Feasibility of Using EMG for Early Detection of the Facial Nerve During Robotic Direct Cochlear Access

HYPOTHESIS Facial nerve monitoring can be used synchronous with a high-precision robotic tool as a functional warning to prevent of a collision of the drill bit with the facial nerve during direct cochlear access (DCA). BACKGROUND Minimally invasive direct cochlear access (DCA) aims to eliminate the need for a mastoidectomy by drilling a small tunnel through the facial recess to the cochlea with the aid of stereotactic tool guidance. Because the procedure is performed in a blind manner, structures such as the facial nerve are at risk. Neuromonitoring is a commonly used tool to help surgeons identify the facial nerve (FN) during routine surgical procedures in the mastoid. Recently, neuromonitoring technology was integrated into a commercially available drill system enabling real-time monitoring of the FN. The objective of this study was to determine if this drilling system could be used to warn of an impending collision with the FN during robot-assisted DCA. MATERIALS AND METHODS The sheep was chosen as a suitable model for this study because of its similarity to the human ear anatomy. The same surgical workflow applicable to human patients was performed in the animal model. Bone screws, serving as reference fiducials, were placed in the skull near the ear canal. The sheep head was imaged using a computed tomographic scanner and segmentation of FN, mastoid, and other relevant structures as well as planning of drilling trajectories was carried out using a dedicated software tool. During the actual procedure, a surgical drill system was connected to a nerve monitor and guided by a custom built robot system. As the planned trajectories were drilled, stimulation and EMG response signals were recorded. A postoperative analysis was achieved after each surgery to determine the actual drilled positions. RESULTS Using the calibrated pose synchronized with the EMG signals, the precise relationship between distance to FN and EMG with 3 different stimulation intensities could be determined for 11 different tunnels drilled in 3 different subjects. CONCLUSION From the results, it was determined that the current implementation of the neuromonitoring system lacks sensitivity and repeatability necessary to be used as a warning device in robotic DCA. We hypothesize that this is primarily because of the stimulation pattern achieved using a noninsulated drill as a stimulating probe. Further work is necessary to determine whether specific changes to the design can improve the sensitivity and specificity.

Effect of selective dorsal rhizotomy on gait in children with bilateral spastic paresis: kinematic and EMG-pattern changes

Selective dorsal rhizotomy (SDR) is an effective treatment for reducing spasticity and improving gait in children with spastic cerebral palsy. Data concerning muscle activity changes after SDR treatment are limited.

Differential modulation of ROS signals and other mitochondrial parameters by the antioxidants MitoQ, resveratrol and curcumin in human adipocytes

Abstract Mitochondrial reactive oxygen species (ROS) have been demonstrated to play an important role as signaling and regulating molecules in human adipocytes. In order to evaluate the differential modulating roles of antioxidants, we treated human adipocytes differentiated from human bone marrow-derived mesenchymal stem cells with MitoQ, resveratrol and curcumin. The effects on ROS, viability, mitochondrial respiration and intracellular ATP levels were examined. MitoQ lowered both oxidizing and reducing ROS. Resveratrol decreased reducing and curcumin oxidizing radicals only. All three substances slightly decreased state III respiration immediately after addition. After 24 h of treatment, MitoQ inhibited both basal and uncoupled oxygen consumption, whereas curcumin and resveratrol had no effect. Intracellular ATP levels were not altered. This demonstrates that MitoQ, resveratrol and curcumin exert potent modulating effects on ROS signaling in human adipocyte with marginal effects on metabolic parameters.

Fiberoptic system for recording dendritic calcium signals in layer 5 neocortical pyramidal cells in freely moving rats

Calcium influx into the dendritic tufts of layer 5 neocortical pyramidal neurons modifies a number of important cellular mechanisms. It can trigger local synaptic plasticity and switch the firing properties from regular to burst firing. Due to methodological limitations, our knowledge about Ca2+ spikes in the dendritic tuft stems mostly from in vitro experiments. However, it has been speculated that regenerative Ca2+ events in the distal dendrites correlate with distinct behavioral states. Therefore it would be most desirable to be able to record these Ca2+ events in vivo, preferably in the behaving animal. Here, we present a novel approach for recording Ca2+ signals in the dendrites of populations of layer 5 pyramidal neurons in vivo, which ensures that all recorded fluorescence changes are due to intracellular Ca2+ signals in the apical dendrites. The method has two main features: 1) bolus loading of layer 5 with a membrane-permeant Ca2+ dye resulting in specific loading of pyramidal cell dendrites in the upper layers and 2) a fiberoptic cable attached to a gradient index lens and a prism reflecting light horizontally at 90 degrees to the angle of the apical dendrites. We demonstrate that the in vivo signal-to-noise ratio recorded with this relatively inexpensive and easy-to-implement fiberoptic-based device is comparable to conventional camera-based imaging systems used in vitro. In addition, the device is flexible and lightweight and can be used for recording Ca2+ signals in the distal dendritic tuft of freely behaving animals.

Perceptual Learning via Modification of Cortical Top-Down Signals

The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1. However, any rewiring of V1 would deteriorate other perceptual abilities different from the trained one. We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact. Consistent with biological data, the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1. Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning.

Automatic Reduction of Artifacts in EEG-Signals

Electroencephalograms (EEG) are often contaminated with high amplitude artifacts limiting the usability of data. Methods that reduce these artifacts are often restricted to certain types of artifacts, require manual interaction or large training data sets. Within this paper we introduce a novel method, which is able to eliminate many different types of artifacts without manual intervention. The algorithm first decomposes the signal into different sub-band signals in order to isolate different types of artifacts into specific frequency bands. After signal decomposition with principal component analysis (PCA) an adaptive threshold is applied to eliminate components with high variance corresponding to the dominant artifact activity. Our results show that the algorithm is able to significantly reduce artifacts while preserving the EEG activity. Parameters for the algorithm do not have to be identified for every patient individually making the method a good candidate for preprocessing in automatic seizure detection and prediction algorithms.