Automated 3-dimensional brain atlas fitting to microelectrode recordings from deep brain stimulation surgeries

Intra-operative micro-electrode recording in functional neurosurgery: Past, present, future

The field of functional neurosurgery has experienced a rise, fall and lastly a renaissance over the past 75years. Micro-electrode recording (MER) played a key role during this eventful journey. However, as the intra-operative MRI continues to evolve, a pertinent question about the utility of MER has been raised in recent years. In this article, we critically review these current controversies. The English literature is reviewed and the complex technique of MER is discussed in a simplified manner. The improvement of neuroimaging and its application in functional neurosurgery, especially in deep brain stimulation, is discussed. Finally, the current controversies and technical advances which can direct the future are reviewed. The results of existing meta-analyses addressing the controversies are summarized. Wide variations of pre-operative and intra-operative targeting methods have been described in the literature. Though functional neurosurgery is generally safe, complications do occur and multiple passes during MER can certainly add to the risk of inadvertent hemorrhage and infection. Additionally, the recent introduction of newer MRI modalities has ensured better delineation of the target. However, MER is still useful to address brain shift, for mapping of newer targets, for ablative surgeries and in centers without an intra-operative imaging facility. In the current scenario, it is nearly impossible to conduct a prospective study to decide the utility of MER. The importance of MER may further diminish in the future as a routine procedure, but its role as a gold standard procedure may still persist.

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

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

Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease

BACKGROUND

Finding the optimal location for the implantation of the electrode in deep brain stimulation (DBS) surgery is crucial for maximizing the therapeutic benefit to the patient. Such targeting is challenging for several reasons, including anatomic variability between patients as well as the lack of consensus about the location of the optimal target.

OBJECTIVE

To compare the performance of popular manual targeting methods against a fully automatic nonrigid image registration-based approach.

METHODS

In 71 Parkinson disease subthalamic nucleus (STN)-DBS implantations, an experienced functional neurosurgeon selected the target manually using 3 different approaches: indirect targeting using standard stereotactic coordinates, direct targeting based on the patient magnetic resonance imaging, and indirect targeting relative to the red nucleus. Targets were also automatically predicted by using a leave-one-out approach to populate the CranialVault atlas with the use of nonrigid image registration. The different targeting methods were compared against the location of the final active contact, determined through iterative clinical programming in each individual patient.

RESULTS

Targeting by using standard stereotactic coordinates corresponding to the center of the motor territory of the STN had the largest targeting error (3.69 mm), followed by direct targeting (3.44 mm), average stereotactic coordinates of active contacts from this study (3.02 mm), red nucleus-based targeting (2.75 mm), and nonrigid image registration-based automatic predictions using the CranialVault atlas (2.70 mm). The CranialVault atlas method had statistically smaller variance than all manual approaches.

CONCLUSION

Fully automatic targeting based on nonrigid image registration with the use of the CranialVault atlas is as accurate and more precise than popular manual methods for STN-DBS.

Closed-loop stimulation of a delayed neural fields model of parkinsonian STN-GPe network: a theoretical and computational study

Several disorders are related to pathological brain oscillations. In the case of Parkinson's disease, sustained low-frequency oscillations (especially in the β-band, 13-30 Hz) correlate with motor symptoms. It is still under debate whether these oscillations are the cause of parkinsonian motor symptoms. The development of techniques enabling selective disruption of these β-oscillations could contribute to the understanding of the underlying mechanisms, and could be exploited for treatments. A particularly appealing technique is Deep Brain Stimulation (DBS). With clinical electrical DBS, electrical currents are delivered at high frequency to a region made of potentially heterogeneous neurons (the subthalamic nucleus (STN) in the case of Parkinson's disease). Even more appealing is DBS with optogenetics, which is until now a preclinical method using both gene transfer and deep brain light delivery and enabling neuromodulation at the scale of one given neural network. In this work, we rely on delayed neural fields models of STN and the external Globus Pallidus (GPe) to develop, theoretically validate and test in silico a closed-loop stimulation strategy to disrupt these sustained oscillations with optogenetics. First, we rely on tools from control theory to provide theoretical conditions under which sustained oscillations can be attenuated by a closed-loop stimulation proportional to the measured activity of STN. Second, based on this theoretical framework, we show numerically that the proposed closed-loop stimulation efficiently attenuates sustained oscillations, even in the case when the photosensitization effectively affects only 50% of STN neurons. We also show through simulations that oscillations disruption can be achieved when the same light source is used for the whole STN population. We finally test the robustness of the proposed strategy to possible acquisition and processing delays, as well as parameters uncertainty.

Anatomic correlates of deep brain stimulation electrode impedance

BACKGROUND

The location of the optimal target for deep brain stimulation (DBS) of the subthalamic nucleus (STN) remains controversial. Electrode impedance affects tissue activation by DBS and has been found to vary by contact number, but no studies have examined association between impedance and anatomic location.

OBJECTIVES

To evaluate the relationship between electrode impedance and anatomic contact location, and to assess the clinical significance of impedance.

METHODS

We gathered retrospective impedance data from 101 electrodes in 73 patients with Parkinson's disease. We determined contact location using microelectrode recording (MER) and high-field 7T MRI, and assessed the relationship between impedance and contact location.

RESULTS

For contact location as assessed via MER, impedance was significantly higher for contacts in STN, at baseline (111 Ω vs STN border, p=0.03; 169 Ω vs white matter, p<0.001) and over time (90 Ω vs STN border, p<0.001; 54 Ω vs white matter, p<0.001). Over time, impedance was lowest in contacts situated at STN border (p=0.03). Impedance did not vary by contact location as assessed via imaging. Location determination was 75% consistent between MER and imaging. Impedance was inversely related to absolute symptom reduction during stimulation (-2.5 motor portion of the Unified Parkinson's Disease Rating Scale (mUPDRS) points per 1000 Ω, p=0.01).

CONCLUSIONS

In the vicinity of DBS electrodes chronically implanted in STN, impedance is lower at the rostral STN border and in white matter, than in STN. This finding suggests that current reaches white matter fibres more readily than neuronal cell bodies in STN, which may help explain anatomic variation in stimulation efficacy.

Use of efficacy probability maps for the post-operative programming of deep brain stimulation in essential tremor

INTRODUCTION

Post-operative programming of deep brain stimulation for movement disorders can be both time consuming and difficult, which can delay the optimal symptom control for the patient. Probabilistic maps of stimulation response could improve programming efficiency and optimization.

METHODS

The clinically selected contacts of patients who had undergone ventral intermediate nucleus deep brain stimulation for the treatment of essential tremor at our institution were compared against contacts selected based on a probability map of symptom reduction built by populating data from a number of patients using non-rigid image registration. A subgroup of patients whose clinical contacts did not match the map-based selections prospectively underwent a tremor rating scale evaluation to compare the symptom relief achieved by the two options. Both the patient and video reviewer were blinded to the selection.

RESULTS

54% of the map-based and clinical contacts were an exact match retrospectively and were within one contact 83% of the time. In 5 of the 8 mismatched leads that were evaluated prospectively in a double blind fashion, the map-based contact showed equivalent or better tremor improvement than the clinically active contact.

CONCLUSIONS

This study suggests that probability maps of stimulation responses can assist in selecting the clinically optimal contact and increase the efficiency of programming.

Engineering the next generation of clinical deep brain stimulation technology

Deep brain stimulation (DBS) has evolved into a powerful clinical therapy for a range of neurological disorders, but even with impressive clinical growth, DBS technology has been relatively stagnant over its history. However, enhanced collaborations between neural engineers, neuroscientists, physicists, neurologists, and neurosurgeons are beginning to address some of the limitations of current DBS technology. These interactions have helped to develop novel ideas for the next generation of clinical DBS systems. This review attempts collate some of that progress with two goals in mind. First, provide a general description of current clinical DBS practices, geared toward educating biomedical engineers and computer scientists on a field that needs their expertise and attention. Second, describe some of the technological developments that are currently underway in surgical targeting, stimulation parameter selection, stimulation protocols, and stimulation hardware that are being directly evaluated for near term clinical application.

Determination of electrode to nerve fiber distance and nerve conduction velocity through spectral analysis of the extracellular action potentials recorded from earthworm giant fibers

Microneurography and the use of selective microelectrodes that can resolve single-unit nerve activity have become a tool to understand the coding within the nervous system and a clinical diagnostic tool to assess peripheral neural pathologies. Central to these techniques is the use of the differences in the shape of the extracellular action potential (AP) waveform to identify and discriminate units from one another. Theoretical modeling of the origins of these shape differences has shown that the position of the nerve fiber relative to the electrode and the conduction velocity of the unit contribute to these differences giving rise to the hypothesis that more information about the fiber and its relationship to the electrode could be extracted given further analysis of the AP waveform. This paper addresses this question by exploring the electrical coupling between the electrode and nerve fiber. Idealized models and the literature indicate that two parameters, the electrode-fiber distance and the unit conduction velocity, contribute to the amplitude of the extracellular AP detected by the electrode, which confounds the quantification of coupling using the spike amplitude alone. To resolve this, we develop a method that enables differential quantification of these two parameters using spectral analysis of the single-unit AP waveform and demonstrate that the two parameters could be effectively decoupled in an in vitro earthworm model. The method could open the way forward toward micro-scale in situ monitoring of the interaction of nerve fiber and neural interface.

Central thalamic deep brain stimulation to promote recovery from chronic posttraumatic minimally conscious state: challenges and opportunities

BACKGROUND

Central thalamic deep brain stimulation (CT-DBS) may have therapeutic potential to improve behavioral functioning in patients with severe traumatic brain injury (TBI), but its use remains experimental. Current research suggests that the central thalamus plays a critical role in modulating arousal during tasks requiring sustained attention, working memory, and motor function. The aim of the current article is to review the methodology used in the CT-DBS protocol developed by our group, outline the challenges we encountered and offer suggestions for future DBS trials in this population. RATIONAL FOR CT-DBS IN TBI:  CT-DBS may therefore be able to stimulate these functions by eliciting action potentials that excite thalamocortical and thalamostriatal pathways. Because patients in chronic minimally conscious state (MCS) have a very low probability of regaining functional independence, yet often have significant sparing of cortical connectivity, they may represent a particularly appropriate target group for CT-DBS. PIlOT STUDY RESULTS:  We have conducted a series of single-subject studies of CT-DBS in patients with chronic posttraumatic MCS, with 24-month follow-up. Outcomes were measured using the Coma Recovery Scale-Revised as well as a battery of secondary outcome measures to capture more granular changes. Findings from our index case suggest that CT-DBS can significantly increase functional communication, motor performance, feeding, and object naming in the DBS on state, with performance in some domains remaining above baseline even after DBS was turned off.

CONCLUSIONS

The use of CT-DBS in patients in MCS, however, presents challenges at almost every step, including during surgical planning, outcome measurement, and postoperative care. Additionally, given the difficulties of obtaining informed consent from patients in MCS and the experimental nature of the treatment, a robust, scientifically rooted ethical framework is resented for pursuing this line of work.

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.

Characterization of subcortical structures during deep brain stimulation utilizing support vector machines

In this paper we discuss an efficient methodology for the characterization of Microelectrode Recordings (MER) obtained during deep brain stimulation surgery for Parkinson's disease using Support Vector Machines and present the results of a preliminary study. The methodology is based in two algorithms: (1) an algorithm extracts multiple computational features from the microelectrode neurophysiology, and (2) integrates them in the support vector machines algorithm for classification. It has been applied to the problem of the recognition of subcortical structures: thalamus nucleus, zona incerta, subthalamic nucleus and substantia nigra. The SVM (support vector machines) algorithm performed quite well achieving 99.4% correct classification. In conclusion, the use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity in the localization of the subcortical structures and mainly the subthalamic nucleus (STN) for neurostimulation.

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.

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.

Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modelling approach to deep brain stimulation programming

Deep brain stimulation in the subthalamic nucleus is an effective and safe surgical procedure that has been shown to reduce the motor dysfunction of patients with advanced Parkinson's disease. Bilateral subthalamic nucleus deep brain stimulation, however, has been associated with declines in cognitive and cognitive-motor functioning. It has been hypothesized that spread of current to nonmotor areas of the subthalamic nucleus may be responsible for declines in cognitive and cognitive-motor functioning. The aim of this study was to assess the cognitive-motor performance in advanced Parkinson's disease patients with subthalamic nucleus deep brain stimulation parameters determined clinically (Clinical) to settings derived from a patient-specific computational model (Model). Data were collected from 10 patients with advanced Parkinson's disease bilaterally implanted with subthalamic nucleus deep brain stimulation systems. These patients were assessed off medication and under three deep brain stimulation conditions: Off, Clinical or Model based stimulation. Clinical stimulation parameters had been determined based on clinical evaluations and were stable for at least 6 months prior to study participation. Model-based parameters were selected to minimize the spread of current to nonmotor portions of the subthalamic nucleus using Cicerone Deep Brain Stimulation software. For each stimulation condition, participants performed a working memory (n-back task) and motor task (force tracking) under single- and dual-task settings. During the dual-task, participants performed the n-back and force-tracking tasks simultaneously. Clinical and Model parameters were equally effective in improving the Unified Parkinson's disease Rating Scale III scores relative to Off deep brain stimulation scores. Single-task working memory declines, in the 2-back condition, were significantly less under Model compared with Clinical deep brain stimulation settings. Under dual-task conditions, force tracking was significantly better with Model compared with Clinical deep brain stimulation. In addition to better overall cognitive-motor performance associated with Model parameters, the amount of power consumed was on average less than half that used with the Clinical settings. These results indicate that the cognitive and cognitive-motor declines associated with bilateral subthalamic nucleus deep brain stimulation may be reversed, without compromising motor benefits, by using model-based stimulation parameters that minimize current spread into nonmotor regions of the subthalamic nucleus.