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.

Long-Term Reductions in Opioid Medication Use After Spinal Stimulation: A Claims Analysis Among Commercially-Insured Population

Purpose

Chronic, non-cancer pain significantly and negatively impacts patient quality of life. Neuromodulation is a major component of multi-modal interdisciplinary approaches to chronic pain management, which includes opioid and nonopioid medications. In randomized controlled trials, spinal cord stimulation (SCS) has been shown to reduce pain and decrease short-term opioid use for patients. This study sought to evaluate the effect of SCS on longer term opioid and non-opioid pain medication usage among patients over ≥3 years of follow-up.

Patients and Methods

Claims analysis was conducted using the Merative™ MarketScan® Commercial Database. Patients aged ≥18 who initiated SCS between 1/1/2010 and 3/31/2021 with ≥1 year of baseline data and ≥3 years of follow-up data were included. Opioid discontinuation, daily dose (DD) reduction, proportion of days covered (PDC), concomitant co-medication with benzodiazepines and/or gabapentinoids, and polypharmacy were evaluated during the baseline and follow-up periods. Adjusted logistic regression was used to evaluate the impact of baseline dosages on discontinuation and dose reduction.

Results

During follow-up, 60% of 2,669 SCS patients either discontinued opioid use or reduced opioid DD by at least 20% from baseline; another 15% reduced DD by 1-19%. Logistic regression showed patients with higher baseline dosages were less likely to discontinue opioids completely (odds ratio[OR] 95% confidence intervals[CI]: 0.31[0.18,0.54]) but more likely to reduce their daily dose (OR[CI]: 7.14[4.00,12.73], p<0.001). Mean PDC with opioids decreased from 0.58 (210 of 365 days) at baseline to 0.51 at year 3 (p<0.001). With SCS, co-medication with benzodiazepines decreased from 47.3% at baseline to 30.3% at year 3, co-medication with gabapentinoids reduced from 58.6% to 42.2%, and polypharmacy dropped from 15.6% to 9.6% (all p<0.001).

Conclusion

Approximately three-quarters of patients who received SCS therapy either discontinued or reduced systemic opioid use over the study period. SCS could assist in reducing long-term reliance on opioids and other pain medications to treat chronic non-cancer pain.

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.

Teaser

A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.

Impact of subthalamic nucleus stimulation on urinary dysfunction and constipation in Parkinson's disease

OBJECTIVE

The effect of subthalamic nucleus (STN) deep brain stimulation (DBS) on urinary dysfunction and constipation in Parkinson's disease (PD) is variable. This study aimed to identify potential surgical and nonsurgical variables predictive of these outcomes.

METHODS

The authors used the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I to assess urinary dysfunction (item 10) and constipation (item 11) preoperatively and at 6-12 months postoperatively. A multiple linear regression model was used to investigate the impact of global cerebral atrophy (GCA) and active electrode contact location on the urinary dysfunction and constipation follow-up scores, controlling for age, disease duration, baseline score, motor improvement, and levodopa-equivalent dose changes. An electric field model was applied to localize the maximal-effect sites for constipation and urinary dysfunction compared with those for motor improvement.

RESULTS

Among 74 patients, 23 improved, 28 deteriorated, and 23 remained unchanged for urinary dysfunction; 25 improved, 15 deteriorated, and 34 remained unchanged for constipation. GCA score and age significantly predicted urinary dysfunction follow-up score (R2 = 0.36, p < 0.001). Increased GCA and age were independently associated with worsening urinary symptoms. Disease duration, baseline constipation score, and anterior active electrode contacts in both hemispheres were significant predictors of constipation follow-up score (R2 = 0.31, p < 0.001). Higher baseline constipation score and disease duration were associated with worsening constipation; anterior active contact location was associated with improvement in constipation.

CONCLUSIONS

Anterior active contact location was associated with improvement in constipation in PD patients after STN DBS. PD patients with greater GCA scores before surgery were more likely to experience urinary deterioration after DBS.

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.

Thalamic Segmentation and Neural Activation Modeling Based on Individual Tissue Microstructure in Deep Brain Stimulation for Essential Tremor

OBJECTIVE

Thalamic deep brain stimulation (DBS) is the primary surgical therapy for essential tremor (ET). Thalamic DBS traditionally uses an atlas-based targeting approach, which, although nominally accurate, may obscure individual anatomic differences from population norms. The objective of this study was to compare this traditional atlas-based approach with a novel quantitative modeling methodology grounded in individual tissue microstructure (N-of-1 approach).

MATERIALS AND METHODS

The N-of-1 approach uses individual patient diffusion tensor imaging (DTI) data to perform thalamic segmentation and volume of tissue activation (VTA) modeling. For each patient, the thalamus was individually segmented into 13 nuclei using DTI-based k-means clustering. DBS-induced VTAs associated with tremor suppression and side effects were then computed for each patient with finite-element electric-field models incorporating DTI microstructural data. Results from N-of-1 and traditional atlas-based modeling were compared for a large cohort of patients with ET treated with thalamic DBS.

RESULTS

The size and shape of individual N-of-1 thalamic nuclei and VTAs varied considerably across patients (N = 22). For both methods, tremor-improving therapeutic VTAs showed similar overlap with motor thalamic nuclei and greater motor than sensory nucleus overlap. For VTAs producing undesirable sustained paresthesia, 94% of VTAs overlapped with N-of-1 sensory thalamus estimates, whereas 74% of atlas-based segmentations overlapped. For VTAs producing dysarthria/motor contraction, the N-of-1 approach predicted greater spread beyond the thalamus into the internal capsule and adjacent structures than the atlas-based method.

CONCLUSIONS

Thalamic segmentation and VTA modeling based on individual tissue microstructure explain therapeutic stimulation equally well and side effects better than a traditional atlas-based method in DBS for ET. The N-of-1 approach may be useful in DBS targeting and programming, particularly when patient neuroanatomy deviates from population norms.

Error detection and correction in intracortical brain-machine interfaces controlling two finger groups

Objective.While brain-machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e. consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.Mainresults.First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below5%, it was possible to achieve mean true positive rate of28.1%online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.Significance.Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements may be obtained by enhancing classification and correction strategies.

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.

Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns.

Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain-machine interfaces

OBJECTIVE

Brain-machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user's own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand.

APPROACH

We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI's predictions to control FES of the monkey's finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI's predictions to control movements of virtual fingers, with no FES.

MAIN RESULTS

In the BCFES task, the monkey improved his success rate to 83% (1.5s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time.

SIGNIFICANCE

These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis.

Localization of deep brain stimulation trajectories via automatic mapping of microelectrode recordings to MRI

Objective. Suboptimal electrode placement during subthalamic nucleus deep brain stimulation (STN DBS) surgery may arise from several sources, including frame-based targeting errors and intraoperative brain shift. We present a computer algorithm that can accurately localize intraoperative microelectrode recording (MER) tracks on preoperative magnetic resonance imaging (MRI) in real-time, thereby predicting deviation between the surgical plan and the MER trajectories.Approach. Random forest (RF) modeling was used to derive a statistical relationship between electrophysiological features on intraoperative MER and voxel intensity on preoperative T2-weighted MR imaging. This model was integrated into a larger algorithm that can automatically localize intraoperative MER recording tracks on preoperative MRI in real-time. To verify accuracy, targeting error of both the planned intraoperative trajectory ('planned') and the algorithm-derived trajectory ('calculated') was estimated by measuring deviation from the final DBS lead location on postoperative high-resolution computed tomography ('actual').Main results. MR imaging and MERs were obtained from 24 STN DBS implant trajectories. The cross-validated RF model could accurately distinguish between gray and white matter regions along MER trajectories (AUC 0.84). When applying this model within the localization algorithm, thecalculatedMER trajectory estimate was found to be significantly closer to theactualDBS lead when compared to theplannedtrajectory recorded during surgery (1.04 mm vs 1.52 mm deviation,p< 0.002), with improvement shown in 19/24 cases (79%). When applying the algorithm to simulated DBS trajectory plans with randomized targeting error, up to 4 mm of error could be resolved to <2 mm on average (p< 0.0001).Significance. This work presents an automated system for intraoperative localization of electrodes during STN DBS surgery. This neuroengineering solution may enhance the accuracy of electrode position estimation, particularly in cases where high-resolution intraoperative imaging is not available.

Utah array characterization and histological analysis of a multi-year implant in non-human primate motor and sensory cortices

Objective.The Utah array is widely used in both clinical studies and neuroscience. It has a strong track record of safety. However, it is also known that implanted electrodes promote the formation of scar tissue in the immediate vicinity of the electrodes, which may negatively impact the ability to record neural waveforms. This scarring response has been primarily studied in rodents, which may have a very different response than primate brain.Approach.Here, we present a rare nonhuman primate histological dataset (n= 1 rhesus macaque) obtained 848 and 590 d after implantation in two brain hemispheres. For 2 of 4 arrays that remained within the cortex, NeuN was used to stain for neuron somata at three different depths along the shanks. Images were filtered and denoised, with neurons then counted in the vicinity of the arrays as well as a nearby section of control tissue. Additionally, 3 of 4 arrays were imaged with a scanning electrode microscope to evaluate any materials damage that might be present.Main results.Overall, we found a 63% percent reduction in the number of neurons surrounding the electrode shanks compared to control areas. In terms of materials, the arrays remained largely intact with metal and Parylene C present, though tip breakage and cracks were observed on many electrodes.Significance.Overall, these results suggest that the tissue response in the nonhuman primate brain shows similar neuron loss to previous studies using rodents. Electrode improvements, for example using smaller or softer probes, may therefore substantially improve the tissue response and potentially improve the neuronal recording yield in primate cortex.

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder

Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.

Decline of verbal fluency with lateral superior frontal gyrus penetration in subthalamic nucleus deep brain stimulation for Parkinson disease

OBJECTIVE

Verbal fluency (VF) decline is a well-recognized adverse cognitive outcome following subthalamic nucleus deep brain stimulation (STN DBS) in patients with Parkinson disease (PD). The mechanisms underlying VF decline, whether from stimulation, lesioning, or both, remain unclear. This study aims to investigate the unique effects of DBS lead trajectory on VF beyond previously reported effects of active contact location.

METHODS

The study population included 56 patients with idiopathic PD who underwent bilateral STN DBS. Phonemic and semantic VF scores were compared pre- and postoperatively. Features of the electrode trajectory were measured on postoperative imaging, including distance from the falx cerebri, distance from the superior frontal sulcus, and caudate nucleus penetration. The authors used t-tests, Pearson's correlation, and multiple linear regression analyses to examine the relationship between VF change and demographic, disease, and electrode trajectory variables.

RESULTS

The laterality of entry within the left superior frontal gyrus (SFG) predicted greater phonemic VF decline (sr2 = 0.28, p < 0.001) after controlling for active contact location. VF change did not differ by the presence of caudate nucleus penetration in either hemisphere (p > 0.05).

CONCLUSIONS

Lateral penetration of the SFG in the left hemisphere is associated with worsening phonemic VF and has greater explanatory power than active contact location. This may be explained by lesioning of the lateral SFG-Broca area pathway, which is implicated in language function.

Motor speech effects in subthalamic deep brain stimulation for Parkinson's disease

OBJECTIVE

A motor speech disorder or dysarthria commonly arises in patients with Parkinson's disease (PD). The impact of subthalamic nucleus (STN) deep brain stimulation (DBS) on motor speech and the potential of intraoperative motor speech testing to predict outcomes are unknown. This study examined 1) the types and prevalence of motor speech changes observed with STN DBS and their relation to the preoperative condition, 2) the ability of intraoperative testing to predict postoperative changes in motor speech, and 3) the spatial relationship between stimulation sites producing maximal motor improvement, as measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), and maximal motor speech deterioration.

METHODS

Comprehensive preoperative, intraoperative, and postoperative motor speech/dysarthria evaluations were performed in consecutive patients with advanced idiopathic PD who underwent STN DBS surgery in the period from 2011 to 2016. Preoperative type of dysarthria and overall dysarthria severity rating along with intraoperative motor speech testing results were evaluated as predictors of postoperative change. Atlas-independent, fully individualized field modeling was used to identify stimulation sites associated with maximal MDS-UPDRS motor improvement and motor speech deterioration.

RESULTS

Forty-three patients with PD treated with STN DBS were prospectively studied. Improved MDS-UPDRS motor scores and worsened dysarthria were demonstrated by a subset of patients (16/43). Preoperative dysarthria characteristics did not predict postoperative deterioration. Intraoperative assessment of motor speech strongly predicted postoperative outcomes (OR 4.4, p = 0.02). Sites of maximal MDS-UPDRS motor improvement and worsened dysarthria were distinct. Worsened dysarthria was associated with capsular stimulation, anterior and ventral to the site of maximal MDS-UPDRS motor improvement.

CONCLUSIONS

The predictive reliability of intraoperative motor speech testing, together with the identification of distinct stimulation sites for motor speech impairment and improved MDS-UPDRS motor function, raise the possibility that DBS lead repositioning or reprogramming could reduce adverse effects on motor speech without impacting MDS-UPDRS motor outcomes in patients undergoing STN DBS.

A-190 Posterior Electrode Angle Predicts Post-DBS Apathy Increases in Parkinson’s Disease Patients

Objective: The literature has consistently shown that Parkinson’s disease (PD) patients tend to experience increases in apathy following deep brain stimulation of the subthalamic nucleus (STN-DBS). While surgical variables, i.e., active contact location and lead angle, have previously been found to predict post-DBS cognitive changes, it has yet to be studied whether these variables also impact post-DBS apathy.

Method: Data was retrospectively analyzed from 42 individuals with idiopathic PD who underwent bilateral STN-DBS. Forty-two individuals completed patient-report versions of the Marin Apathy Scale, and 27 had accompanying caregiver reports. Pearson’s correlations and linear regressions were conducted to examine the impact of active contact location (in X, Y, and Z axes) and lead angle (in anterior/posterior and medial/lateral directions) on post-DBS apathy changes. Covariates included disease duration, age, and change in motor symptoms (as measured by the MDS-UPDRS).

Results: Increases in caregiver-reported apathy post-DBS were explained by more posterior angles of the leads in both hemispheres (right: R2 = .33, F(3,23) = 3.79, p = .02, sr2 = .32; left: R2 = .37, F(3,23) = 4.56, p = .01, sr2 = .36). Although a significant correlation was identified between worse patient-reported apathy and more medial active contact location in the right hemisphere, this effect was no longer significant with inclusion of covariates.

Conclusions: Findings indicate that lead angle, but not active contact location, has a significant impact on post-DBS increase in apathy. This is consistent with prior studies predicting post-DBS verbal fluency changes and may suggest an adverse effect on frontostriatal neurocircuitry.

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.

A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates

Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.

A low-power communication scheme for wireless, 1000 channel brain-machine interfaces

OBJECTIVE

Brain-machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating "motes" which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption.

APPROACH

To test PIM's ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption.

MAIN RESULTS

We found that PIM at 1kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3mW for 1000 channels.

SIGNIFICANCE

These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.

A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry

Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 μW/mm² of light exposure. It achieves best-in-class power consumption of 0.57 μW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.

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 (PD) 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 sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTA). 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 (MDS-UPRDS Part III, 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.

The Concept, Development, and Application of a Home-Based High-Definition tDCS for Bilateral Motor Cortex Modulation in Migraine and Pain

Whereas, many debilitating chronic pain disorders are dominantly bilateral (e.g., fibromyalgia, chronic migraine), non-invasive and invasive cortical neuromodulation therapies predominantly apply unilateral stimulation. The development of excitatory stimulation targeting bilateral primary motor (M1) cortices could potentially expand its therapeutic effect to more global pain relief. However, this is hampered by increased procedural and technical complexity. For example, repetitive transcranial magnetic stimulation (rTMS) and 4 × 1/2 × 2 high-definition transcranial direct current stimulation (4 × 1/2 × 2 HD-tDCS) are largely center-based, with unilateral-target focus—bilateral excitation would require two rTMS/4 × 1 HD-tDCS systems. We developed a system that allows for focal, non-invasive, self-applied, and simultaneous bilateral excitatory M1 stimulation, supporting long-term home-based treatment with a well-tolerated wearable battery-powered device. Here, we overviewed the most employed M1 neuromodulation methods, from invasive techniques to non-invasive TMS and tDCS. The evaluation extended from non-invasive diffuse asymmetric bilateral (M1-supraorbital [SO] tDCS), non-invasive and invasive unilateral focal (4 × 1/2 × 2 HD-tDCS, rTMS, MCS), to non-invasive and invasive bilateral bipolar (M1-M1 tDCS, MCS), before outlining our proposal for a neuromodulatory system with unique features. Computational models were applied to compare brain current flow for current laboratory-based unilateral M1¹ and bilateral M1² HD-tDCS models with a functional home-based M1¹⁻² HD-tDCS prototype. We concluded the study by discussing the promising concept of bilateral excitatory M1 stimulation for more global pain relief, which is also non-invasive, focal, and home-based.

Characterization and localization of upper and lower extremity motor improvements in STN DBS for Parkinson's disease

INTRODUCTION

Subthalamic deep brain stimulation (STN DBS) may have differential effects on cardinal motor signs of Parkinson's disease (PD) in the upper and lower extremities. In addition, sites of maximally effective DBS for each sign and extremity may be distinct. Our study seeks to elucidate these structure-function relationships.

METHODS

We applied an ordinary least squares linear regression model to measure motor effects of STN DBS on upper (UE) and lower (LE) extremity tremor, rigidity, and bradykinesia. We then applied an atlas-independent electrical-field model to identify sites of maximally effective stimulation for each sign and each extremity. Distances between sites and statistical power to resolve differences were calculated.

RESULTS

In our study population (n = 78 patients), STN DBS improved all cardinal motor signs (β = 0.64, p < .05). Improvement magnitudes were tremor > rigidity > bradykinesia. Effects of STN DBS on UE versus LE signs were statistically equal for tremor and bradykinesia, but greater for UE rigidity than LE rigidity (β = 0.19, p < .05). UE maximal-effect loci were lateral, anterior, and dorsal to LE loci, but were not statistically resolved, despite sufficient statistical power to resolve differences of ≤0.48 mm (p < .05) between maximally effective loci of stimulation.

CONCLUSION

STN DBS produces differential effects on UE and LE rigidity, but not for tremor or bradykinesia. This finding is not explained by distinct UE and LE loci of maximally effective stimulation. Instead, we hypothesize that downstream effects of STN DBS on motor networks and limb biomechanics are responsible for observed differences in UE and LE responses.

Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface

Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

G-CSF secreted by mutant IDH1 glioma stem cells abolishes myeloid cell immunosuppression and enhances the efficacy of immunotherapy

Mutant isocitrate-dehydrogenase 1 (mIDH1) synthesizes the oncometabolite 2-hydroxyglutarate (2HG), which elicits epigenetic reprogramming of the glioma cells’ transcriptome by inhibiting DNA and histone demethylases. We show that the efficacy of immune-stimulatory gene therapy (TK/Flt3L) is enhanced in mIDH1 gliomas, due to the reprogramming of the myeloid cells’ compartment infiltrating the tumor microenvironment (TME). We uncovered that the immature myeloid cells infiltrating the mIDH1 TME are mainly nonsuppressive neutrophils and preneutrophils. Myeloid cell reprogramming was triggered by granulocyte colony-stimulating factor (G-CSF) secreted by mIDH1 glioma stem/progenitor-like cells. Blocking G-CSF in mIDH1 glioma–bearing mice restores the inhibitory potential of the tumor-infiltrating myeloid cells, accelerating tumor progression. We demonstrate that G-CSF reprograms bone marrow granulopoiesis, resulting in noninhibitory myeloid cells within mIDH1 glioma TME and enhancing the efficacy of immune-stimulatory gene therapy.

Classifier Using Pontine Radial Diffusivity and Symptom Duration Accurately Predicts Recurrence of Trigeminal Neuralgia After Microvascular Decompression: A Pilot Study and Algorithm Description

BACKGROUND

Preprocedure diffusion tensor magnetic resonance imaging (MRI) may predict the response of trigeminal neuralgia (TN) patients to Gamma Knife (Elekta AB) and microvascular decompression (MVD).

OBJECTIVE

To test this hypothesis using pontine-segment diffusion tensor MRI radial diffusivity (RD), a known biomarker for demyelination, to predict TN recurrence following MVD.

METHODS

RD from the pontine segment of the trigeminal tract was extracted in a semiautomated and blinded fashion and normalized to background pontine RD. Following validation against published results, the relationship of normalized RD to symptom duration (DS) was measured. Both parameters were then introduced into machine-learning classifiers to group patient outcomes as TN remission or recurrence. Performance was evaluated in an observational study with leave-one-out cross-validation to calculate accuracy, sensitivity, specificity, and receiver operating characteristic curves.

RESULTS

The study population included 22 patients with TN type 1 (TN1). There was a negative correlation of normalized RD and preoperative symptom duration (P = .035, R2 = .20). When pontine-segment RD and DS were included as input variables, 2 classifiers predicted pain-free remission versus eventual recurrence with 85% accuracy, 83% sensitivity, and 86% specificity (leave-one-out cross-validation; P = .029) in a cohort of 13 patients undergoing MVD.

CONCLUSION

Pontine-segment RD and DS accurately predict MVD outcomes in TN1 and provide further evidence that diffusion tensor MRI contains prognostic information. Use of a classifier may allow more accurate risk stratification for neurosurgeons and patients considering MVD as a treatment for TN1. These findings provide further insight into the relationship of pontine microstructure, represented by RD, and the pathophysiology of TN.

Intrathecal catheter for severe low back pain during deep brain stimulation placement: illustrative case

BACKGROUND

Deep brain stimulation (DBS) is a U.S. Food and Drug Administration–approved therapy for medically refractory Parkinson’s disease, essential tremor, and other neurological conditions. The procedure requires prolonged immobility and can result in significant patient discomfort, potentially limiting patient selection. In addition, surgical requirements necessitate avoidance of medications that may alter or suppress the patient’s arousal or baseline tremor during macrostimulation testing.

OBSERVATIONS

In this study, the authors describe the use of continuous spinal anesthesia with local anesthetic to manage a patient with severe back pain who was intolerant of semisupine position during stereotactic computed tomography and stage 1 of DBS placement.

LESSONS

Continuous spinal anesthesia is an effective strategy to manage patients with severe back pain undergoing DBS surgery for upper extremity motor symptoms.

A Light Tolerant Neural Recording IC for Near-Infrared-Powered Free Floating Motes

A key challenge for near-infrared (NIR) powered neural recording ICs is to maintain robust operation in the presence of parasitic short circuit current from junction diodes when exposed to light. This is especially so when intentional currents are kept small to reduce power consumption. We present a neural recording IC that is tolerant up to 300 μW/mm² light exposure (above tissue limit) and consumes 0.57 μW at 38°C, making it lowest power among standalone motes while incorporating on-chip feature extraction and individual gain control.

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.

Quantitative Sensory Testing of Spinal Cord and Dorsal Root Ganglion Stimulation in Chronic Pain Patients

BACKGROUND/OBJECTIVES

The physiological mechanisms underlying the pain-modulatory effects of clinical neurostimulation therapies, such as spinal cord stimulation (SCS) and dorsal root ganglion stimulation (DRGS), are only partially understood. In this pilot prospective study, we used patient-reported outcomes (PROs) and quantitative sensory testing (QST) to investigate the physiological effects and possible mechanisms of action of SCS and DRGS therapies.

MATERIALS AND METHODS

We tested 16 chronic pain patients selected for SCS and DRGS therapy, before and after treatment. PROs included pain intensity, pain-related symptoms (e.g., pain interference, pain coping, sleep interference) and disability, and general health status. QST included assessments of vibration detection theshold (VDT), pressure pain threshold (PPT) and tolerance (PPToL), temporal summation (TS), and conditioned pain modulation (CPM), at the most painful site.

RESULTS

Following treatment, all participants reported significant improvements in PROs (e.g., reduced pain intensity [p < 0.001], pain-related functional impairment [or pain interference] and disability [p = 0.001 for both]; better pain coping [p = 0.03], sleep [p = 0.002]), and overall health [p = 0.005]). QST showed a significant treatment-induced increase in PPT (p = 0.002) and PPToL (p = 0.011), and a significant reduction in TS (p = 0.033) at the most painful site, but showed no effects on VDT and CPM. We detected possible associations between a few QST measures and a few PROs. Notably, higher TS was associated with increased pain interference scores at pre-treatment (r = 0.772, p = 0.009), and a reduction in TS was associated with the reduction in pain interference (r = 0.669, p = 0.034) and pain disability (r = 0.690, p = 0.027) scores with treatment.

CONCLUSIONS

Our preliminary findings suggest significant clinical and therapeutic benefits associated with SCS and DRGS therapies, and the possible ability of these therapies to modulate pain processing within the central nervous system. Replication of our pilot findings in future, larger studies is necessary to characterize the physiological mechanisms of SCS and DRGS therapies.

Atlas-independent, N-of-1 tissue activation modeling to map optimal regions of subthalamic deep brain stimulation for Parkinson disease

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Neuroanatomical variations among patients are obscured in atlas-based VTA modeling.

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N-of-1 neuroanatomical and VTA modeling enables patient-level precision.

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Mean optimal stimulation is dorsomedial to the STN, near its posterior half.

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Individual VTAs deviate from optimal stimulation sites to varying degrees.

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Optimal stimulation sites for rigidity, bradykinesia, and tremor partially overlap.

Background

Motor outcomes after subthalamic deep brain stimulation (STN DBS) for Parkinson disease (PD) vary considerably among patients and strongly depend on stimulation location. The objective of this retrospective study was to map the regions of optimal STN DBS for PD using an atlas-independent, fully individualized (N-of-1) tissue activation modeling approach and to assess the relationship between patient-level therapeutic volumes of tissue activation (VTAs) and motor improvement.

Methods

The stimulation-induced electric field for 40 PD patients treated with bilateral STN DBS was modeled using finite element analysis. Neurostimulation models were generated for each patient, incorporating their individual STN anatomy, DBS lead position and orientation, anisotropic tissue conductivity, and clinical stimulation settings. A voxel-based analysis of the VTAs was then used to map the optimal location of stimulation. The amount of stimulation in specific regions relative to the STN was measured and compared between STNs with more and less optimal stimulation, as determined by their motor improvement scores and VTA. The relationship between VTA location and motor outcome was then assessed using correlation analysis. Patient variability in terms of STN anatomy, active contact position, and VTA location were also evaluated. Results from the N-of-1 model were compared to those from a simplified VTA model.

Results

Tissue activation modeling mapped the optimal location of stimulation to regions medial, posterior, and dorsal to the STN centroid. These regions extended beyond the STN boundary towards the caudal zona incerta (cZI). The location of the VTA and active contact position differed significantly between STNs with more and less optimal stimulation in the dorsal-ventral and anterior-posterior directions. Therapeutic stimulation spread noticeably more in the dorsal and posterior directions, providing additional evidence for cZI as an important DBS target. There were significant linear relationships between the amount of dorsal and posterior stimulation, as measured by the VTA, and motor improvement. These relationships were more robust than those between active contact position and motor improvement. There was high variability in STN anatomy, active contact position, and VTA location among patients. Spherical VTA modeling was unable to reproduce these results and tended to overestimate the size of the VTA.

Conclusion

Accurate characterization of the spread of stimulation is needed to optimize STN DBS for PD. High variability in neuroanatomy, stimulation location, and motor improvement among patients highlights the need for individualized modeling techniques. The atlas-independent, N-of-1 tissue activation modeling approach presented in this study can be used to develop and evaluate stimulation strategies to improve clinical outcomes on an individual basis.

Localization of motor and verbal fluency effects in subthalamic DBS for Parkinson's disease

INTRODUCTION

Subthalamic nucleus deep brain stimulation (STN DBS) improves cardinal motor symptoms of Parkinson's disease (PD) but can worsen verbal fluency (VF). An optimal site of stimulation for overall motor improvement has been previously identified using an atlas-independent, fully individualized, field-modeling approach. This study examines if cardinal motor components (bradykinesia, tremor, and rigidity) share this identified optimal improvement site and if there is co-localization with a site that worsens VF.

METHODS

An atlas-independent, field-modeling approach was used to identify sites of maximal STN DBS effect on overall and cardinal motor symptoms and VF in 60 patients. Anatomic coordinates were referenced to the STN midpoint. Symptom severity was assessed with the MDS-UPDRS part III and established VF scales.

RESULTS

Sites for improved bradykinesia and rigidity co-localized with each other and the overall part III site (0.09 mm lateral, 0.93 mm posterior, 1.75 mm dorsal). The optimal site for tremor was posterior to this site (0.10 mm lateral, 1.40 mm posterior, 1.93 mm dorsal). Semantic and phonemic VF sites were indistinguishable and co-localized medial to the motor sites (0.32 mm medial, 1.18 mm posterior, 1.74 mm dorsal).

CONCLUSION

This study identifies statistically distinct, maximally effective stimulation sites for tremor improvement, VF worsening, and overall and other cardinal motor improvements in STN DBS. Current electrode sizes and voltage settings stimulate all of these sites simultaneously. However, future targeted lead placement and focused directional stimulation may avoid VF worsening while maintaining motor improvements in STN DBS.

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.

Magnetic resonance imaging of human neural stem cells in rodent and primate brain

Stem cell transplantation therapies are currently under investigation for central nervous system disorders. Although preclinical models show benefit, clinical translation is somewhat limited by the absence of reliable noninvasive methods to confirm targeting and monitor transplanted cells in vivo. Here, we assess a novel magnetic resonance imaging (MRI) contrast agent derived from magnetotactic bacteria, magneto‐endosymbionts (MEs), as a translatable methodology for in vivo tracking of stem cells after intracranial transplantation. We show that ME labeling provides robust MRI contrast without impairment of cell viability or other important therapeutic features. Labeled cells were visualized immediately post‐transplantation and over time by serial MRI in nonhuman primate and mouse brain. Postmortem tissue analysis confirmed on‐target grft location, and linear correlations were observed between MRI signal, cell engraftment, and tissue ME levels, suggesting that MEs may be useful for determining graft survival or rejection. Overall, these findings indicate that MEs are an effective tool for in vivo tracking and monitoring of cell transplantation therapies with potential relevance to many cellular therapy applications.

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces

The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.

Caregiver burden worsens in the second year after subthalamic nucleus deep brain stimulation for Parkinson's disease

BACKGROUND

Caregiver burden (CB) in Parkinson's disease (PD) does not improve in the short term after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS), despite motor improvement. This may be due to increased caregiver demands after surgery or the possibility that DBS unresponsive non-motor factors, such as executive dysfunction, contribute to CB.

OBJECTIVE

To evaluate the trajectory of CB in year 2 following bilateral STN DBS surgery for PD, and to test whether post-operative CB changes correlate with changes in executive function in a subgroup with available neuropsychological testing.

METHODS

This retrospective analysis included 35 patients with PD whose caregivers completed the Caregiver Burden Inventory (CBI) at baseline and between 9 and 24 months after bilateral STN DBS. 14 of these patients had neuropsychological testing both at baseline and within 6 months of their follow up CBI assessment.

RESULTS

CBI scores showed worsened CB from baseline to follow-up (16.4-21.5, p = 0.006). There was no correlation between change in executive function and change in CBI in the smaller subsample.

CONCLUSION

CB worsens in the 2 years after bilateral STN DBS despite improvement in motor symptoms and is not associated with change in executive dysfunction in the setting of advancing PD. These findings have implications on pre-operative counselling for patients and caregivers considering DBS for PD.

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

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.

Diffusion tensor imaging reveals microstructural differences between subtypes of trigeminal neuralgia

OBJECTIVE

Trigeminal neuralgia (TN) is an uncommon idiopathic facial pain syndrome. To assist in diagnosis, treatment, and research, TN is often classified as type 1 (TN1) when pain is primarily paroxysmal and episodic or type 2 (TN2) when pain is primarily constant in character. Recently, diffusion tensor imaging (DTI) has revealed microstructural changes in the symptomatic trigeminal root and root entry zone of patients with unilateral TN. In this study, the authors explored the differences in DTI parameters between subcategories of TN, specifically TN1 and TN2, in the pontine segment of the trigeminal tract.

METHODS

The authors enrolled 8 patients with unilateral TN1, 7 patients with unilateral TN2, and 23 asymptomatic controls. Patients underwent DTI with parameter measurements in a region of interest within the pontine segment of the trigeminal tract. DTI parameters were compared between groups.

RESULTS

In the pontine segment, the radial diffusivity (p = 0.0049) and apparent diffusion coefficient (p = 0.023) values in TN1 patients were increased compared to the values in TN2 patients and controls. The DTI measures in TN2 were not statistically significant from those in controls. When comparing the symptomatic to asymptomatic sides in TN1 patients, radial diffusivity was increased (p = 0.025) and fractional anisotropy was decreased (p = 0.044) in the symptomatic sides. The apparent diffusion coefficient was increased, with a trend toward statistical significance (p = 0.066).

CONCLUSIONS

Noninvasive DTI analysis of patients with TN may lead to improved diagnosis of TN subtypes (e.g., TN1 and TN2) and improve patient selection for surgical intervention. DTI measurements may also provide insights into prognosis after intervention, as TN1 patients are known to have better surgical outcomes than TN2 patients.

Effect of mechanical and chemical treatments of arecanut (areca catechu L.) fruit husk on husk and its fibre

Arecanut husk, a biomass is generated during extraction of nut and is stored in heaps in the backyards of processing units. Arecanut husk presently has no industrial/commercial applications and is a cause of major environmental hazards. Arecanut husk is rich in good quality lingo-cellulosic fibres which have potential applications in various fields. Presently there is no proper process and machinery available to extract these fibres on a mass scale. Designing such machinery requires characterisation of husks, bonding force between fibres and the shell, etc. The aim of the present study is develop suitable mechanical and chemical treatments to facilitate easy extraction of fibres and also to study the effects of theses pretreatments on tensile property of fibres. The variety used was Shreevardhini. It was noticed that the pretreatments of husk had significant effect on fibre separation. Highest fibre recovery of about 57% was achieved with pressed husks soaked in water for 24 h. Detachment force required for fibre / fibres separation from husk was least in 2.5% KOH treated husks. There was no adverse effect of pretreatments on tensile strength of fibres. Pressing of husks resulted in maximum fibre recovery due to weakening of bonds, hence pressing of arecanut husk before mechanical fibre extraction is recommended, whereas chemical pretreatment of husks with KOH is not recommended.