Electrical Stimulation of Regenerative Peripheral Nerve Interfaces (RPNIs) Induces Referred Sensations in People With Upper Limb Loss.

Individuals with upper limb loss lack sensation of the missing hand, which can negatively impact their daily function. Several groups have attempted to restore this sensation through electrical stimulation of residual nerves. The purpose of this study was to explore the utility of regenerative peripheral nerve interfaces (RPNIs) in eliciting referred sensation. In four participants with upper limb loss, we characterized the quality and location of sensation elicited through electrical stimulation of RPNIs over time. We also measured functional stimulation ranges (sensory perception and discomfort thresholds), sensitivity to changes in stimulation amplitude, and ability to differentiate objects of different stiffness and sizes. Over a period of up to 54 months, stimulation of RPNIs elicited sensations that were consistent in quality (e.g. tingling, kinesthesia) and were perceived in the missing hand and forearm. The location of elicited sensation was partially-stable to stable in 13 of 14 RPNIs. For 5 of 7 RPNIs tested, participants demonstrated a sensitivity to changes in stimulation amplitude, with an average just noticeable difference of 45 nC. In a case study, one participant was provided RPNI stimulation proportional to prosthetic grip force. She identified four objects of different sizes and stiffness with 56% accuracy with stimulation alone and 100% accuracy when stimulation was combined with visual feedback of hand position. Collectively, these experiments suggest that RPNIs have the potential to be used in future bi-directional prosthetic systems.

Dorsal subthalamic deep brain stimulation improves pain in Parkinson's disease.

Introduction: Inconsistent effects of subthalamic deep brain stimulation (STN DBS) on pain, a common non-motor symptom of Parkinson's disease (PD), may be due to variations in active contact location relative to some pain-reducing locus of stimulation. This study models and compares the loci of maximal effect for pain reduction and motor improvement in STN DBS. Methods: We measured Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) Part I pain score (item-9), and MDS-UPDRS Part III motor score, preoperatively and 6-12 months after STN DBS. An ordinary least-squares regression model was used to examine active contact location as a predictor of follow-up pain score while controlling for baseline pain, age, dopaminergic medication, and motor improvement. An atlas-independent isotropic electric field model was applied to distinguish sites of maximally effective stimulation for pain and motor improvement. Results: In 74 PD patients, mean pain score significantly improved after STN DBS (p = 0.01). In a regression model, more dorsal active contact location was the only significant predictor of pain improvement (R2 = 0.17, p = 0.03). The stimulation locus for maximal pain improvement was lateral, anterior, and dorsal to that for maximal motor improvement. Conclusion: STN stimulation, dorsal to the site of optimal motor improvement, improves pain. This region contains the zona incerta, which is known to modulate pain in humans, and may explain this observation.

Restoration of Proprioceptive and Cutaneous Sensation Using Regenerative Peripheral Nerve Interfaces in Humans with Upper Limb Amputations.

SUMMARY: Without meaningful and intuitive sensory feedback, even the most advanced prosthetic limbs remain insensate and impose an enormous cognitive burden during use. The regenerative peripheral nerve interface can serve as a novel bidirectional motor and sensory neuroprosthetic interface. In previous human studies, regenerative peripheral nerve interfaces demonstrated stable high-amplitude motor electromyography signals with excellent signal-to-noise ratio for prosthetic control. In addition, they can treat and prevent postamputation pain by mitigating neuroma formation. In this study, the authors investigated whether electrical stimulation applied to regenerative peripheral nerve interfaces could produce appreciable proprioceptive and/or tactile sensations in two participants with upper limb amputations. Stimulation of the interfaces resulted in both participants reporting proprioceptive sensations in the phantom hand. Specifically, stimulation of participant 1's median nerve regenerative peripheral nerve interface activated a flexion sensation in the thumb or index finger, whereas stimulation of the ulnar nerve interface evoked a flexion sensation of the ring or small finger. Likewise, stimulation of one of participant 2's ulnar nerve interfaces produced a sensation of flexion at the ring finger distal interphalangeal joint. In addition, stimulation of participant 2's other ulnar nerve interface and the median nerve interface resulted in perceived cutaneous sensations that corresponded to each nerve's respective dermatome. These results suggest that regenerative peripheral nerve interfaces have the potential to restore proprioceptive and cutaneous sensory feedback that could significantly improve prosthesis use and embodiment.

Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson's disease.

Objective. Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming.Approach. Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings (MERs) sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTAs). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA.Main results. Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (Part III of the Movement Disorders Society revision of the Unified Parkinson Disease Rating Scale,R= 0.61,p= 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R= -0.31,p= 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p= 0.047) in this dataset.Significance. A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.

Stimulation of zona incerta selectively modulates pain in humans.

Stimulation of zona incerta in rodent models has been shown to modulate behavioral reactions to noxious stimuli. Sensory changes observed in Parkinsonian patients with subthalamic deep brain stimulation suggest that this effect is translatable to humans. Here, we utilized the serendipitous placement of subthalamic deep brain stimulation leads in 6 + 5 Parkinsonian patients to directly investigate the effects of zona incerta stimulation on human pain perception. We found that stimulation at 20 Hz, the physiological firing frequency of zona incerta, reduces experimental heat pain by a modest but significant amount, achieving a 30% reduction in one fifth of implants. Stimulation at higher frequencies did not modulate heat pain. Modulation was selective for heat pain and was not observed for warmth perception or pressure pain. These findings provide a mechanistic explanation of sensory changes seen in subthalamic deep brain stimulation patients and identify zona incerta as a potential target for neuromodulation of pain.

Comparison of signal decomposition techniques for analysis of human cortical signals.

OBJECTIVE: Conventional neural signal analysis methods assume that features of interest are linear, time-invariant signals confined to well-delineated spectral bands. However, new evidence suggests that neural signals exhibit important non-stationary characteristics with ill-defined spectral distributions. These features pose a need for signal processing algorithms that can characterize temporal and spectral features of non-linear time series. This study compares the effectiveness of four algorithms in extracting neural information for use in decoding cortical signals: Fast Fourier Transform bandpass filtering (FFT), principal spectral component analysis (PSCA), wavelet analysis (WA), and empirical mode decomposition (EMD). APPROACH: Electrocorticographic signals were recorded from the motor and sensory cortex of two epileptic patients performing finger movements. Each signal processing algorithm was used to extract beta (10-30 Hz) and gamma (66-114 Hz) band power to detect thumb movement and decode finger flexions, respectively. Naïve-Bayes (NB), support vector machine (SVM), and linear discriminant analysis (LDA) classifiers using each signal were validated using leave-one-out cross-validation. MAIN RESULTS: Decoders using all four signal decompositions achieved above 90% average accuracy in finger movement detection using beta power. When decoding individual finger flexion using gamma, the PSCA NB classifiers achieved 78 ± 4% accuracy while FFT, WA, and EMD analysis achieved accuracies of 73 ± 8%, 68 ± 7%, and 62 ± 3% respectively, with similar results using SVM and LDA. SIGNIFICANCE: These results illustrate the relative levels of useful information contributed by each decomposition method in the case of finger movement decoding, which can inform the development of effective neural decoding pipelines. Further analyses could compare performance using more specific non-sinusoidal features, such as transients and phase-amplitude coupling.

Correspondence of optimal stimulation and beta power varies regionally in STN DBS for Parkinson disease.

INTRODUCTION: Subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson disease (PD) normalizes neuronal hypersynchrony in the beta frequency range (13-30 Hz). The spatial correspondence of maximal beta power to the site of optimal stimulation along the DBS lead trajectory has been debated. METHODS: We determined the trajectory locations of the active contact, maximal beta power, and the dorsal border of the STN (DB-STN) in DBS patients. Beta power profiles were measured during intraoperative microelectrode recording (MER). Active contact locations were assigned during blinded, postoperative DBS programming. The DB-STN was identified both electrophysiologically during MER and anatomically on MRI. After grouping DBS trajectories into quadrants relative to the anatomic STN midpoint, we examined regional variations in the relative trajectory locations of the three entities. RESULTS: STN DBS significantly improved motor performance for all 13 DBS patients, with active contacts at the DB-STN. Along trajectories passing posterior-medial to the STN midpoint, maximal beta power co-localized with active contacts at the DB-STN (difference Δ = 0.4 ± 1.6 mm, p = 0.57). By contrast, in posterior-lateral trajectories, maximal beta arose within the STN, ventral to active contacts (Δ = 1.9 ± 1.3 mm, p = 0.002). For trajectories anterior to the STN midpoint, maximal beta power co-localized with the DB-STN, while active contacts were ventral to peak beta power (p = 0.05). CONCLUSION: Our findings indicate that co-localization of optimal stimulation and beta power varies by anatomical region in STN DBS for Parkinson disease.

Distinct perceptive pathways selected with tonic and bursting patterns of thalamic stimulation.

BACKGROUND: Novel patterns of electrical stimulation of the brain and spinal cord hold tremendous promise to improve neuromodulation therapies for diverse disorders, including tremor and pain. To date, there are limited numbers of experimental studies in human subjects to help explain how stimulation patterns impact the clinical response, especially with deep brain stimulation. We propose using novel stimulation patterns during electrical stimulation of somatosensory thalamus in awake deep brain stimulation surgeries and hypothesize that stimulation patterns will influence the sensory percept without moving the electrode. METHODS: In this study of 15 fully awake patients, the threshold of perception as well as perceptual characteristics were compared for tonic (trains of regularly-repeated pulses) and bursting stimulation patterns. RESULTS: In a majority of subjects, tonic and burst percepts were located in separate, non-overlapping body regions (i.e., face vs. hand) without moving the stimulating electrode (p < 0.001; binomial test). The qualitative features of burst percepts also differed from those of tonic-evoked percepts as burst patterns were less likely to evoke percepts described as tingling (p = 0.013; Fisher's exact test). CONCLUSIONS: Because somatosensory thalamus is somatotopically organized, percept location can be related to anatomic thalamocortical pathways. Thus, stimulation pattern may provide a mechanism to select for different thalamocortical pathways. This added control could lead to improvements in neuromodulation - such as improved efficacy and side effect attenuation - and may also improve localization for sensory prostheses.

High density microelectrode recording predicts span of therapeutic tissue activation volumes in subthalamic deep brain stimulation for Parkinson disease.

BACKGROUND: Subthalamic deep brain stimulation alleviates motor symptoms of Parkinson disease by activating precise volumes of neural tissue. While electrophysiological and anatomical correlates of clinically effective electrode sites have been described, therapeutic stimulation likely acts through multiple distinct neural populations, necessitating characterization of the full span of tissue activation. Microelectrode recordings have yet to be mapped to therapeutic tissue activation volumes and surveyed for predictive markers. OBJECTIVE: Combine high-density, broadband microelectrode recordings with detailed computational models of tissue activation to describe and to predict regions of therapeutic tissue activation. METHODS: Electrophysiological features were extracted from microelectrode recordings along 23 subthalamic deep brain stimulation implants in 16 Parkinson disease patients. These features were mapped in space against tissue activation volumes of therapeutic stimulation, modeled using clinically-determined stimulation programming parameters and fully individualized, atlas-independent anisotropic tissue properties derived from 3T diffusion tensor magnetic resonance images. Logistic LASSO was applied to a training set of 17 implants out of the 23 implants to identify predictors of therapeutic stimulation sites in the microelectrode recording. A support vector machine using these predictors was used to predict therapeutic activation. Performance was validated with a test set of six implants. RESULTS: Analysis revealed wide variations in the distribution of therapeutic tissue activation across the microelectrode recording-defined subthalamic nucleus. Logistic LASSO applied to the training set identified six oscillatory predictors of therapeutic tissue activation: theta, alpha, beta, high gamma, high frequency oscillations (HFO, 200-400 Hz), and high frequency band (HFB, 500-2000 Hz), in addition to interaction terms: theta x HFB, alpha x beta, beta x HFB, and high gamma x HFO. A support vector classifier using these features predicted therapeutic sites of activation with 64% sensitivity and 82% specificity in the test set, outperforming a beta-only classifier. A probabilistic predictor achieved 0.87 area under the receiver-operator curve with test data. CONCLUSIONS: Together, these results demonstrate the importance of personalized targeting and validate a set of microelectrode recording signatures to predict therapeutic activation volumes. These features may be used to improve the efficiency of deep brain stimulation programming and highlight specific neural oscillations of physiological importance.

A Computerized Microelectrode Recording to Magnetic Resonance Imaging Mapping System for Subthalamic Nucleus Deep Brain Stimulation Surgery.

BACKGROUND: Accurate electrode placement is critical to the success of deep brain stimulation (DBS) surgery. Suboptimal targeting may arise from poor initial target localization, frame-based targeting error, or intraoperative brain shift. These uncertainties can make DBS surgery challenging. OBJECTIVE: To develop a computerized system to guide subthalamic nucleus (STN) DBS electrode localization and to estimate the trajectory of intraoperative microelectrode recording (MER) on magnetic resonance (MR) images algorithmically during DBS surgery. METHODS: Our method is based upon the relationship between the high-frequency band (HFB; 500-2000 Hz) signal from MER and voxel intensity on MR images. The HFB profile along an MER trajectory recorded during surgery is compared to voxel intensity profiles along many potential trajectories in the region of the surgically planned trajectory. From these comparisons of HFB recordings and potential trajectories, an estimate of the MER trajectory is calculated. This calculated trajectory is then compared to actual trajectory, as estimated by postoperative high-resolution computed tomography. RESULTS: We compared 20 planned, calculated, and actual trajectories in 13 patients who underwent STN DBS surgery. Targeting errors for our calculated trajectories (2.33 mm ± 0.2 mm) were significantly less than errors for surgically planned trajectories (2.83 mm ± 0.2 mm; P = .01), improving targeting prediction in 70% of individual cases (14/20). Moreover, in 4 of 4 initial MER trajectories that missed the STN, our method correctly indicated the required direction of targeting adjustment for the DBS lead to intersect the STN. CONCLUSION: A computer-based algorithm simultaneously utilizing MER and MR information potentially eases electrode localization during STN DBS surgery.

Current challenges to the clinical translation of brain machine interface technology.

Development of neural prostheses over the past few decades has produced a number of clinically relevant brain-machine interfaces (BMIs), such as the cochlear prostheses and deep brain stimulators. Current research pursues the restoration of communication or motor function to individuals with neurological disorders. Efforts in the field, such as the BrainGate trials, have already demonstrated that such interfaces can enable humans to effectively control external devices with neural signals. However, a number of significant issues regarding BMI performance, device capabilities, and surgery must be resolved before clinical use of BMI technology can become widespread. This chapter reviews challenges to clinical translation and discusses potential solutions that have been reported in recent literature, with focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation.