Accurate muscle geometry is essential to estimate moment arms in musculoskeletal models. Given the complex interactions between shoulder structures, we hypothesized that finite element (FE) modelling is suitable to obtain physiological muscle trajectory. A FE glenohumeral joint model was developed based on medical imaging. Moment arms were computed and compared to literature and MRI-based estimation. Our FE model produces moment arms consistent with the literature and with MRI data (max 17 mm differences). The inferior and superior fibres of a same muscle can have opposite action; predictions of moment arms are sensitive to muscle insertion (up to 20 mm variation).
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Dynamic processes, such as intracellular calcium signaling, are hallmark of cellular biology. As real-time imaging modalities become widespread, a need for analytical tools to reliably characterize time-series data without prior knowledge of the nature of the recordings becomes more pressing. The goal of this study is to develop a signal-processing algorithm for MATLAB that autonomously computes the parameters characterizing prominent single transient responses (TR) and/or multi-peaks responses (MPR). The algorithm corrects for signal contamination and decomposes experimental recordings into contributions from drift, TRs, and MPRs. It subsequently provides numerical estimates for the following parameters: time of onset after stimulus application, activation time (time for signal to increase from 10 to 90% of peak), and amplitude of response. It also provides characterization of the (i) TRs by quantifying their area under the curve (AUC), response duration (time between 1/2 amplitude on ascent and descent of the transient), and decay constant of the exponential decay region of the deactivation phase of the response, and (ii) MPRs by quantifying the number of peaks, mean peak magnitude, mean periodicity, standard deviation of periodicity, oscillatory persistence (time between first and last discernable peak), and duty cycle (fraction of period during which system is active) for all the peaks in the signal, as well as coherent oscillations (i.e., deterministic spikes). We demonstrate that the signal detection performance of this algorithm is in agreement with user-mediated detection and that parameter estimates obtained manually and algorithmically are correlated. We then apply this algorithm to study how metabolic acidosis affects purinergic (P2) receptor-mediated calcium signaling in osteoclast precursor cells. Our results reveal that acidosis significantly attenuates the amplitude and AUC calcium responses at high ATP concentrations. Collectively, our data validated this algorithm as a general framework for comprehensively analyzing dynamic time-series.
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We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.
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Example code for the analysis pipeline used to create the structural template and quantitative myelin water imaging atlases for An atlas for human brain myelin content throughout the adult life span https://www.nature.com/articles/s41598-020-79540-3
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handle: 11025/38457
In recent years, the use of Virtual Reality (VR) has skyrocketed in many fields. From videogames to education, the popularity of this technology is well known also thanks to its affordability and the growing number of available content. An immersive approach and the correct use of gamification have been proven to be valuable tools for learning in different fields. There are many examples of the adoption of VR application also in the healthcare setting not only for learning purposes but also for patients’ rehabilitation. This paper is intended to list some existing works and then present our project for an immersive serious game with the aim of enhancing the training of healthcare workers for the assessment of stroke patients.
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We describe here a simple, cost-effective apparatus for continuous tethered electroencephalographic (EEG) monitoring of spontaneous recurrent seizures in mice. We used a small, low torque slip ring as an EEG commutator, mounted the slip ring onto a standard mouse cage and connected rotary wires of the slip ring directly to animal's implanted headset. Modifications were made in the cage to allow for a convenient installation of the slip ring and accommodation of animal ambient activity. We tested the apparatus for hippocampal EEG recordings in adult C57 black mice. Spontaneous recurrent seizures were induced using extended hippocampal kindling (≥95 daily stimulation). Control animals underwent similar hippocampal electrode implantations but no stimulations were given. Combined EEG and webcam monitoring were performed for 24 h daily for 5–9 consecutive days. During the monitoring periods, the animals moved and accessed water and food freely and showed no apparent restriction in ambient cage activities. Ictal-like hippocampal EEG discharges and concurrent convulsive behaviors that are characteristics of spontaneous recurrent seizures were reliably recorded in a majority of the monitoring experiments in extendedly kindled but not in control animals. However, 1–2 rotary wires were disconnected from the implanted headset in some animals after continuous recordings for ≥5 days. The key features and main limitations of our recording apparatus are discussed.
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This toolbox contains Matlab codes for time-varying multivariate autoregressive (TV-MVAR) modeling. MVAR models are usually applied to investigate couplings between various time-series in frequency domain. Herein, changes in the model parameters are tracked using the conventional Kalman Filer (KF) and a proposed modified KF. Model order selection and hyperparameter optimization is realized using Genetic Algorithms, significantly improving accuracy and run-time. Residual heteroskedasticity is tackled by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models leading to more accurate representations of the strength and directionality of the underlying couplings.
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This repository contains the code & data for the bulk analysis included the G34R/V HGG manuscript (Chen, Deshmukh, Jessa, Hadjadj, et al, Cell, 2020), for the analysis that was performed by our lab. This repository is meant to enhance the STAR Methods section by providing code for the custom analyses in the manuscript and the exact R dependencies, in order to improve reproducibility for the main results. However, it is not a fully executable workflow.
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Contraction-level invariant surface electromyography pattern recognition introduces the decrease of training time and decreases the limitation of clinical prostheses. This study intended to examine whether a signal pre-processing method named frequency division technique (FDT) for online myoelectric pattern recognition classification is robust against contraction-level variation, and whether this pre-processing method has an advantage over traditional time-domain pattern recognition techniques even in the absence of muscle contraction-level variation. Eight healthy and naïve subjects performed wrist contractions during two degrees of freedom goal-oriented tasks, divided in three groups of type I, type II, and type III. The performance of these tasks, when the two different methods were used, was quantified by completion rate, completion time, throughput, efficiency, and overshoot. The traditional and the FDT method were compared in four runs, using combinations of normal or high muscle contraction level, and the traditional method or FDT. The results indicated that FDT had an advantage over traditional methods in the tested real-time myoelectric control tasks. FDT had a much better median completion rate of tasks (95%) compared to the traditional method (77.5%) among non-perfect runs, and the variability in FDT was strikingly smaller than the traditional method (p < 0.001). Moreover, the FDT method outperformed the traditional method in case of contraction-level variation between the training and online control phases (p = 0. 005 for throughput in type I tasks with normal contraction level, p = 0.006 for throughput in type II tasks, and p = 0.001 for efficiency with normal contraction level of all task types). This study shows that FDT provides advantages in online myoelectric control as it introduces robustness over contraction-level variations.
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For versions >= 1.9.0, please see this record: https://zenodo.org/doi/10.5281/zenodo.10175845 ---------- This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions. To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version. See Appendix I for a list of the BIDS contributors who jointly created this specification.
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Accurate muscle geometry is essential to estimate moment arms in musculoskeletal models. Given the complex interactions between shoulder structures, we hypothesized that finite element (FE) modelling is suitable to obtain physiological muscle trajectory. A FE glenohumeral joint model was developed based on medical imaging. Moment arms were computed and compared to literature and MRI-based estimation. Our FE model produces moment arms consistent with the literature and with MRI data (max 17 mm differences). The inferior and superior fibres of a same muscle can have opposite action; predictions of moment arms are sensitive to muscle insertion (up to 20 mm variation).
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Dynamic processes, such as intracellular calcium signaling, are hallmark of cellular biology. As real-time imaging modalities become widespread, a need for analytical tools to reliably characterize time-series data without prior knowledge of the nature of the recordings becomes more pressing. The goal of this study is to develop a signal-processing algorithm for MATLAB that autonomously computes the parameters characterizing prominent single transient responses (TR) and/or multi-peaks responses (MPR). The algorithm corrects for signal contamination and decomposes experimental recordings into contributions from drift, TRs, and MPRs. It subsequently provides numerical estimates for the following parameters: time of onset after stimulus application, activation time (time for signal to increase from 10 to 90% of peak), and amplitude of response. It also provides characterization of the (i) TRs by quantifying their area under the curve (AUC), response duration (time between 1/2 amplitude on ascent and descent of the transient), and decay constant of the exponential decay region of the deactivation phase of the response, and (ii) MPRs by quantifying the number of peaks, mean peak magnitude, mean periodicity, standard deviation of periodicity, oscillatory persistence (time between first and last discernable peak), and duty cycle (fraction of period during which system is active) for all the peaks in the signal, as well as coherent oscillations (i.e., deterministic spikes). We demonstrate that the signal detection performance of this algorithm is in agreement with user-mediated detection and that parameter estimates obtained manually and algorithmically are correlated. We then apply this algorithm to study how metabolic acidosis affects purinergic (P2) receptor-mediated calcium signaling in osteoclast precursor cells. Our results reveal that acidosis significantly attenuates the amplitude and AUC calcium responses at high ATP concentrations. Collectively, our data validated this algorithm as a general framework for comprehensively analyzing dynamic time-series.
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We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.
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Example code for the analysis pipeline used to create the structural template and quantitative myelin water imaging atlases for An atlas for human brain myelin content throughout the adult life span https://www.nature.com/articles/s41598-020-79540-3
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handle: 11025/38457
In recent years, the use of Virtual Reality (VR) has skyrocketed in many fields. From videogames to education, the popularity of this technology is well known also thanks to its affordability and the growing number of available content. An immersive approach and the correct use of gamification have been proven to be valuable tools for learning in different fields. There are many examples of the adoption of VR application also in the healthcare setting not only for learning purposes but also for patients’ rehabilitation. This paper is intended to list some existing works and then present our project for an immersive serious game with the aim of enhancing the training of healthcare workers for the assessment of stroke patients.
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We describe here a simple, cost-effective apparatus for continuous tethered electroencephalographic (EEG) monitoring of spontaneous recurrent seizures in mice. We used a small, low torque slip ring as an EEG commutator, mounted the slip ring onto a standard mouse cage and connected rotary wires of the slip ring directly to animal's implanted headset. Modifications were made in the cage to allow for a convenient installation of the slip ring and accommodation of animal ambient activity. We tested the apparatus for hippocampal EEG recordings in adult C57 black mice. Spontaneous recurrent seizures were induced using extended hippocampal kindling (≥95 daily stimulation). Control animals underwent similar hippocampal electrode implantations but no stimulations were given. Combined EEG and webcam monitoring were performed for 24 h daily for 5–9 consecutive days. During the monitoring periods, the animals moved and accessed water and food freely and showed no apparent restriction in ambient cage activities. Ictal-like hippocampal EEG discharges and concurrent convulsive behaviors that are characteristics of spontaneous recurrent seizures were reliably recorded in a majority of the monitoring experiments in extendedly kindled but not in control animals. However, 1–2 rotary wires were disconnected from the implanted headset in some animals after continuous recordings for ≥5 days. The key features and main limitations of our recording apparatus are discussed.
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