Computational modeling of deep brain stimulation

Strong connectivity to the sensorimotor cortex predicts clinical effectiveness of thalamic deep brain stimulation in essential tremor

INTRODUCTION

The outcome of thalamic deep brain stimulation (DBS) for essential tremor (ET) varies, probably due to the difficulty in identifying the optimal target for DBS placement. Recent approaches compared the clinical response with a connectivity-based segmentation of the target area. However, studies are contradictory by indicating the connectivity to the primary motor cortex (M1) or to the premotor/supplementary motor cortex (SMA) to be therapeutically relevant.

OBJECTIVE

To identify the connectivity profile that corresponds to clinical effective targeting of DBS for ET.

METHODS

Patient-specific probabilistic diffusion tensor imaging was performed in 20 ET patients with bilateral thalamic DBS. Following monopolar review, the stimulation response was classified for the most effective contact in each hemisphere as complete vs. incomplete upper limb tremor suppression (40 assessments). Finally, the connectivity profiles of these contacts within the cortical and cerebellar tremor network were estimated and compared between groups.

RESULTS

The active contacts that led to complete (n = 25) vs. incomplete (n = 15) tremor suppression showed significantly higher connectivity to M1 (p < 0.001), somatosensory cortex (p = 0.008), anterior lobe of the cerebellum (p = 0.026) and SMA (p = 0.05); with Cohen's (d) effect sizes of 0.53, 0.42, 0.25 and 0.10, respectively. The clinical benefits were achieved without requiring higher stimulation intensities or causing additional side effects.

CONCLUSION

Clinical effectiveness of DBS for ET corresponded to a distributed connectivity profile, with the connection to the sensorimotor cortex being most relevant. Long-term follow-up in larger cohorts and replication in out-of-sample data are necessary to confirm the robustness of these findings.

A deep learning model of dorsal and ventral visual streams for DVSD

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the brain. In particular, Convolutional Neural Networks (CNNs) offer state-of-the-art models of the ventral visual stream. Furthermore, no proposed model estimates the distance between objects as a function of the dorsal stream. In this paper, we present a quantitatively accurate model for the visual system. Specifically, we propose a VeDo-Net model that comprises both ventral and dorsal branches. As in the ventral visual stream, our model recognizes objects. The model also locates and estimates the distance between objects as a spatial relationship task performed by the dorsal stream. One application of the proposed model is in the simulation of visual impairments. In this study, however, we show how the proposed model can simulate the occurrence of dorsal stream impairments such as Autism Spectrum Disorder (ASD) and cerebral visual impairment (CVI). In the end, we explore the impacts of learning on the recovery of the synaptic disruptions of the dorsal visual stream. Results indicated a direct relationship between the positive and negative changes in the weights of the dorsal stream's last layers and the output of the dorsal stream under an allocentric situation. Our results also demonstrate that visual-spatial perception impairments in ASD may be caused by a disturbance in the last layers of the dorsal stream.

Effect of Anisotropic Brain Conductivity on Patient-Specific Volume of Tissue Activation in Deep Brain Stimulation for Parkinson Disease

OBJECTIVE

Deep brain stimulation (DBS) modeling can improve surgical targeting by quantifying the spatial extent of stimulation relative to subcortical structures of interest. A certain degree of model complexity is required to obtain accurate predictions, particularly complexity regarding electrical properties of the tissue around DBS electrodes. In this study, the effect of anisotropy on the volume of tissue activation (VTA) was evaluated in an individualized manner.

METHODS

Tissue activation models incorporating patient-specific tissue conductivity were built for 40 Parkinson disease patients who had received bilateral subthalamic nucleus (STN) DBS. To assess the impact of local changes in tissue anisotropy, one VTA was computed at each electrode contact using identical stimulation parameters. For comparison, VTAs were also computed assuming isotropic tissue conductivity. Stimulation location was considered by classifying the anisotropic VTAs relative to the STN. VTAs were characterized based on volume, spread in three directions, sphericity, and Dice coefficient.

RESULTS

Incorporating anisotropy generated significantly larger and less spherical VTAs overall. However, its effect on VTA size and shape was variable and more nuanced at the individual patient and implantation levels. Dorsal VTAs had significantly higher sphericity than ventral VTAs, suggesting more isotropic behavior. Contrastingly, lateral and posterior VTAs had significantly larger and smaller lateral-medial spreads, respectively. Volume and spread correlated negatively with sphericity.

CONCLUSION

The influence of anisotropy on VTA predictions is important to consider, and varies across patients and stimulation location.

SIGNIFICANCE

This study highlights the importance of considering individualized factors in DBS modeling to accurately characterize the VTA.

Biophysical Principles and Computational Modeling of Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community.

OBJECTIVE

In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS.

RESULTS

Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response.

CONCLUSION

This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

Insights and opportunities for deep brain stimulation as a brain circuit intervention

Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.

Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters

OBJECTIVES

Subthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming.

MATERIALS AND METHODS

A total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months.

RESULTS

Image-based contact prediction showed high interrater reliability (Cohen kappa 0.851-0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0-83.3) and 65% (95% CI 44.9-81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was -0.5 mA (p < 0.001, Wilcoxon paired signed rank test).

CONCLUSIONS

Integration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.

Neuromodulation for treatment-resistant depression: Functional network targets contributing to antidepressive outcomes

Non-invasive brain stimulation is designed to target accessible brain regions that underlie many psychiatric disorders. One such method, transcranial magnetic stimulation (TMS), is commonly used in patients with treatment-resistant depression (TRD). However, for non-responders, the choice of an alternative therapy is unclear and often decided empirically without detailed knowledge of precise circuit dysfunction. This is also true of invasive therapies, such as deep brain stimulation (DBS), in which responses in TRD patients are linked to circuit activity that varies in each individual. If the functional networks affected by these approaches were better understood, a theoretical basis for selection of interventions could be developed to guide psychiatric treatment pathways. The mechanistic understanding of TMS is that it promotes long-term potentiation of cortical targets, such as dorsolateral prefrontal cortex (DLPFC), which are attenuated in depression. DLPFC is highly interconnected with other networks related to mood and cognition, thus TMS likely alters activity remote from DLPFC, such as in the central executive, salience and default mode networks. When deeper structures such as subcallosal cingulate cortex (SCC) are targeted using DBS for TRD, response efficacy has depended on proximity to white matter pathways that similarly engage emotion regulation and reward. Many have begun to question whether these networks, targeted by different modalities, overlap or are, in fact, the same. A major goal of current functional and structural imaging in patients with TRD is to elucidate neuromodulatory effects on the aforementioned networks so that treatment of intractable psychiatric conditions may become more predictable and targeted using the optimal technique with fewer iterations. Here, we describe several therapeutic approaches to TRD and review clinical studies of functional imaging and tractography that identify the diverse loci of modulation. We discuss differentiating factors associated with responders and non-responders to these stimulation modalities, with a focus on mechanisms of action for non-invasive and intracranial stimulation modalities. We advance the hypothesis that non-invasive and invasive neuromodulation approaches for TRD are likely impacting shared networks and critical nodes important for alleviating symptoms associated with this disorder. We close by describing a therapeutic framework that leverages personalized connectome-guided target identification for a stepwise neuromodulation paradigm.

Artifacts Can Be Deceiving: The Actual Location of Deep Brain Stimulation Electrodes Differs from the Artifact Seen on Magnetic Resonance Images

INTRODUCTION

Deep brain stimulation (DBS) is a common treatment for a variety of neurological and psychiatric disorders. Recent studies have highlighted the role of neuroimaging in localizing the position of electrode contacts relative to target brain areas in order to optimize DBS programming. Among different imaging methods, postoperative magnetic resonance imaging (MRI) has been widely used for DBS electrode localization; however, the geometrical distortion induced by the lead limits its accuracy. In this work, we investigated to what degree the difference between the actual location of the lead's tip and the location of the tip estimated from the MRI artifact varies depending on the MRI sequence parameters such as acquisition plane and phase encoding direction, as well as the lead's extracranial configuration. Accordingly, an imaging technique to increase the accuracy of lead localization was devised and discussed.

METHODS

We designed and constructed an anthropomorphic phantom with an implanted DBS system following 18 clinically relevant configurations. The phantom was scanned at a Siemens 1.5 Tesla Aera scanner using a T1MPRAGE sequence optimized for clinical use and a T1TSE sequence optimized for research purposes. We varied slice acquisition plane and phase encoding direction and calculated the distance between the caudal tip of the DBS lead MRI artifact and the actual tip of the lead, as estimated from MRI reference markers.

RESULTS

Imaging parameters and lead configuration substantially altered the difference in the depth of the lead within its MRI artifact on the scale of several millimeters - with a difference as large as 4.99 mm. The actual tip of the DBS lead was found to be consistently more rostral than the tip estimated from the MR image artifact. The smallest difference between the tip of the DBS lead and the tip of the MRI artifact using the clinically relevant sequence (i.e., T1MPRAGE) was found with the sagittal acquisition plane and anterior-posterior phase encoding direction.

DISCUSSION/CONCLUSION

The actual tip of an implanted DBS lead is located up to several millimeters rostral to the tip of the lead's artifact on postoperative MR images. This distance depends on the MRI sequence parameters and the DBS system's extracranial trajectory. MRI parameters may be altered to improve this localization.

Fascicles split or merge every ∼560 microns within the human cervical vagus nerve

Objective.Vagus nerve stimulation (VNS) is Food and Drug Administration-approved for epilepsy, depression, and obesity, and stroke rehabilitation; however, the morphological anatomy of the vagus nerve targeted by stimulatation is poorly understood. Here, we used microCT to quantify the fascicular structure and neuroanatomy of human cervical vagus nerves (cVNs).Approach.We collected eight mid-cVN specimens from five fixed cadavers (three left nerves, five right nerves). Analysis focused on the 'surgical window': 5 cm of length, centered around the VNS implant location. Tissue was stained with osmium tetroxide, embedded in paraffin, and imaged on a microCT scanner. We visualized and quantified the merging and splitting of fascicles, and report a morphometric analysis of fascicles: count, diameter, and area.Main results.In our sample of human cVNs, a fascicle split or merge event was observed every ∼560µm (17.8 ± 6.1 events cm-1). Mean morphological outcomes included: fascicle count (6.6 ± 2.8 fascicles; range 1-15), fascicle diameter (514 ± 142µm; range 147-1360µm), and total cross-sectional fascicular area (1.32 ± 0.41 mm2; range 0.58-2.27 mm).Significance.The high degree of fascicular splitting and merging, along with wide range in key fascicular morphological parameters across humans may help to explain the clinical heterogeneity in patient responses to VNS. These data will enable modeling and experimental efforts to determine the clinical effect size of such variation. These data will also enable efforts to design improved VNS electrodes.

A computational roadmap to electronic drugs

A growing number of complex neurostimulation strategies promise symptom relief and functional recovery for several neurological, psychiatric, and even multi-organ disorders. Although pharmacological interventions are currently the mainstay of treatment, neurostimulation offers a potentially effective and safe alternative, capable of providing rapid adjustment to short-term variation and long-term decline of physiological functions. However, rapid advances made by clinical studies have often preceded the fundamental understanding of mechanisms underlying the interactions between stimulation and the nervous system. In turn, therapy design and verification are largely driven by clinical-empirical evidence. Even with titanic efforts and budgets, it is infeasible to comprehensively explore the multi-dimensional optimization space of neurostimulation through empirical research alone, especially since anatomical structures and thus outcomes vary dramatically between patients. Instead, we believe that the future of neurostimulation strongly depends on personalizable computational tools, i.e. Digital Neuro Twins (DNTs) to efficiently identify effective and safe stimulation parameters. DNTs have the potential to accelerate scientific discovery and hypothesis-driven engineering, and aid as a critical regulatory and clinical decision support tool. We outline here how DNTs will pave the way toward effective, cost-, time-, and risk-limited electronic drugs with a broad application bandwidth.

Neuromodulation devices for heart failure

Autonomic imbalance with a sympathetic dominance is acknowledged to be a critical determinant of the pathophysiology of chronic heart failure with reduced ejection fraction (HFrEF), regardless of the etiology. Consequently, therapeutic interventions directly targeting the cardiac autonomic nervous system, generally referred to as neuromodulation strategies, have gained increasing interest and have been intensively studied at both the pre-clinical level and the clinical level. This review will focus on device-based neuromodulation in the setting of HFrEF. It will first provide some general principles about electrical neuromodulation and discuss specifically the complex issue of dose-response with this therapeutic approach. The paper will thereafter summarize the rationale, the pre-clinical and the clinical data, as well as the future prospectives of the three most studied form of device-based neuromodulation in HFrEF. These include cervical vagal nerve stimulation (cVNS), baroreflex activation therapy (BAT), and spinal cord stimulation (SCS). BAT has been approved by the Food and Drug Administration for use in patients with HfrEF, while the other two approaches are still considered investigational; VNS is currently being investigated in a large phase III Study.

True location of deep brain stimulation electrodes differs from what is seen on postoperative magnetic resonance images: An anthropomorphic phantom study

Deep brain stimulation (DBS) is an established yet growing treatment for a range of neurological and psychiatric disorders. Over the last decade, numerous studies have underscored the effect of electrode placement on the clinical outcome of DBS. As a result, imaging is now extensively used for DBS electrode localization, even though the accuracy of different modalities in determining the true coordinates of DBS electrodes is less explored. Postoperative magnetic resonance imaging (MRI) is a gold standard method for DBS electrode localization, however, the geometrical distortion induced by the lead's artifact could limit the accuracy. In this work, we investigated to what degree the difference between the true location of the lead's tip and the location of the tip estimated from the MRI artifact varies depending on the MRI sequence parameters, acquisition plane, phase encoding direction, and the implant"s extracranial trajectory. Clinical Relevance- Results will help researchers and clinicians to estimate the true location of DBS leads and contacts from postoperative MRI scans.

Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters

BACKGROUND

Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses.

OBJECTIVE

To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated.

METHODS

We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites).

RESULTS

Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation.

CONCLUSION

These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.

Brain Stimulation Treatments in Epilepsy: Basic Mechanisms and Clinical Advances

Drug-resistant epilepsy, characterized by ongoing seizures despite appropriate trials of anti-seizure medications, affects approximately one-third of people with epilepsy. Brain stimulation has recently become available as an alternative treatment option to reduce symptomatic seizures in short and long-term follow-up studies. Several questions remain on how to optimally develop patient-specific treatments and manage therapy over the long term. This review aims to discuss the clinical use and mechanisms of action of Responsive Neural Stimulation and Deep Brain Stimulation in the treatment of epilepsy and highlight recent advances that may both improve outcomes and present new challenges. Finally, a rational approach to device selection is presented based on current mechanistic understanding, clinical evidence, and device features.

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.

OBJECTIVE

Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.

METHODS

Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.

RESULTS

Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.

CONCLUSIONS

Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.

Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models

OBJECTIVE

The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop- imaging data, such analysis using preop- and intraop- imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography.

APPROACH

The study examined six patients who had undergone interventional magnetic resonance (iMR)-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation (VTA) was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift.

MAIN RESULT

The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R=-0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ~2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental.

SIGNIFICANCE

The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.

Modeling of Electric Fields in Individual Imaging Atlas for Capsular Threshold Prediction of Deep Brain Stimulation in Parkinson's Disease: A Pilot Study

Background: Modeling of deep brain stimulation electric fields and anatomy-based software might improve post-operative management of patients with Parkinson's disease (PD) who have benefitted from subthalamic nucleus deep brain stimulation (STN-DBS).

Objective: We compared clinical and software-guided determination of the thresholds for current diffusion to the pyramidal tract, the most frequent limiting side effect in post-operative management of STN-DBS PD patients.

Methods: We assessed monopolar reviews in 16 consecutive STN-DBS PD patients and retrospectively compared clinical capsular thresholds, which had been assessed according to standard clinical practice, to those predicted by volume of tissue activated (VTA) model software. All the modeling steps were performed blinded from patients' clinical evaluations.

Results: At the group level, we found a significant correlation (p = 0.0001) when performing statistical analysis on the z-scored capsular thresholds, but with a low regression coefficient (r = 0.2445). When considering intra-patient analysis, we found significant correlations (p < 0.05) between capsular threshold as modeled with the software and capsular threshold as determined clinically in five patients (31.2%).

Conclusions: In this pilot study, the VTA model software was of limited assistance in identifying capsular thresholds for the whole cohort due to a large inter-patient variability. Clinical testing remains the gold standard in selecting stimulation parameters for STN-DBS in PD.

A computational outlook on neurostimulation

Efficient identification of effective neurostimulation strategies is critical due to the growing number of clinical applications and the increasing complexity of the corresponding technology. In consequence, investigators are encouraged to accelerate translational research of neurostimulation technologies and move quickly to clinical applications. However, this process is hampered by rigorous, but necessary, regulations and lack of a mechanistic understanding of the interactions between electric fields and neural circuits. Here we discuss how computational models have influenced the field of neurostimulation for pain and movement recovery, deep brain stimulation, and even device regulations. Finally, we propose our vision on how computational models will be key to accelerate clinical developments through mechanistic understanding.

Development and testing of implanted carbon electrodes for electromagnetic field mapping during neuromodulation

PURPOSE

Deep brain stimulation electrodes composed of carbon fibers were tested as a means of administering and imaging magnetic resonance electrical impedance tomography (MREIT) currents. Artifacts and heating properties of custom carbon-fiber deep brain stimulation (DBS) electrodes were compared with those produced with standard DBS electrodes.

METHODS

Electrodes were constructed from multiple strands of 7-μm carbon-fiber stock. The insulated carbon electrodes were matched to DBS electrode diameter and contact areas. Images of DBS and carbon electrodes were collected with and without current flow and were compared in terms of artifact and thermal effects in phantoms or tissue samples in 7T imaging conditions. Effects on magnetic flux density and current density distributions were also assessed.

RESULTS

Carbon electrodes produced magnitude artifacts with smaller FWHM values compared to the magnitude artifacts around DBS electrodes in spin echo and gradient echo imaging protocols. DBS electrodes appeared 269% larger than actual size in gradient echo images, in sharp contrast to the negligible artifact observed in diameter-matched carbon electrodes. As expected, larger temperature changes were observed near DBS electrodes during extended RF excitations compared with carbon electrodes in the same phantom. Magnitudes and distribution of magnetic flux density and current density reconstructions were comparable for carbon and DBS electrodes.

CONCLUSION

Carbon electrodes may offer a safer, MR-compatible method for administering neuromodulation currents. Use of carbon-fiber electrodes should allow imaging of structures close to electrodes, potentially allowing better targeting, electrode position revision, and the facilitation of functional imaging near electrodes during neuromodulation.

High resolution transcranial acoustoelectric imaging of current densities from a directional deep brain stimulator

OBJECTIVE

New innovations in deep brain stimulation (DBS) enable directional current steering-allowing more precise electrical stimulation of the targeted brain structures for Parkinson's disease, essential tremor and other neurological disorders. While intra-operative navigation through MRI or CT approaches millimeter accuracy for placing the DBS leads, no existing modality provides feedback of the currents as they spread from the contacts through the brain tissue. In this study, we investigate transcranial acoustoelectric imaging (tAEI) as a new modality to non-invasively image and characterize current produced from a directional DBS lead. tAEI uses ultrasound (US) to modulate tissue resistivity to generate detectable voltage signals proportional to the local currents.

APPROACH

An 8-channel directional DBS lead (Infinity 6172ANS, Abbott Inc) was inserted inside three adult human skulls submerged in 0.9% NaCl. A 2.5 MHz linear array delivered US pulses through the transtemporal window and focused near the contacts on the lead, while a custom amplifier and acquisition system recorded the acoustoelectric (AE) interaction used to generate images.

MAIN RESULTS

tAEI detected monopolar current with stimulation pulses as short as 100 µs with an SNR ranging from 10-27 dB when using safe US pressure (mechanical indices  <0.78) and injected current of ~2 mA peak amplitude. Adjacent contacts were discernable along the length and within each ring of the lead with a mean radial separation between contacts of 2.10 and 1.34 mm, respectively.

SIGNIFICANCE

These results demonstrate the feasibility of tAEI for high resolution mapping of directional DBS currents using clinically-relevant stimulation parameters. This new modality may improve the accuracy for placing the DBS leads, guide calibration and programming, and monitor long-term performance of DBS for treatment of Parkinson's disease.

Consistent linear and non-linear responses to invasive electrical brain stimulation across individuals and primate species with implanted electrodes

BACKGROUND

Electrical neuromodulation via implanted electrodes is used in treating numerous neurological disorders, yet our knowledge of how different brain regions respond to varying stimulation parameters is sparse.

OBJECTIVE/HYPOTHESIS

We hypothesized that the neural response to electrical stimulation is both region-specific and non-linearly related to amplitude and frequency.

METHODS

We examined evoked neural responses following 400 ms trains of 10-400 Hz electrical stimulation ranging from 0.1 to 10 mA. We stimulated electrodes implanted in cingulate cortex (dorsal anterior cingulate and rostral anterior cingulate) and subcortical regions (nucleus accumbens, amygdala) of non-human primates (NHP, N = 4) and patients with intractable epilepsy (N = 15) being monitored via intracranial electrodes. Recordings were performed in prefrontal, subcortical, and temporal lobe locations.

RESULTS

In subcortical regions as well as dorsal and rostral anterior cingulate cortex, response waveforms depended non-linearly on frequency (Pearson's linear correlation r < 0.39), but linearly on current (r > 0.58). These relationships between location, and input-output characteristics were similar in homologous brain regions with average Pearson's linear correlation values r > 0.75 between species and linear correlation values between participants r > 0.75 across frequency and current values per brain region. Evoked waveforms could be described by three main principal components (PCs) which allowed us to successfully predict response waveforms across individuals and across frequencies using PC strengths as functions of current and frequency using brain region specific regression models.

CONCLUSIONS

These results provide a framework for creation of an atlas of input-output relationships which could be used in the principled selection of stimulation parameters per brain region.

A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain

OBJECTIVE

Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region.

APPROACH

We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate.

RESULTS

We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent.

SIGNIFICANCE

Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.

Objective Measures to Characterize the Physiological Effects of Spinal Cord Stimulation in Neuropathic Pain: A Literature Review

OBJECTIVE

The physiological mechanisms behind the therapeutic effects of spinal cord stimulation (SCS) are only partially understood. Our aim was to perform a literature review of studies that used objective measures to characterize mechanisms of action of SCS in neuropathic pain patients.

MATERIALS AND METHODS

We searched the PubMed data base to identify clinical studies that used objective measures to assess the effects of SCS in neuropathic pain. We extracted the study factors (e.g., type of measure, diagnoses, painful area[s], and SCS parameters) and outcomes from the included studies.

RESULTS

We included 67 studies. Of these, 24 studies used neurophysiological measures, 14 studies used functional neuroimaging techniques, three studies used a combination of neurophysiological and functional neuroimaging techniques, 14 studies used quantitative sensory testing, and 12 studies used proteomic, vascular, and/or pedometric measures. Our findings suggest that SCS largely inhibits somatosensory processing and/or spinal nociceptive activity. Our findings also suggest that SCS modulates activity across specific regions of the central nervous system that play a prominent role in the sensory and emotional functions of pain.

CONCLUSIONS

SCS appears to modulate pain via spinal and/or supraspinal mechanisms of action (e.g., pain gating, descending pain inhibition). However, to better understand the mechanisms of action of SCS, we believe that it is necessary to carry out systematic, controlled, and well-powered studies using objective patient measures. To optimize the clinical effectiveness of SCS for neuropathic pain, we also believe that it is necessary to develop and implement patient-specific approaches.

Computational motility models of neurogastroenterology and neuromodulation

The success of neuromodulation therapies, particularly in the brain, spinal cord, and peripheral nerves, has been greatly aided by computational, biophysical models. However, treating gastrointestinal disorders with electrical stimulation has been much less explored, partly because the mode of action of such treatments is unclear, and selection of stimulation parameters is often empirical. Progress in gut neuromodulation is limited by the comparative lack of biophysical models capable of simulating neuromodulation of gastrointestinal function. Here, we review the recently developed biophysical models of electrically-active cells in the gastrointestinal system that contribute to motility. Biophysical models are replacing phenomenologically-defined models due to advancements in electrophysiological characterization of key players in the gut: enteric neurons, smooth muscle fibers, and interstitial cells of Cajal. In this review, we explore existing biophysically-defined cellular and network models that contribute to gastrointestinal motility. We focus on recent models that are laying the groundwork for modeling electrical stimulation of the gastrointestinal system. Developing models of gut neuromodulation will improve our mechanistic understanding of these treatments, leading to better parameterization, selectivity, and efficacy of neuromodulation to treat gastrointestinal disorders. Such models may have direct clinical translation to current neuromodulation therapies, such as sacral nerve stimulation.

Innovations in deep brain stimulation methodology

Deep brain stimulation is a powerful clinical method for movement disorders that no longer respond satisfactorily to pharmacological management, but its progress has been hampered by stagnation in technological procedure solutions and device development. Recently, the combined research efforts of bioengineers, neuroscientists, and clinicians have helped to better understand the mechanisms of deep brain stimulation, and solutions for the translational roadblock are emerging. Here, we define the needs for methodological advances in deep brain stimulation from a neurophysiological perspective and describe technological solutions that are currently evaluated for near-term clinical application. © 2016 International Parkinson and Movement Disorder Society.

Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation

Accurate surgical localization of the varied targets for deep brain stimulation (DBS) is a process undergoing constant evolution, with increasingly sophisticated techniques to allow for highly precise targeting. However, despite the fastidious placement of electrodes into specific structures within the brain, there is increasing evidence to suggest that the clinical effects of DBS are likely due to the activation of widespread neuronal networks directly and indirectly influenced by the stimulation of a given target. Selective activation of these complex and inter-connected pathways may further improve the outcomes of currently treated diseases by targeting specific fiber tracts responsible for a particular symptom in a patient-specific manner. Moreover, the delivery of such focused stimulation may aid in the discovery of new targets for electrical stimulation to treat additional neurological, psychiatric, and even cognitive disorders. As such, advancements in surgical targeting, computational modeling, engineering designs, and neuroimaging techniques play a critical role in this process. This article reviews the progress of these applications, discussing the importance of target localization for DBS, and the role of computational modeling and novel neuroimaging in improving our understanding of the pathophysiology of diseases, and thus paving the way for improved selective target localization using DBS.

Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury

Epidural electrical stimulation of lumbar segments facilitates standing and walking in animal models and humans with spinal cord injury. However, the mechanisms through which this neuromodulation therapy engages spinal circuits remain enigmatic. Using computer simulations and behavioral experiments, we provide evidence that epidural electrical stimulation interacts with muscle spindle feedback circuits to modulate muscle activity during locomotion. Hypothesis-driven strategies emerging from simulations steered the design of stimulation protocols that adjust bilateral hindlimb kinematics throughout gait execution. These stimulation strategies corrected subject-specific gait and balance deficits in rats with incomplete and complete spinal cord injury. The conservation of muscle spindle feedback circuits across mammals suggests that the same mechanisms may facilitate motor control in humans. These results provide a conceptual framework to improve stimulation protocols for clinical applications.

Postmortem volumetric analysis of the nucleus accumbens in male heroin addicts: implications for deep brain stimulation

Deep brain stimulation (DBS) of the nucleus accumbens (NAc) is increasingly investigated in neuropsychiatric disorders. DBS requires computer-assisted 3D planning to implant the stimulation electrode precisely. Recently, there has been a debate about the true dimensions of NAc in healthy as well as in mentally ill individuals. Knowing its true dimensions in different neuropsychiatric disorders may improve even more precise targeting of NAc for therapeutic DBS. Volumes of NAc of heroin addicts (n = 14) and healthy controls (n = 12) were calculated by using morphometry of serial whole-brain sections. Total brain volume was larger in the heroin group (mean 1478.85 ± 62.34 vs. mean 1352.38 ± 103.24 cm(3)), as the heroin group was more than 10 years younger (p = 0.001). However, the mean volume of the NAc in heroin addicts was smaller than in controls (0.528 ± 0.166 vs. 0.623 ± 0.196 cm(3); p = 0.019). This group effect did not significantly differ between the hemispheres. When assessed separately, left-hemispheric NAc volume was 15 % lower (p = 0.020), while right-hemispheric NAc volume was 16 % lower (p = 0.047) in the heroin-addicted group compared to controls. Based on these diagnosis-related differences, we believe it is important to further analyze NAc volumes in different psychiatric disorders to further improve precise targeting and electrode placement.

Automatic target validation based on neuroscientific literature mining for tractography

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Multiscale coupling of transcranial direct current stimulation to neuron electrodynamics: modeling the influence of the transcranial electric field on neuronal depolarization

Transcranial direct current stimulation (tDCS) continues to demonstrate success as a medical intervention for neurodegenerative diseases, psychological conditions, and traumatic brain injury recovery. One aspect of tDCS still not fully comprehended is the influence of the tDCS electric field on neural functionality. To address this issue, we present a mathematical, multiscale model that couples tDCS administration to neuron electrodynamics. We demonstrate the model's validity and medical applicability with computational simulations using an idealized two-dimensional domain and then an MRI-derived, three-dimensional human head geometry possessing inhomogeneous and anisotropic tissue conductivities. We exemplify the capabilities of these simulations with real-world tDCS electrode configurations and treatment parameters and compare the model's predictions to those attained from medical research studies. The model is implemented using efficient numerical strategies and solution techniques to allow the use of fine computational grids needed by the medical community.