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Over the past decade, stereotactically placed electrodes have become the gold standard for deep brain recording and stimulation for a wide variety of neurological and psychiatric diseases. Current electrodes, however, are limited in their spatial resolution and ability to record from small populations of neurons, let alone individual neurons. Here, we report on an innovative, customizable, monolithically integrated human-grade flexible depth electrode capable of recording from up to 128 channels and able to record at a depth of 10 cm in brain tissue. This thin, stylet-guided depth electrode is capable of recording local field potentials and single unit neuronal activity (action potentials), validated across species. This device represents an advance in manufacturing and design approaches which extends the capabilities of a mainstay technology in clinical neurology.

PurposeExisting automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.MethodsThe authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactual movement of the neonate or people near the neonate though dense optical flow estimation.ResultsUsing the methods mentioned above, 197 periods of patting activity were identified and quantified, of which 45 generated false-positive automated seizure detection events. A binary patting detection algorithm was trained with a subset of 470 event videos. This supervised detection algorithm was applied to a testing subset of 187 event videos with 8 false-positive events, which resulted in a 24% reduction in false-positive automated seizure detections and a 50% reduction in false-positive events caused by neonatal care patting, while maintaining 11 of 12 true-positive seizure detection events.ConclusionsThis work presents a novel approach to improving automated seizure detection algorithms used during neonatal video EEG monitoring. This artifact detection mechanism can improve the ability of a seizure detector algorithm to distinguish between artifact and true seizure activity.

Background: The COVID-19 pandemic has changed our working environment and divided workers into essential or nonessential statuses. Employment status is a major factor determining the amount of physical activity performed. Our purpose was to understand how employment status affects physical activity and sitting time. Methods: Between April 13 and May 4, 2020, 735 full-time employed individuals responded to a survey investigating daily life and overall health during the COVID-19 pandemic. Participants reported how much physical activity they had performed in the last 7 days. Multiple linear regressions were performed for physical activity and sitting time. Results: Physical activity was not associated with employment status. An interaction effect between hours worked and employment status was found for sitting time. Conclusions: Employment status was not related to physical activity; however, it did affect the amount of time spent sitting, with nonessential employees sitting more and working more hours than essential employees. Because greater amounts of daily total sitting time have been associated with increased risk of all-cause mortality, it is important that increased sitting time be attenuated by greater physical activity. [ABSTRACT FROM AUTHOR]

Merrigan, JJ and Martin, JR. Is the OUTPUT sports unit reliable and valid when estimating back squat and bench press concentric velocity? J Strength Cond Res 36(8): 2069–2076, 2022—This study evaluated the reliability and concurrent validity of the OUTPUT sports inertial unit to measure concentric velocity of free-weight back squat and bench press exercises. Eleven men and women performed back squat and bench press 1 repetition maximum (1RM) testing. One week later, subjects performed 3 repetitions of each exercise with 35, 45, 55, 65, 75, and 85% 1RM (18 total repetitions). The OUTPUT and 4 cable extension transducers (criterion) simultaneously recorded the mean and peak velocity. The OUTPUT had acceptable reliability for all loads except 85% 1RM for back squat and bench press (intraclass correlation coefficient = 0.72–0.96, coefficient of variation = 0.03–0.12). High systematic biases existed for the mean and peak velocity for the back squat and bench press, according to Bland-Altman plot's wide limits of agreement and ordinary least products regressions. According to Bland-Altman plots, OUTPUT tended to overestimate bench press velocity and overestimate back squat velocity at slower velocities. Least products regression analyses determined proportional bias existed for the mean and peak velocity of the back squat and peak velocity of the bench press. In conclusion, researchers and practitioners are advised not to compare velocity estimates of the OUTPUT unit with criterion devices because these methods cannot be used interchangeably. However, because of the demonstrated reliability when estimating the mean and peak velocity, strength and conditioning practitioners may find the OUTPUT unit valuable for monitoring performance of the back squat and bench press exercises. Yet, caution should be taken when evaluating loads ≥85% 1RM. [ABSTRACT FROM AUTHOR]

A hypothesis was proposed that the central nervous system controls force production by the fingers through hypothetical neural commands. The neural commands are scaled between values of 0 to 1, indicating no intentional force production or maximal voluntary contraction (MVC) force production, respectively. A matrix of interfinger connections transforms neural commands into finger forces. Two methods have been proposed to compute the interfinger connection matrix. The first method uses only single finger MVC trials and multiplies the interfinger connection matrix by a gain factor. The second method uses a neural network model based on experimental data. The performance of the two methods was compared on the MVC data and on a data set of submaximal forces, collected over a range of total forces and moments of force. The methods were compared in terms of (1) ability to predict finger forces, (2) accuracy of neural command reconstruction, and (3) preserved planarity of force data for submaximal force production task. Both methods did a reasonable job of predicting the total force in multifinger MVC trials; however, the neural network model performed better in regards to all other criteria. Overall, the results indicate that for modeling multifinger interaction the neural network method is preferable. [ABSTRACT FROM AUTHOR]

Age-predicted maximal heart rate (APMHR) is an essential measure for healthcare professionals in determining cardiovascular response to exercise testing, exertion, and prescription. Although multiple APMHR prediction equations have been validated for specific populations, the accuracy of each within a general population requires testing. We aimed to determine which APMHR equation (Fox, Gellish, Gulati, Tanaka, Arena, Astrand, Nes, Fairbarn) most accurately predicts max heart rate (HRmax) in a general population. HRmax from 99 graded treadmill exercise tests (GXT) were measured. GXTs ended upon volitional fatigue and were only included for analysis if RER > 1.10. Individual paired t-test were performed to determine if significant differences existed between measured and predicted HRmax, along with root mean square errors for each equation. Bland-Altman plots were constructed to determine agreement between equations and measured HRmax. Significant differences between measured and predicted HRmax were found for the Gulati, Astrand, Nes, and Fairbarn (male) equations (p < 0.05). Bland-Altman plots revealed wide limits of agreement for all nine APMHR equations, suggesting poor agreement between measured and predicted HRmax. Proportional bias indicates that prediction equations under and overestimated HRmax in individuals with lower and higher measured HRmax, respectively, with the exception of the Fox equation. All equations used in this study show poor agreement between measured HRmax and APMHR. The Fox equation may represent the best option for a general population as it is less likely to under or overestimate based on individual HRmax. Individuals should use data from GXTs to determine HRmax when applicable to ensure accuracy. [ABSTRACT FROM AUTHOR]

Shoulder joint injuries are common for professional firefighters. A potential cause of shoulder injury is an imbalance between anterior (push) and posterior (pull) shoulder joint musculature. Understanding what contributes to these imbalances may help to identify areas needing improvement. The purpose of this study was to investigate different push to pull (P2P) ratios and the relationships among common upper body fitness assessments, body composition, and push to pull (P2P) ratios in firefighters. Thirty-three professional firefighters completed the following testing protocol: one-repetition maximum (1RM) bench press, pull-up repetitions to failure, push-up repetitions to failure, and a body composition assessment. The endurance P2P (eP2P) was computed by dividing the number of push-up by pull-up repetitions, while strength P2P (sP2P) was the relative 1RM divided by pull-up repetitions. Bivariate relationships among variables were assessed with correlation coefficients and linear regression assessed association between eP2P and sP2P (p ≤ 0.05). The sP2P and eP2P were not associated (R2 = 0.032, p = 0.99). Strength P2P was related with bench press 1RM (r = 0.80) and push-ups (r = 0.40). Endurance P2P was related with pull-up repetitions (r = -0.62), body fat percentage (r = 0.40), and fat mass index (r = 0.34). The results of the present study suggest sP2P and eP2P ratios should not be used interchangeably. To improve sP2P and eP2P for firefighters, it is recommended to improve the strength of anterior and posterior upper body musculature, respectively, and reduce total body fat mass. [ABSTRACT FROM AUTHOR]

Clinical Question: Does the elevation training mask (ETM) increase VO2max in healthy individuals who are physically active? Clinical Bottom Line: The current best evidence shows no difference in V02max improvement compared to training without wearing the ETM while following an exercise program. Several studies demonstrated improvement in measures of respiratory function when wearing the ETM. Further research should investigate changes in physiological variables other than VO2max, specifically those related to respiratory function. [ABSTRACT FROM AUTHOR]

This study investigated the effects of modifying contact finger forces in one direction-normal or tangential-on the entire set of the contact forces, while statically holding an object. Subjects grasped a handle instrumented with finger force-moment sensors, maintained it at rest in the air, and then slowly: (1) increased the grasping force, (2) tried to spread fingers apart, and (3) tried to squeeze fingers together. Analysis was mostly performed at the virtual finger (VF) level (the VF is an imaginable finger that generates the same force and moment as the four fingers combined). For all three tasks there were statistically significant changes in the VF normal and tangential forces. For finger spreading/squeezing the tangential force neutral point was located between the index and middle fingers. We conclude that the internal forces are regulated as a whole, including adjustments in both normal and tangential force, instead of only a subset of forces (normal or tangential). The effects of such factors as EFFORT and TORQUE were additive; their interaction was not statistically significant, thus supporting the principle of superposition in human prehension. [ABSTRACT FROM AUTHOR]