Classification of directionally specific vagus nerve activity using an upper airway obstruction model in anesthetized rodents

Electrical signals from the peripheral nervous system have the potential to provide the necessary motor, sensory or autonomic information for implementing closed-loop control of neuroprosthetic or neuromodulatory systems. However, developing methods to recover information encoded in these signals is a significant challenge. Our goal was to test the feasibility of measuring physiologically generated nerve action potentials that can be classified as sensory or motor signals. A tetrapolar recording nerve cuff electrode was used to measure vagal nerve (VN) activity in a rodent model of upper airway obstruction. The effect of upper airway occlusions on VN activity related to respiration (RnP) was calculated and compared for 4 different cases: (1) intact VN, (2) VN transection only proximal to recording electrode, (3) VN transection only distal to the recording electrode, and (4) transection of VN proximal and distal to electrode. We employed a Support Vector Machine (SVM) model with Gaussian Kernel to learn a model capable of classifying efferent and afferent waveforms obtained from the tetrapolar electrode. In vivo results showed that the RnP values decreased significantly during obstruction by 91.7% ± 3.1%, and 78.2% ± 3.4% for cases of intact VN or proximal transection, respectively. In contrast, there were no significant changes for cases of VN transection at the distal end or both ends of the electrode. The SVM model yielded an 85.8% accuracy in distinguishing motor and sensory signals. The feasibility of measuring low-noise directionally-sensitive neural activity using a tetrapolar nerve cuff electrode along with the use of an SVM classifier was shown. Future experimental work in chronic implant studies is needed to support clinical translatability.

Simultaneous Intra- and Extracochlear Electrocochleography During Cochlear Implantation to Enhance Response Interpretation

The use of electrocochleography (ECochG) for providing real-time feedback of cochlear function during cochlear implantation is receiving increased attention for preventing cochlear trauma and preserving residual hearing. Although various studies investigated the relationship between intra-operative ECochG measurements and surgical outcomes in recent years, the limited interpretability of ECochG response changes leads to conflicting study results and prevents the adoption of this method for clinical use. Specifically, the movement of the recording electrode with respect to the different signal generators in intracochlear recordings makes the interpretation of signal changes with respect to cochlear trauma difficult. Here, we demonstrate that comparison of ECochG signals recorded simultaneously from intracochlear locations and from a fixed extracochlear location can potentially allow a differentiation between traumatic and atraumatic signal changes in intracochlear recordings. We measured ECochG responses to 500 Hz tone bursts with alternating starting phases during cochlear implant insertions in six human cochlear implant recipients. Our results show that an amplitude decrease with associated near 180° phase shift and harmonic distortions in the intracochlear difference curve during the first half of insertion was not accompanied by a decrease in the extracochlear difference curve’s amplitude ( n = 1), while late amplitude decreases in intracochlear difference curves (near full insertion, n = 2) did correspond to extracochlear amplitude decreases. These findings suggest a role for phase shifts, harmonic distortions, and recording location in interpreting intracochlear ECochG responses.

Computation of the electroencephalogram (EEG) from network models of point neurons

The electroencephalogram (EEG) is one of the main tools for non-invasively studying brain function and dysfunction. To better interpret EEGs in terms of neural mechanisms, it is important to compare experimentally recorded EEGs with the output of neural network models. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neuron networks cannot directly generate an EEG, since EEGs are generated by spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of the EEG with a combination of quantities defined in point-neuron network models. We constructed several different candidate approximations (or proxies) of the EEG that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and specific combinations of synaptic currents. We then evaluated how well each proxy reconstructed a realistic ground-truth EEG obtained when the synaptic input currents of the LIF network were fed into a three-dimensional (3D) network model of multi-compartmental neurons with realistic cell morphologies. We found that a new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states of the LIF point-neuron network. The new linear proxies explained most of the variance (85-95%) of the ground-truth EEG for a wide range of cell morphologies, distributions of presynaptic inputs, and position of the recording electrode. Non-linear proxies, obtained using a convolutional neural network (CNN) to predict the EEG from synaptic currents, increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations and thereby allow a quantitative comparison between computational models and experimental EEG recordings.Author summaryNetworks of point neurons are widely used to model neural dynamics. Their output, however, cannot be directly compared to the electroencephalogram (EEG), which is one of the most used tools to non-invasively measure brain activity. To allow a direct integration between neural network theory and empirical EEG data, here we derived a new mathematical expression, termed EEG proxy, which estimates with high accuracy the EEG based simply on the variables available from simulations of point-neuron network models. To compare and validate these EEG proxies, we computed a realistic ground-truth EEG produced by a network of simulated neurons with realistic 3D morphologies that receive the same spikes of the simpler network of point neurons. The new obtained EEG proxies outperformed previous approaches and worked well under a wide range of simulated configurations of cell morphologies, distribution of presynaptic inputs, and position of the recording electrode. The new proxies approximated well both EEG spectra and EEG evoked potentials. Our work provides important mathematical tools that allow a better interpretation of experimentally measured EEGs in terms of neural models of brain function.

Electrochemical Evaluation of a Multi-Site Clinical Depth Recording Electrode for Monitoring Cerebral Tissue Oxygen

The intracranial measurement of local cerebral tissue oxygen levels—PbtO2—has become a useful tool for the critical care unit to investigate severe trauma and ischemia injury in patients. Our preliminary work in animal models supports the hypothesis that multi-site depth electrode recording of PbtO2 may give surgeons and critical care providers needed information about brain viability and the capacity for better recovery. Here, we present a surface morphology characterization and an electrochemical evaluation of the analytical properties toward oxygen detection of an FDA-approved, commercially available, clinical grade depth recording electrode comprising 12 Pt recording contacts. We found that the surface of the recording sites is composed of a thin film of smooth Pt and that the electrochemical behavior evaluated by cyclic voltammetry in acidic and neutral electrolyte is typical of polycrystalline Pt surface. The smoothness of the Pt surface was further corroborated by determination of the electrochemical active surface, confirming a roughness factor of 0.9. At an optimal working potential of −0.6 V vs. Ag/AgCl, the sensor displayed suitable values of sensitivity and limit of detection for in vivo PbtO2 measurements. Based on the reported catalytical properties of Pt toward the electroreduction reaction of O2, we propose that these probes could be repurposed for multisite monitoring of PbtO2 in vivo in the human brain.

In Vivo Femtosecond Laser Subsurface Cortical Microtransections Attenuate Acute Rat Focal Seizures

Recent evidence shows that seizures propagate primarily through supragranular cortical layers. To selectively modify these circuits, we developed a new technique using tightly focused, femtosecond infrared laser pulses to make as small as ~100 µm-wide subsurface cortical incisions surrounding an epileptic focus. We use this “laser scalpel” to produce subsurface cortical incisions selectively to supragranular layers surrounding an epileptic focus in an acute rodent seizure model. Compared with sham animals, these microtransections completely blocked seizure initiation and propagation in 1/3 of all animals. In the remaining animals, seizure frequency was reduced by 2/3 and seizure propagation reduced by 1/3. In those seizures that still propagated, it was delayed and reduced in amplitude. When the recording electrode was inside the partially isolated cube and the seizure focus was on the outside, the results were even more striking. In spite of these microtransections, somatosensory responses to tail stimulation were maintained but with reduced amplitude. Our data show that just a single enclosing wall of laser cuts limited to supragranular layers led to a significant reduction in seizure initiation and propagation with preserved cortical function. Modification of this concept may be a useful treatment for human epilepsy.

MEMS-Actuated Carbon Fiber Microelectrode for Neural Recording

Microwire and microelectrode arrays used for cortical neural recording typically consist of tens to hundreds of recording sites, but often only a fraction of these sites are in close enough proximity to firing neurons to record single-unit activity. Recent work has demonstrated precise, depth-controllable mechanisms for the insertion of single neural recording electrodes, but these methods are mostly only capable of inserting electrodes which elicit adverse biological response. We present an electrostatic-based actuator capable of inserting individual carbon fiber microelectrodes which elicit minimal to no adverse biological response. The device is shown to insert a carbon fiber recording electrode into an agar brain phantom and can record an artificial neural signal in saline. This technique provides a platform generalizable to many microwire-style recording electrodes.