Zachary T. Irwin

Universal automated high frequency oscillation detector for real-time, long term EEG.

OBJECTIVE Interictal high frequency oscillations (HFOs) in intracranial EEG are a potential biomarker of epilepsy, but current automated HFO detectors require human review to remove artifacts. Our objective is to automatically redact false HFO detections, facilitating clinical use of interictal HFOs. METHODS Intracranial EEG data from 23 patients were processed with automated detectors of HFOs and artifacts. HFOs not concurrent with artifacts were labeled quality HFOs (qHFOs). Methods were validated by human review on a subset of 2000 events. The correlation of qHFO rates with the seizure onset zone (SOZ) was assessed via (1) a retrospective asymmetry measure and (2) a novel quasi-prospective algorithm to identify SOZ. RESULTS Human review estimated that less than 12% of qHFOs are artifacts, whereas 78.5% of redacted HFOs are artifacts. The qHFO rate was more correlated with SOZ (p=0.020, Wilcoxon signed rank test) and resected volume (p=0.0037) than baseline detections. Using qHFOs, our algorithm was able to determine SOZ in 60% of the ILAE Class I patients, with all algorithmically-determined SOZs fully within the resected volumes. CONCLUSIONS The algorithm reduced false-positive HFO detections, improving the precision of the HFO-biomarker. SIGNIFICANCE These methods provide a feasible strategy for HFO detection in real-time, continuous EEG with minimal human monitoring of data quality.

Disruption of corticocortical information transfer during ketamine anesthesia in the primate brain.

The neural mechanisms of anesthetic-induced unconsciousness have yet to be fully elucidated, in part because of the diverse molecular targets of anesthetic agents. We demonstrate, using intracortical recordings in macaque monkeys, that information transfer between structurally connected cortical regions is disrupted during ketamine anesthesia, despite preserved primary sensory representation. Furthermore, transfer entropy, an information-theoretic measure of directed connectivity, decreases significantly between neuronal units in the anesthetized state. This is the first direct demonstration of a general anesthetic disrupting corticocortical information transfer in the primate brain. Given past studies showing that more commonly used GABAergic drugs inhibit surrogate measures of cortical communication, this finding suggests the potential for a common network-level mechanism of anesthetic-induced unconsciousness.

Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates.

OBJECTIVE We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model. APPROACH A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data. Histology was obtained seventeen weeks post-implantation to measure gliosis. MAIN RESULTS From week 1-3, mean impedance and amplitude increased at rates of 115.8 kΩ/week and 23.1 μV/week, respectively. This initial ramp up in impedance and amplitude was observed across all arrays, and is consistent with biofouling (increasing interface resistivity) and edema clearing (increasing tissue resistivity), respectively, in the model. Beyond week 3, the trends leveled out. Histology showed that thin scars formed around the electrodes. In the model, scarring could not match the in vivo data. However, a thin interface layer at the electrode tip could. Despite having a large effect on impedance, interface resistivity did not have a noticeable effect on amplitude. SIGNIFICANCE This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the recording site-tissue interface rather than elimination of the glial scar.

Chronic recording of hand prosthesis control signals via a regenerative peripheral nerve interface in a rhesus macaque

OBJECTIVE Loss of even part of the upper limb is a devastating injury. In order to fully restore natural function when lacking sufficient residual musculature, it is necessary to record directly from peripheral nerves. However, current approaches must make trade-offs between signal quality and longevity which limit their clinical potential. To address this issue, we have developed the regenerative peripheral nerve interface (RPNI) and tested its use in non-human primates. APPROACH The RPNI consists of a small, autologous partial muscle graft reinnervated by a transected peripheral nerve branch. After reinnervation, the graft acts as a bioamplifier for descending motor commands in the nerve, enabling long-term recording of high signal-to-noise ratio (SNR), functionally-specific electromyographic (EMG) signals. We implanted nine RPNIs on separate branches of the median and radial nerves in two rhesus macaques who were trained to perform cued finger movements. MAIN RESULTS No adverse events were noted in either monkey, and we recorded normal EMG with high SNR (>8) from the RPNIs for up to 20 months post-implantation. Using RPNI signals recorded during the behavioral task, we were able to classify each monkeys finger movements as flexion, extension, or rest with >96% accuracy. RPNI signals also enabled functional prosthetic control, allowing the monkeys to perform the same behavioral task equally well with either physical finger movements or RPNI-based movement classifications. SIGNIFICANCE The RPNI signal strength, stability, and longevity demonstrated here represents a promising method for controlling advanced prosthetic limbs and fully restoring natural movement.

Effect of sampling rate and filter settings on High Frequency Oscillation detections

OBJECTIVE High Frequency Oscillations (HFOs) are being studied as a biomarker of epilepsy, yet it is unknown how various acquisition parameters at different centers affect detection and analysis of HFOs. This paper specifically quantifies effects of sampling rate (FS) and anti-aliasing filter (AAF) positions on automated HFO detection. METHODS HFOs were detected on intracranial EEG recordings (17 patients) with 5kHz FS. HFO detection was repeated on downsampled and/or filtered copies of the EEG data, mimicking sampling rates and low-pass filter settings of various acquisition equipment. For each setting, we compared the HFO detection sensitivity, HFO features, and ability to identify the ictal onset zone. RESULTS The relative sensitivity remained above 80% for either FS ⩾2kHz or AAF ⩾500Hz. HFO feature distributions were consistent (AUROC<0.7) down to 1kHz FS or 200Hz AAF. HFO rate successfully identified ictal onset zone over most settings. HFO peak frequency was highly variable under most parameters (Spearman correlation<0.5). CONCLUSIONS We recommend at least FS ⩾2kHz and AAF ⩾500Hz to detect HFOs. Additionally, HFO peak frequency is not robust at any setting: the same HFO event can be variably classified either as a ripple (<200Hz) or fast ripple (>250Hz) under different acquisition settings. SIGNIFICANCE These results inform clinical centers on requirements to analyze HFO rates and features.

Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.High frequency oscillations (HFO) are a promising biomarker for identifying epileptogenic zones without the need to monitor spontaneous seizure episodes. Here the authors report that there is much variability in the location of HFOs offering a note of caution toward using HFO locations from short recordings as a guide for surgery.

Closed-Loop Continuous Hand Control via Chronic Recording of Regenerative Peripheral Nerve Interfaces

Loss of the upper limb imposes a devastating interruption to everyday life. Full restoration of natural arm control requires the ability to simultaneously control multiple degrees of freedom of the prosthetic arm and maintain that control over an extended period of time. Current clinically available myoelectric prostheses do not provide simultaneous control or consistency for transradial amputees. To address this issue, we have implemented a standard Kalman filter for continuous hand control using intramuscular electromyography (EMG) from both regenerative peripheral nerve interfaces (RPNI) and an intact muscle within non-human primates. Seven RPNIs and one intact muscle were implanted with indwelling bipolar intramuscular electrodes in two rhesus macaques. Following recuperations, function-specific EMG signals were recorded and then fed through the Kalman filter during a hand-movement behavioral task to continuously predict the monkey’s finger position. We were able to reconstruct continuous finger movement offline with an average correlation of

Development of regenerative peripheral nerve interfaces for motor control of neuroprosthetic devices

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Abstract 24: Successful Control of Virtual and Robotic Hands using Neuroprosthetic Signals from Regenerative Peripheral Nerve Interfaces in a Human Subject

and a root mean squared error (RMSE) of 0.12 between actual and predicted position from two macaques. This finger movement prediction was also performed in real time to enable closed-loop neural control of a virtual hand. Compared with physical hand control, neural control performance was slightly slower but maintained an average target hit success rate of 96.70%. Recalibration longevity measurements maintained consistent average correlation over time but had a significant change in RMSE (