Wireless closed-loop optogenetics across the entire dorsoventral spinal cord in mice.

Optoelectronic systems can exert precise control over targeted neurons and pathways throughout the brain in untethered animals, but similar technologies for the spinal cord are not well established. In the present study, we describe a system for ultrafast, wireless, closed-loop manipulation of targeted neurons and pathways across the entire dorsoventral spinal cord in untethered mice. We developed a soft stretchable carrier, integrating microscale light-emitting diodes (micro-LEDs), that conforms to the dura mater of the spinal cord. A coating of silicone-phosphor matrix over the micro-LEDs provides mechanical protection and light conversion for compatibility with a large library of opsins. A lightweight, head-mounted, wireless platform powers the micro-LEDs and performs low-latency, on-chip processing of sensed physiological signals to control photostimulation in a closed loop. We use the device to reveal the role of various neuronal subtypes, sensory pathways and supraspinal projections in the control of locomotion in healthy and spinal-cord injured mice.

Epineural optogenetic activation of nociceptors initiates and amplifies inflammation.

Activation of nociceptor sensory neurons by noxious stimuli both triggers pain and increases capillary permeability and blood flow to produce neurogenic inflammation1,2, but whether nociceptors also interact with the immune system remains poorly understood. Here we report a neurotechnology for selective epineural optogenetic neuromodulation of nociceptors and demonstrate that nociceptor activation drives both protective pain behavior and inflammation. The wireless optoelectronic system consists of sub-millimeter-scale light-emitting diodes embedded in a soft, circumneural sciatic nerve implant, powered and driven by a miniaturized head-mounted control unit. Photostimulation of axons in freely moving mice that express channelrhodopsin only in nociceptors resulted in behaviors characteristic of pain, reflecting orthodromic input to the spinal cord. It also led to immune reactions in the skin in the absence of inflammation and potentiation of established inflammation, a consequence of the antidromic activation of nociceptor peripheral terminals. These results reveal a link between nociceptors and immune cells, which might have implications for the treatment of inflammation.

[Improved diagnostics and therapeutic decision making in traumatic peripheral nerve lesions using MR-neurography]. / Gezielte Therapieplanung bei traumatischen Nervenläsionen mittels MR-Neurographie.

INTRODUCTION: The correct diagnosis of peripheral nerve injuries is essential for choosing the correct treatment in nerve surgery. Especially, nerve defects require early diagnosis to provide quick surgical reconstruction and prevent long-term disabilities. Recent developments in MR-neurography provide surgeons with a diagnostic tool delivering precise information on the structure and possibly function of nerves. Here we describe a series of cases, that benefited from preoperative MR-neurography to identify the correct type of injury. MATERIAL AND METHODS: We demonstrate five traumatic nerve injuries which were evaluated using high-resolution MR-neurography imaging for therapeutic planning, combined with standard clinical, electrophysiological and sonography diagnostics. We show the clinical feasibility, benefit of this new technique for nerve surgery and the correlation of preoperative MR-neurography images to the intraoperative situation (in surgically managed cases). RESULTS: Two cases were successfully treated without surgery based on the intact nerve-integrity found in the MR-neurography, despite pathological electrophysiology and inconclusive sonography. In three cases, the MR-Neurography enabled a precise diagnosis and localization of the nerve lesion. Thereby, a precise surgical reconstruction of the nerve lesion was achieved, confirming the matching of MR-neurography findings and intraoperative situs. DISCUSSION: Although, systematic clinical analyses are not available yet, our data suggest that MR-neurography can help surgeons to correctly define the type of nerve injury and thus identify the appropriate treatment preoperatively. In the presented cases, MR-neurography correctly diagnosed the type of injury and therefore allowed adequate planning and decision making between non-surgical treatment, neurolysis or nerve reconstruction. We believe that MR-neurography is an emerging tool for nerve surgeons to improve the treatment of nerve injuries. CONCLUSION: MR-neurography delivers decisive information on the nerve lesion and helps to identify the necessity to operate and the correct surgical treatment.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.