Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.
KEY WORDS 10 motion compensation, whole-cell, in vivo, automated patching, autopatching, optogenetics 11 12
Abstract 13Whole-cell patch-clamp recording in vivo is the gold-standard method for measuring subthreshold 14 electrophysiology from single cells during behavioural tasks, sensory stimulations, and 15 optogenetic manipulation. However, these recordings require a tight, gigaohm resistance, seal 16 between a glass pipette electrode's aperture and a cell's membrane. These seals are difficult to 17 form, especially in vivo, in part because of a strong dependence on the distance between the pipette 18 aperture and cell membrane. We elucidate and utilize this dependency to develop an autonomous 19 109 1.
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