SCS, it still remains unknown. The mechanism of action of SCS would be based on the antidromic activation of the dorsal column fibers, which activate the inhibitory interneurons within the dorsal horn. At present, the indications of SCS are being revised constantly, while new applications are being proposed and researched worldwide. Failed back surgery syndrome (FBSS) is the most common indication for SCS, whereas, the complex regional pain syndrome (CRPS) is the second one. Also, this technique is useful in patients with refractory angina and critical limb ischemia, in whom surgical or endovascular treatment cannot be performed. Further indications may be phantom limb pain, chronic intractable pain located in the head, face, neck, or upper extremities, spinal lumbar stenosis in patients who are not surgical candidates, and others.

Conclusion:

Spinal cord stimulation is a useful tool for neuromodulation, if an accurate patient selection is carried out prior, which should include a trial period. Undoubtedly, this proper selection and a better knowledge of its underlying mechanisms of action, will allow this cutting edge technique to be more acceptable among pain physicians.

Effects of repeated deep brain stimulation on depressive- and anxiety-like behavior in rats: comparing entopeduncular and subthalamic nuclei

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or internal globus pallidus (GPi) has been routinely used for the treatment of some movement disorders. However, DBS may be associated with adverse psychiatric effects, such as depression, anxiety and impulsivity.

OBJECTIVE

To compare DBS applied to the entopeduncular nucleus (EPN; the rodent homolog of the GPi) and STN in terms of their effects on depressive- and anxiety-like behavior in rats.

METHODS

DBS was applied for 21 days (4 h a day) to either the STN or EPN. Rats then underwent behavioral testing on learned helplessness and elevated plus maze tasks before being sacrificed for brain analyses of zif268, BDNF and trkB mRNA as well as BDNF protein levels.

RESULTS

Repeated DBS of the STN, but not of the EPN, led to impaired performance in the learned helplessness task, suggesting that STN-DBS induces or potentiates depressive-like behavior. There was no effect of DBS on elevated plus maze or on open field behavior. Repeated STN-DBS, but not EPN-DBS, led to decreased levels of BDNF and trkB mRNA in hippocampus. Acute stimulation of the STN or EPN resulted in similar changes in zif268 levels in several brain areas, except for the raphe where decreases were seen only after STB-DBS.

CONCLUSIONS

Together these results indicate that the effects of STN- and EPN-DBS differ in behavioral and neurochemical respects. Results further suggest that the EPN may be a preferable target for clinical DBS when psychiatric side effects are considered insofar as it may be associated with a lower incidence of depressive-like behavior than the STN.

[Obsessive-compulsive disorder, a new model of basal ganglia dysfunction? Elements from deep brain stimulation studies]

Deep brain stimulation was first developed for movement disorders but is now being offered as a therapeutic alternative in severe psychiatric disorders after the failure of conventional therapies. One of such pathologies is obsessive-compulsive disorder. This disorder which associates intrusive thoughts (obsessions) and repetitive irrepressible rituals (compulsions) is characterized by a dysfunction of a cortico-subcortical loop. After having reviewed the pathophysiological evidence to show why deep brain stimulation was an interesting path to take for severe and resistant cases of obsessive-compulsive disorder, we will present the results of the different clinical trials. Finally, we will provide possible mechanisms for the effects of deep brain stimulation in this pathology.

Current steering to activate targeted neural pathways during deep brain stimulation of the subthalamic region

Deep brain stimulation (DBS) has steadily evolved into an established surgical therapy for numerous neurological disorders, most notably Parkinson's disease (PD). Traditional DBS technology relies on voltage-controlled stimulation with a single source; however, recent engineering advances are providing current-controlled devices with multiple independent sources. These new stimulators deliver constant current to the brain tissue, irrespective of impedance changes that occur around the electrode, and enable more specific steering of current towards targeted regions of interest. In this study, we examined the impact of current steering between multiple electrode contacts to directly activate three distinct neural populations in the subthalamic region commonly stimulated for the treatment of PD: projection neurons of the subthalamic nucleus (STN), globus pallidus internus (GPi) fibers of the lenticular fasiculus, and internal capsule (IC) fibers of passage. We used three-dimensional finite element electric field models, along with detailed multicompartment cable models of the three neural populations to determine their activations using a wide range of stimulation parameter settings. Our results indicate that selective activation of neural populations largely depends on the location of the active electrode(s). Greater activation of the GPi and STN populations (without activating any side effect related IC fibers) was achieved by current steering with multiple independent sources, compared to a single current source. Despite this potential advantage, it remains to be seen if these theoretical predictions result in a measurable clinical effect that outweighs the added complexity of the expanded stimulation parameter search space generated by the more flexible technology.

Improved spatial targeting with directionally segmented deep brain stimulation leads for treating essential tremor

Deep brain stimulation (DBS) in the ventral intermediate nucleus of thalamus (Vim) is known to exert a therapeutic effect on postural and kinetic tremor in patients with essential tremor (ET). For DBS leads implanted near the caudal border of Vim, however, there is an increased likelihood that one will also induce paresthesia side-effects by stimulating neurons within the sensory pathway of the ventral caudal (Vc) nucleus of thalamus. The aim of this computational study was to (1) investigate the neuronal pathways modulated by therapeutic, sub-therapeutic and paresthesia-inducing DBS settings in three patients with ET and (2) determine how much better an outcome could have been achieved had these patients been implanted with a DBS lead containing directionally segmented electrodes (dDBS). Multi-compartment neuron models of the thalamocortical, cerebellothalamic and medial lemniscal pathways were first simulated in the context of patient-specific anatomies, lead placements and programming parameters from three ET patients who had been implanted with Medtronic 3389 DBS leads. The models showed that in these patients, complete suppression of tremor was associated most closely with activating an average of 62% of the cerebellothalamic afferent input into Vim (n = 10), while persistent paresthesias were associated with activating 35% of the medial lemniscal tract input into Vc thalamus (n = 12). The dDBS lead design demonstrated superior targeting of the cerebello-thalamo-cortical pathway, especially in cases of misaligned DBS leads. Given the close proximity of Vim to Vc thalamus, the models suggest that dDBS will enable clinicians to more effectively sculpt current through and around thalamus in order to achieve a more consistent therapeutic effect without inducing side-effects.

A multi-modal approach to computer-assisted deep brain stimulation trajectory planning

PURPOSE

Both frame-based and frameless approaches to deep brain stimulation (DBS) require planning of insertion trajectories that mitigate hemorrhagic risk and loss of neurological function. Currently, this is done by manual inspection of multiple potential electrode trajectories on MR-imaging data. We propose and validate a method for computer-assisted DBS trajectory planning.

METHOD

Our framework integrates multi-modal MRI analysis (T1w, SWI, TOF-MRA) to compute suitable DBS trajectories that optimize the avoidance of specific critical brain structures. A cylinder model is used to process each trajectory and to evaluate complex surgical constraints described via a combination of binary and fuzzy segmented datasets. The framework automatically aggregates the multiple constraints into a unique ranking of recommended low-risk trajectories. Candidate trajectories are represented as a few well-defined cortical entry patches of best-ranked trajectories and presented to the neurosurgeon for final trajectory selection.

RESULTS

The proposed algorithm permits a search space containing over 8,000 possible trajectories to be processed in less than 20 s. A retrospective analysis on 14 DBS cases of patients with severe Parkinson's disease reveals that our framework can improve the simultaneous optimization of many pre-formulated surgical constraints. Furthermore, all automatically computed trajectories were evaluated by two neurosurgeons, were judged suitable for surgery and, in many cases, were judged preferable or equivalent to the manually planned trajectories used during the operation.

CONCLUSIONS

This work provides neurosurgeons with an intuitive and flexible decision-support system that allows objective and patient-specific optimization of DBS lead trajectories, which should improve insertion safety and reduce surgical time.

Influence of white matter conductivity anisotropy on electric field strength induced by electroconvulsive therapy

The goal of this study is to investigate the influence of white matter conductivity anisotropy on the electric field strength induced by electroconvulsive therapy (ECT). We created an anatomically-realistic finite element human head model incorporating tissue heterogeneity and white matter conductivity anisotropy using structural magnetic resonance imaging (MRI) and diffusion tensor MRI data. The electric field spatial distributions of three conventional ECT electrode placements (bilateral, bifrontal, and right unilateral) and an experimental electrode configuration, focal electrically administered seizure therapy (FEAST), were computed. A quantitative comparison of the electric field strength was subsequently performed in specific brain regions of interest thought to be associated with side effects of ECT (e.g., hippocampus and in-sula). The results show that neglecting white matter conductivity anisotropy yields a difference up to 19%, 25% and 34% in electric field strength in the whole brain, hippocampus, and insula, respectively. This study suggests that white matter conductivity anisotropy should be taken into account in ECT electric field models.

Deep brain stimulation for treatment-resistant depression: efficacy, safety and mechanisms of action

Deep brain stimulation (DBS), a neuromodulation therapy that has been used successfully in the treatment of symptoms associated with movement disorders, has recently undergone clinical trials for individuals suffering from treatment-resistant depression (TRD). Although the small patient numbers and open label study design limit our ability to identify optimum targets and make definitive conclusions about treatment efficacy, a review of the published research demonstrates significant reductions in depressive symptomatology and high rates of remission in a severely treatment-resistant patient group. Despite these encouraging results, an incomplete understanding of the mechanisms of action underlying the therapeutic effects of DBS for TRD is highlighted, paralleling the incomplete understanding of the neuroanatomy of mood regulation and treatment resistance. Proposed mechanisms of action include short and long-term local effects of stimulation at the neuronal level, to modulation of neural network activity.

High frequency deep brain stimulation attenuates subthalamic and cortical rhythms in Parkinson's disease

Parkinson's disease (PD) is marked by excessive synchronous activity in the beta (8–35 Hz) band throughout the cortico-basal ganglia network. The optimal location of high frequency deep brain stimulation (HF DBS) within the subthalamic nucleus (STN) region and the location of maximal beta hypersynchrony are currently matters of debate. Additionally, the effect of STN HF DBS on neural synchrony in functionally connected regions of motor cortex is unknown and is of great interest. Scalp EEG studies demonstrated that stimulation of the STN can activate motor cortex antidromically, but the spatial specificity of this effect has not been examined. The present study examined the effect of STN HF DBS on neural synchrony within the cortico-basal ganglia network in patients with PD. We measured local field potentials dorsal to and within the STN of PD patients, and additionally in the motor cortex in a subset of these patients. We used diffusion tensor imaging (DTI) to guide the placement of subdural cortical surface electrodes over the DTI-identified origin of the hyperdirect pathway (HDP) between motor cortex and the STN. The results demonstrated that local beta power was attenuated during HF DBS both dorsal to and within the STN. The degree of attenuation was monotonic with increased DBS voltages in both locations, but this voltage-dependent effect was greater in the central STN than dorsal to the STN (p < 0.05). Cortical signals over the estimated origin of the HDP also demonstrated attenuation of beta hypersynchrony during DBS dorsal to or within STN, whereas signals from non-specific regions of motor cortex were not attenuated. The spatially-specific suppression of beta synchrony in the motor cortex support the hypothesis that DBS may treat Parkinsonism by reducing excessive synchrony in the functionally connected sensorimotor network.

Biomedical Visual Computing: Case Studies and Challenges

Advances in computational geometric modeling, imaging, and simulation let researchers build and test models of increasing complexity, generating unprecedented amounts of data. As recent research in biomedical applications illustrates, visualization will be critical in making this vast amount of data usable; it's also fundamental to understanding models of complex phenomena.

"Connectomic surgery": diffusion tensor imaging (DTI) tractography as a targeting modality for surgical modulation of neural networks

Deep brain stimulation (DBS) is being used to treat a growing number of neurological disorders. Until recently, DBS has been thought to act mainly by suppressing local neuronal activity, essentially producing a functional lesion. Numerous studies are now demonstrating that DBS has widespread network effects mediated by white matter pathways. The new science of connectomics aims to map the connectivity between brain regions in health and disease. Targeting DBS specifically to pathways which exhibit pathological connectivity could greatly expand the possibilities for treating brain diseases. This brief review examines the current state of brain imaging for visualization of these networks and describes how DBS might be used to restore normal connectivity in pathological states.

Neural targets for relieving parkinsonian rigidity and bradykinesia with pallidal deep brain stimulation

Clinical evidence has suggested that subtle changes in deep brain stimulation (DBS) settings can have differential effects on bradykinesia and rigidity in patients with Parkinson's disease. In this study, we first investigated the degree of improvement in bradykinesia and rigidity during targeted globus pallidus DBS in three 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated rhesus macaques. Behavioral outcomes of DBS were then coupled with detailed, subject-specific computational models of neurons in the globus pallidus internus (GPi), globus pallidus externus (GPe), and internal capsule (IC) to determine which neuronal pathways when modulated with high-frequency electrical stimulation best correlate with improvement in motor symptoms. The modeling results support the hypothesis that multiple neuronal pathways can underlie the therapeutic effect of DBS on parkinsonian bradykinesia and rigidity. Across all three subjects, improvements in rigidity correlated most strongly with spread of neuronal activation into IC, driving a small percentage of fibers within this tract (<10% on average). The most robust effect on bradykinesia resulted from stimulating a combination of sensorimotor axonal projections within the GP, specifically at the site of the medial medullary lamina. Thus the beneficial effects of pallidal DBS for parkinsonian symptoms may occur from multiple targets within and near the target nucleus.

Microelectrode targeting of the subthalamic nucleus for deep brain stimulation surgery

Though microelectrode recordings likely increase the risks and costs of DBS, incremental improvement in accuracy may translate into improved outcomes that justify these risks and costs. Clinically based, controlled studies to resolve these issues are problematic. Until such studies are reported, physicians must rely on indirect evidence. The spatial variability of physiologically defined optimal targets, as determined by microelectrode recording (MER), necessary for targeting the STN was calculated. Study of the effectiveness of a MER algorithm was based on the number of penetrations required. The radius of the volume with a 99% chance of including the physiologically defined optimal target, based on 108 cases, was 4.5 mm. This is larger than the estimated radius of the DBS effect, which is variously estimated to be 2 to 3.9 mm. The 99% confidence radius in the plane orthogonal to the lead was 3.2 mm. In 70% of cases, the imaging-based trajectories corresponded to the physiologically defined optimal target. For the remaining 30% of cases, 70% required only a single additional MER tract. The radii of the 99% confidence volume and area may be larger than the effective radius of stimulation. Surveying within those volumes or areas is therefore necessary to assure that at least 99% of cases will cover the physiologically defined target. The MER algorithm was robust in detecting the physiologically defined optimal target. However, there are significant caveats in interpretation of the data.

CranialVault and its CRAVE tools: a clinical computer assistance system for deep brain stimulation (DBS) therapy

A number of methods have been developed to assist surgeons at various stages of deep brain stimulation (DBS) therapy. These include construction of anatomical atlases, functional databases, and electrophysiological atlases and maps. But, a complete system that can be integrated into the clinical workflow has not been developed. In this paper we present a system designed to assist physicians in pre-operative target planning, intra-operative target refinement and implantation, and post-operative DBS lead programming. The purpose of this system is to centralize the data acquired a the various stages of the procedure, reduce the amount of time needed at each stage of the therapy, and maximize the efficiency of the entire process. The system consists of a central repository (CranialVault), of a suite of software modules called CRAnialVault Explorer (CRAVE) that permit data entry and data visualization at each stage of the therapy, and of a series of algorithms that permit the automatic processing of the data. The central repository contains image data for more than 400 patients with the related pre-operative plans and position of the final implants and about 10,550 electrophysiological data points (micro-electrode recordings or responses to stimulations) recorded from 222 of these patients. The system has reached the stage of a clinical prototype that is being evaluated clinically at our institution. A preliminary quantitative validation of the planning component of the system performed on 80 patients who underwent the procedure between January 2009 and December 2009 shows that the system provides both timely and valuable information.

Regional electric field induced by electroconvulsive therapy in a realistic finite element head model: influence of white matter anisotropic conductivity

We present the first computational study investigating the electric field (E-field) strength generated by various electroconvulsive therapy (ECT) electrode configurations in specific brain regions of interest (ROIs) that have putative roles in the therapeutic action and/or adverse side effects of ECT. This study also characterizes the impact of the white matter (WM) conductivity anisotropy on the E-field distribution. A finite element head model incorporating tissue heterogeneity and WM anisotropic conductivity was constructed based on structural magnetic resonance imaging (MRI) and diffusion tensor MRI data. We computed the spatial E-field distributions generated by three standard ECT electrode placements including bilateral (BL), bifrontal (BF), and right unilateral (RUL) and an investigational electrode configuration for focal electrically administered seizure therapy (FEAST). The key results are that (1) the median E-field strength over the whole brain is 3.9, 1.5, 2.3, and 2.6 V/cm for the BL, BF, RUL, and FEAST electrode configurations, respectively, which coupled with the broad spread of the BL E-field suggests a biophysical basis for observations of superior efficacy of BL ECT compared to BF and RUL ECT; (2) in the hippocampi, BL ECT produces a median E-field of 4.8 V/cm that is 1.5-2.8 times stronger than that for the other electrode configurations, consistent with the more pronounced amnestic effects of BL ECT; and (3) neglecting the WM conductivity anisotropy results in E-field strength error up to 18% overall and up to 39% in specific ROIs, motivating the inclusion of the WM conductivity anisotropy in accurate head models. This computational study demonstrates how the realistic finite element head model incorporating tissue conductivity anisotropy provides quantitative insight into the biophysics of ECT, which may shed light on the differential clinical outcomes seen with various forms of ECT, and may guide the development of novel stimulation paradigms with improved risk/benefit ratio.

Subspace matching thalamic microstimulation to tactile evoked potentials in rat somatosensory cortex

We show experimental results that the evoked local field potentials of the rat somatosensory cortex from natural tactile touch of forepaw digits and matched thalamic microstimulation can be qualitatively and quantitively similar. In ongoing efforts to optimize the microstimulation settings (e.g., location, amplitude, etc.) to match the natural response, we investigate whether subspace projection methods, specifically the eigenface approach proposed in the computer vision community (Turk and Pentland 1991 [1]), can be used to choose the parameters of microstimulation such that the response matches a single tactile touch realization. Since the evoked potentials from multiple electrodes are high dimensional spatio-temporal data, the subspace projections improve computational efficiency and can reduce the effect of noisy realizations. In computing the PCA projections we use the peristimulus averages instead of the realizations. The dataset is pruned of unreliable stimulation types. A new subspace is computed for the pruned stimulation type, and is used to estimate a sequence of microstimulations to best match the natural responses. This microstimulation sequence is applied in vivo and quantitative analysis shows that per realization matching does statistically better than choosing randomly from the pruned subset.

Contribution of dielectric dispersions to voltage waveforms arising from electrical stimulation

This study presents an analysis of the effect of incorporating a subset of the complete set of dielectric dispersions in electric field models of implanted electrical stimulation. An analytic volume conductor model was used to determine the voltage waveform at a distance of 5mm from a point current stimulus for 17 different biological tissues. The RMS error of the voltage waveform resulting from the incorporation of a subset of all poles with respect to the voltage waveform resulting from the incorporation of the complete set of dispersive poles was calculated. The stimulus amplitude necessary to elicit action potential propagation in a myelinated mammalian nerve fibre in each of the dispersive models was also determined using a multi-compartment cable axon model. It was found that, for all tissues, removal of dispersions with pole frequencies greater than 1 MHz had a negligible effect on the threshold stimulation amplitude, suggesting that they may be neglected when constructing volume conductor models of electrical stimulation. However, removal of low-frequency dispersions below 1 MHz resulted in greater reductions in the threshold stimulus amplitudes necessary for activation of axons, with errors of up to 86% observed.

Diffusion tensor imaging and neuromodulation: DTI as key technology for deep brain stimulation

Diffusion tensor imaging (DTI) is more than just a useful adjunct to invasive techniques like optogenetics which recently have tremendously influenced our understanding of the mechanisms of deep brain stimulation (DBS). In combination with other technologies, DTI helps us to understand which parts of the brain tissue are connected to others and which ones are truly influenced with neuromodulation. The complex interaction of DBS with the surrounding tissues-scrutinized with DTI-allows to create testable hypotheses that can explain network interactions. Those interactions are vital for our understanding of the net effects of neuromodulation. This work naturally was first done in the field of movement disorder surgery, where a lot of experience regarding therapeutic effects and only a short latency between initiation of neuromodulation and alleviation of symptoms exist. This chapter shows the journey over the past 10 years with first applications in DBS toward current research in affect regulating network balances and their therapeutic alterations with the neuromodulation technology.

Return of bradykinesia after subthalamic stimulation ceases: relationship to electrode location

In 20 subjects we quantified the rate at which subthalamic nucleus deep brain stimulation effects on Parkinson's bradykinesia "washed-out" after stimulation ceased. We found that wash-out was a two-step process, consisting of an initial fast decrease in stimulation's therapeutic effect, followed by a further, slow decline. Moreover, the relative contribution of the fast and slow components differed between patients. Finally, we found that lateral stimulation caused more of the fast-decaying component, while medial stimulation caused more of the slow-decaying component. This implies the existence of at least two separate mechanisms by which subthalamic nucleus deep brain stimulation improves bradykinesia, associated with activation of spatially separate zones in the vicinity of the subthalamic nucleus.

Electrical stimulation for epilepsy: experimental approaches

Direct electrical stimulation of the brain is an increasingly popular means of treating refractory epilepsy. Although there has been moderate success in human trials, the rate of seizure freedom does not yet compare favorably to resective surgery. It therefore remains critical to advance experimental investigations aimed toward understanding brain stimulation and its utility. This article introduces the concepts necessary for understanding these experimental studies, describing recording and stimulation technology, animal models of epilepsy, and various subcortical targets of stimulation. Bidirectional and closed-loop device technologies are also highlighted, along with the challenges presented by their experimental use.

Three-dimensional visualization of subdural electrodes for presurgical planning

BACKGROUND

Accurate localization and visualization of subdural electrodes implanted for intracranial electroencephalography in cases of medically refractory epilepsy remains a challenging clinical problem.

OBJECTIVE

We introduce a technique for creating accurate 3-dimensional (3D) brain models with electrode overlays, ideal for resective surgical planning.

METHODS

Our procedure uses postimplantation magnetic resonance imaging (MRI) and computed tomographic (CT) imaging to create 3D models of compression-affected brain combined with intensity-thresholded CT-derived electrode models using freely available software. Footprints, or "shadows," beneath electrodes are also described for better visualization of sulcus-straddling electrodes. Electrode models were compared with intraoperative photography for validation.

RESULTS

Realistic representations of intracranial electrode positions on patient-specific postimplantation MRI brain renderings were reliably created and proved accurate when compared with photographs. Electrodes placed interhemispherically were also visible with our rendering technique. Electrode shadows were useful in locating electrodes that straddle sulci.

CONCLUSION

We present an accurate method for visualizing subdural electrodes on brain compression effected 3D models that serves as an ideal platform for surgical planning.

Axonal pathways linked to therapeutic and nontherapeutic outcomes during psychiatric deep brain stimulation

OBJECTIVE

The underlying hypothesis of our work is that specific clinical neuropsychiatric benefits can be achieved by selective activation of specific axonal pathways during deep brain stimulation (DBS). As such, the goal of this study was to develop a method for identifying axonal pathways whose activation is most likely necessary for achieving therapeutic benefits during DBS.

EXPERIMENTAL DESIGN

Our approach combined clinical data, diffusion tensor tractography, and computer models of patient-specific neurostimulation to identify particular axonal pathways activated by DBS and determine their correlations with individual clinical outcome measures. We used this method to evaluate a cohort of seven treatment-resistant depression patients treated with DBS of the ventral anterior internal capsule and ventral striatum (VC/VS).

PRINCIPAL OBSERVATIONS

Clinical responders exhibited five axonal pathways that were consistently activated by DBS. All five pathways coursed lateral and medial to the VS or dorsal and lateral to the nucleus accumbens; however, details of their specific trajectories differed. Similarly, one common pathway was identified across nonresponders.

CONCLUSIONS

Our method and preliminary results provide important background for studies aiming to expand scientific characterization of neural circuitry associated with specific psychiatric outcomes from DBS. Furthermore, identification of pathways linked to therapeutic benefit provides opportunities to improve clinical selection of surgical targets and stimulation settings for DBS devices.

Steering deep brain stimulation fields using a high resolution electrode array

Deep brain stimulation (DBS) therapy relies on electrical stimulation of neuronal elements in small brain targets. However, the lack of fine spatial control over field distributions in current systems implies that stimulation easily spreads into adjacent structures that may induce adverse side-effects. This study investigates DBS field steering using a novel DBS lead design carrying a high-resolution electrode array. We apply computational models to simulate voltage distributions and DBS activation volumes in order to theoretically assess the potential of field steering in DBS. Our computational analysis demonstrates that the DBS-array is capable of accurately displacing activation volumes with sub-millimeter precision. Our findings demonstrate that future systems for DBS therapy may provide for more accurate target coverage than currently available systems achieve.

Probabilistic analysis of activation volumes generated during deep brain stimulation

Deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD) and shows great promise for the treatment of several other disorders. However, while the clinical analysis of DBS has received great attention, a relative paucity of quantitative techniques exists to define the optimal surgical target and most effective stimulation protocol for a given disorder. In this study we describe a methodology that represents an evolutionary addition to the concept of a probabilistic brain atlas, which we call a probabilistic stimulation atlas (PSA). We outline steps to combine quantitative clinical outcome measures with advanced computational models of DBS to identify regions where stimulation-induced activation could provide the best therapeutic improvement on a per-symptom basis. While this methodology is relevant to any form of DBS, we present example results from subthalamic nucleus (STN) DBS for PD. We constructed patient-specific computer models of the volume of tissue activated (VTA) for 163 different stimulation parameter settings which were tested in six patients. We then assigned clinical outcome scores to each VTA and compiled all of the VTAs into a PSA to identify stimulation-induced activation targets that maximized therapeutic response with minimal side effects. The results suggest that selection of both electrode placement and clinical stimulation parameter settings could be tailored to the patient's primary symptoms using patient-specific models and PSAs.

Patient-specific analysis of the relationship between the volume of tissue activated during DBS and verbal fluency

Deep brain stimulation (DBS) for the treatment of advanced Parkinson's disease involves implantation of a lead with four small contacts usually within the subthalamic nucleus (STN) or globus pallidus internus (GPi). While generally safe from a cognitive standpoint, STN DBS has been commonly associated with a decrease in the speeded production of words, a skill referred to as verbal fluency. Virtually all studies comparing presurgical to postsurgical verbal fluency performance have detected a decrease with DBS. The decline may be attributable in part to the surgical procedures, yet the relative contributions of stimulation effects are not known. In the present study, we used patient-specific DBS computer models to investigate the effects of stimulation on verbal fluency performance. Specifically, we investigated relationships of the volume and locus of activated STN tissue to verbal fluency outcome. Stimulation of different electrode contacts within the STN did not affect total verbal fluency scores. However, models of activation revealed subtle relationships between the locus and volume of activated tissue and verbal fluency performance. At ventral contacts, more tissue activation inside the STN was associated with decreased letter fluency performance. At optimal contacts, more tissue activation within the STN was associated with improved letter fluency performance. These findings suggest subtle effects of stimulation on verbal fluency performance, consistent with the functional nonmotor subregions/somatotopy of the STN.

Individualized model predicts brain current flow during transcranial direct-current stimulation treatment in responsive stroke patient

Although numerous published reports have demonstrated the beneficial effects of transcranial direct-current stimulation (tDCS) on task performance, fundamental questions remain regarding the optimal electrode configuration on the scalp. Moreover, it is expected that lesioned brain tissue will influence current flow and should therefore be considered (and perhaps leveraged) in the design of individualized tDCS therapies for stroke. The current report demonstrates how different electrode configurations influence the flow of electrical current through brain tissue in a patient who responded positively to a tDCS treatment targeting aphasia. The patient, a 60-year-old man, sustained a left hemisphere ischemic stroke (lesion size = 87.42 mL) 64 months before his participation. In this study, we present results from the first high-resolution (1 mm(3)) model of tDCS in a brain with considerable stroke-related damage; the model was individualized for the patient who received anodal tDCS to his left frontal cortex with the reference cathode electrode placed on his right shoulder. We modeled the resulting brain current flow and also considered three additional reference electrode positions: right mastoid, right orbitofrontal cortex, and a "mirror" configuration with the anode over the undamaged right cortex. Our results demonstrate the profound effect of lesioned tissue on resulting current flow and the ability to modulate current pattern through the brain, including perilesional regions, through electrode montage design. The complexity of brain current flow modulation by detailed normal and pathologic anatomy suggest: (1) That computational models are critical for the rational interpretation and design of individualized tDCS stroke-therapy; and (2) These models must accurately reproduce head anatomy as shown here.

Modeling deep brain stimulation: point source approximation versus realistic representation of the electrode

Deep brain stimulation (DBS) has emerged as an effective treatment for movement disorders; however, the fundamental mechanisms by which DBS works are not well understood. Computational models of DBS can provide insights into these fundamental mechanisms and typically require two steps: calculation of the electrical potentials generated by DBS and, subsequently, determination of the effects of the extracellular potentials on neurons. The objective of this study was to assess the validity of using a point source electrode to approximate the DBS electrode when calculating the thresholds and spatial distribution of activation of a surrounding population of model neurons in response to monopolar DBS. Extracellular potentials in a homogenous isotropic volume conductor were calculated using either a point current source or a geometrically accurate finite element model of the Medtronic DBS 3389 lead. These extracellular potentials were coupled to populations of model axons, and thresholds and spatial distributions were determined for different electrode geometries and axon orientations. Median threshold differences between DBS and point source electrodes for individual axons varied between -20.5% and 9.5% across all orientations, monopolar polarities and electrode geometries utilizing the DBS 3389 electrode. Differences in the percentage of axons activated at a given amplitude by the point source electrode and the DBS electrode were between -9.0% and 12.6% across all monopolar configurations tested. The differences in activation between the DBS and point source electrodes occurred primarily in regions close to conductor-insulator interfaces and around the insulating tip of the DBS electrode. The robustness of the point source approximation in modeling several special cases--tissue anisotropy, a long active electrode and bipolar stimulation--was also examined. Under the conditions considered, the point source was shown to be a valid approximation for predicting excitation of populations of neurons in response to DBS.

Mapping Go-No-Go performance within the subthalamic nucleus region

The basal ganglia are thought to be important in the selection of wanted and the suppression of unwanted motor patterns according to explicit rules (i.e. response inhibition). The subthalamic nucleus has been hypothesized to play a particularly critical role in this function. Deep brain stimulation of the subthalamic nucleus in individuals with Parkinson's disease has been used to test this hypothesis, but results have been variable. Based on current knowledge of the anatomical organization of the subthalamic nucleus, we propose that the location of the contacts used in deep brain stimulation could explain variability in the effects of deep brain stimulation of the subthalamic nucleus on response inhibition tasks. We hypothesized that stimulation affecting the dorsal subthalamic nucleus (connected to the motor cortex) would be more likely to affect motor symptoms of Parkinson's disease, and stimulation affecting the ventral subthalamic nucleus (connected to higher order cortical regions) would be more likely to affect performance on a response inhibition task. We recruited 10 individuals with Parkinson's disease and bilateral deep brain stimulation of the subthalamic nucleus with one contact in the dorsal and another in the ventral subthalamic region on one side of the brain. Patients were tested with a Go-No-Go task and a motor rating scale in three conditions: stimulation off, unilateral dorsal stimulation and unilateral ventral stimulation. Both dorsal and ventral stimulation improved motor symptoms, but only ventral subthalamic stimulation affected Go-No-Go performance, decreasing hits and increasing false alarms, but not altering reaction times. These results suggest that the ventral subthalamic nucleus is involved in the balance between appropriate selection and inhibition of prepotent responses in cognitive paradigms, but that a wide area of the subthalamic nucleus region is involved in the motor symptoms of Parkinson's disease. This finding has implications for resolving inconsistencies in previous research, highlights the role of the ventral subthalamic nucleus region in response inhibition and suggests an approach for the clinical optimization of deep brain stimulation of the subthalamic nucleus for both motor and cognitive functions.

Electroconvulsive therapy stimulus parameters: rethinking dosage

In this article, we review the parameters that define the electroconvulsive therapy (ECT) electrical stimulus and discuss their biophysical roles. We also present the summary metrics of charge and energy that are conventionally used to describe the dose of ECT and the rules commonly deployed to individualize the dose for each patient. We then highlight the limitations of these summary metrics and dosing rules in that they do not adequately capture the roles of the distinct stimulus parameters. Specifically, there is strong theoretical and empirical evidence that stimulus parameters (pulse amplitude, shape, and width, and train frequency, directionality, polarity, and duration) exert unique neurobiological effects that are important for understanding ECT outcomes. Consideration of the distinct stimulus parameters, in conjunction with electrode placement, is central to further optimization of ECT dosing paradigms to improve the risk-benefit ratio. Indeed, manipulation of specific parameters, such as reduction of pulse width and increase in number of pulses, has already resulted in dramatic reduction of adverse effects, while maintaining efficacy. Furthermore, the manipulation of other parameters, such as current amplitude, which are commonly held at fixed, high values, might be productively examined as additional means of targeting and individualizing the stimulus, potentially reducing adverse effects. We recommend that ECT dose be defined using all stimulus parameters rather than a summary metric. All stimulus parameters should be noted in treatment records and published reports. To enable research on optimization of dosing paradigms, we suggest that ECT devices provide capabilities to adjust and display all stimulus parameters.

Spatial steering of deep brain stimulation volumes using a novel lead design

OBJECTIVE

To investigate steering the volume of activated tissue (VTA) with deep brain stimulation (DBS) using a novel high spatial-resolution lead design.

METHODS

We examined the effect of asymmetric current-injection across the DBS-array on the VTA. These predictions were then evaluated acutely in a non-human primate implanted with the DBS-array, using motor side-effect thresholds as the metric for estimating VTA asymmetries.

RESULTS

Simulations show the DBS-array, with electrodes arranged together in a cylindrical configuration, can generate field distributions equivalent to commercial DBS leads, and these field distributions can be modulated using field-steering methods. Stimulation with implanted DBS-arrays showed directionally-selective muscle activation, presumably through spread of stimulation fields into portions of the corticospinal tract lying in the internal capsule.

CONCLUSIONS

Our computational and experimental studies demonstrate that the DBS-array is capable of spatially selective stimulation. Displacing VTAs away from the lead's axis can be achieved using a single simple and intuitive control parameter.

SIGNIFICANCE

Optimal DBS likely requires non-uniform VTAs that may differentially affect a nucleus or fiber pathway. The DBS-array allows positioning VTAs with sub-millimeter precision, which is especially relevant for those patients with DBS leads placed in sub-optimal locations. This may present clinicians with an additional degree of freedom to optimize the DBS therapy.

Effect of dispersive conductivity and permittivity in volume conductor models of deep brain stimulation

The aim of this study was to examine the effect of dispersive tissue properties on the volume-conducted voltage waveforms and volume of tissue activated during deep brain stimulation. Inhomogeneous finite-element models were developed, incorporating a distributed dispersive electrode-tissue interface and encapsulation tissue of high and low conductivity, under both current-controlled and voltage-controlled stimulation. The models were used to assess the accuracy of capacitive models, where material properties were estimated at a single frequency, with respect to the full dispersive models. The effect of incorporating dispersion in the electrical conductivity and relative permittivity was found to depend on both the applied stimulus and the encapsulation tissue surrounding the electrode. Under current-controlled stimulation, and during voltage-controlled stimulation when the electrode was surrounded by high-resistivity encapsulation tissue, the dispersive material properties of the tissue were found to influence the voltage waveform in the tissue, indicated by RMS errors between the capacitive and dispersive models of 20%-38% at short pulse durations. When the dispersive model was approximated by a capacitive model, the accuracy of estimates of the volume of tissue activated was very sensitive to the frequency at which material properties were estimated. When material properties at 1 kHz were used, the error in the volume of tissue activated by capacitive approximations was reduced to -4.33% and 11.10%, respectively, for current-controlled and voltage-controlled stimulations, with higher errors observed when higher or lower frequencies were used.

Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation

Deep brain stimulation (DBS) of the subthlamic nucleus (STN) represents an effective treatment for medically refractory Parkinson's disease; however, understanding of its effects on basal ganglia network activity remains limited. We constructed a computational model of the subthalamopallidal network, trained it to fit in vivo recordings from parkinsonian monkeys, and evaluated its response to STN DBS. The network model was created with synaptically connected single compartment biophysical models of STN and pallidal neurons, and stochastically defined inputs driven by cortical beta rhythms. A least mean square error training algorithm was developed to parameterize network connections and minimize error when compared to experimental spike and burst rates in the parkinsonian condition. The output of the trained network was then compared to experimental data not used in the training process. We found that reducing the influence of the cortical beta input on the model generated activity that agreed well with recordings from normal monkeys. Further, during STN DBS in the parkinsonian condition the simulations reproduced the reduction in GPi bursting found in existing experimental data. The model also provided the opportunity to greatly expand analysis of GPi bursting activity, generating three major predictions. First, its reduction was proportional to the volume of STN activated by DBS. Second, GPi bursting decreased in a stimulation frequency dependent manner, saturating at values consistent with clinically therapeutic DBS. And third, ablating STN neurons, reported to generate similar therapeutic outcomes as STN DBS, also reduced GPi bursting. Our theoretical analysis of stimulation induced network activity suggests that regularization of GPi firing is dependent on the volume of STN tissue activated and a threshold level of burst reduction may be necessary for therapeutic effect.

Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the surgical therapy of choice for medically intractable Parkinson's disease. However, quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is limited. Recently, computational models of varying levels of complexity have been used to study the neural response to DBS. The goal of this study was to evaluate the quantitative impact of incrementally incorporating increasing levels of complexity into computer models of STN DBS. Our analysis focused on the direct activation of experimentally measureable fiber pathways within the internal capsule (IC). Our model system was customized to an STN DBS patient and stimulation thresholds for activation of IC axons were calculated with electric field models that ranged from an electrostatic, homogenous, isotropic model to one that explicitly incorporated the voltage-drop and capacitance of the electrode-electrolyte interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to experimental IC activation defined from electromyographic (EMG) recordings from eight different muscle groups in the contralateral arm and leg of the STN DBS patient. Coupled evaluation of the model and experimental data showed that the most realistic predictions of axonal thresholds were achieved with the most detailed model. Furthermore, the more simplistic neurostimulation models substantially overestimated the spatial extent of neural activation.