Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system

Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

Video-based quantification of human movement frequency using pose estimation: A pilot study

Assessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1–4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson’s correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations (all r>0.99) with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, quantitative, video-based approach to assessment of repetitive movements in clinical populations.

Superiority of mosapride citrate to picosulfate sodium as a laxative for withdrawal from regular enemas in children with severe functional constipation

Background: Severe functional constipation (FC) with low bowel movement frequency (BMF) of ?1 day/week and hard stools oftenrequires regularly repeated enemas or often leads to enema dependency (ED). Aim: The current study aimed to compare the efficacy of mosapride citrate (Mo) with the traditional stimulant laxative picosulfate sodium (Pi) for withdrawal from ED in children with severe FC. Results: Twenty-four treatment-naïve patients who met the Rome IV diagnostic criteria for FC seen at our center for 8 years from 2012 were enrolled. Glycerin enema was repeated until the BMF was ?3.5 days/week. Simultaneously, Mo at 0.3 mg/kg/day (n=11) or Pi at 0.25 mg/kg/day (n=13) was administered concomitantly with magnesium oxide or lactulose. The proportion of withdrawal from ED was significantly higher in the Mo group than Pi group during the 4 months observational period (90.9% vs. 46.2%, respectively; p=0.034) and shorter in time to withdraw from ED (0 vs. 3.5 months, respectively; p=0.015). Conclusion: Mo is more effective than Pi for withdrawal from ED in children with severe FC.

Functional cortical localization of the tongue using corticokinematic coherence with a deep learning-assisted motion capture system

Measuring the corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer can evaluate the functional localization of the primary sensorimotor cortex (SM1) of the upper limbs. However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. We introduce and validate a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined offline using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres, with no difference between the left and right hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC based on deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1. In this approach, because no devices are placed on the tongue, magnetic noise, disturbances due to tongue movements, risk of aspiration of the device, and risk of infection to the experimenter are eliminated.

Herders’ Perceptions about Rangeland Degradation and Herd Management: A Case among Traditional and Non-Traditional Herders in Khentii Province of Mongolia

Herders play essential roles in sustaining Mongolia’s economy and rangeland conditions. As about 90% of Mongolia’s livestock grazes on natural pasture, how herders manage it largely affects the future sustainability of the livestock industry. Since Mongolia transformed its grazing practices from communal management into loosely regulated household practices in 1990, overgrazing has become a growing concern. Considering this concern, this paper examines the extent to which traditional and non-traditional herders perceive pasture conditions and practice management. We conducted the questionnaire survey among 120 herders in Murun Soum of Khentii Province and asked about rangeland degradation and their coping strategies. To determine correlations between their perceptions/practices and sociodemographic characteristics, we conducted multiple regression analyses. We found that, overall, most herders identified rangeland conditions degrading and grass yield declining with less plant diversity and more soil damage by Brandt’s vole. Herders’ mobility and herd movement frequency have decreased since 1990, placing more strains on limited pasture areas. In coping with overgrazing, about 20% of the respondents had practiced traditional rangeland management, whereas many others had overlooked pasture conditions and increased goat production as the world’s demand for cashmere rose. In response to our question about herders’ future contribution of their traditional knowledge to sustainable rangeland management, traditional herders demonstrated their willingness to help local officials manage the pasture. This paper then explores how local administrations and herders may collaborate in the future.

Bimanual coordination associated with left- and right-hand dominance: testing the limb assignment and limb dominance hypothesis

In an experiment conducted by Kennedy et al. (Exp Brain Res 233:181–195, 2016), dominant right-handed individuals were required to produce a rhythm of isometric forces in a 2:1 or 1:2 bimanual coordination pattern. In the 2:1 pattern, the left limb performed the faster rhythm, while in the 1:2 pattern, the right limb produced the faster pattern. In the 1:2 pattern, interference occurred in the limb which had to produce the slower rhythm of forces. However, in the 2:1 condition, interference occurred in both limbs. The conclusion was that interference was not only influenced by movement frequency, but also influenced by limb dominance. The present experiment was designed to replicate these findings in dynamic bimanual 1:2 and 2:1 tasks where performers had to move one wrist faster than the other, and to determine the influence of limb dominance. Dominant left-handed (N = 10; LQ = − 89.81) and dominant right-handed (N = 14; LQ = 91.25) participants were required to perform a 2:1 and a 1:2 coordination pattern using Lissajous feedback. The harmonicity value was calculated to quantify the interference in the trial-time series. The analysis demonstrated that regardless of limb dominance, harmonicity was always lower in the slower moving limb than in the faster moving limb. The present results indicated that for dominant left- and dominant right-handers the faster moving limb influenced the slower moving limb. This is in accordance with the assumption that movement frequency has a higher impact on limb control in bimanual 2:1 and 1:2 coordination tasks than handedness.

Video-based quantification of human movement frequency using pose estimation

ABSTRACTAssessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1-4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson’s correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, low-cost, accessible approach to quantitative assessment of repetitive movements in clinical populations.