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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La croissance dendritique est calculée en utilisant un modèle champ de phase avec adaptation automatique anisotrope et non structurées d’un maillage éléments finis. Les inconnues sont la fonction champ de phase, une température adimensionnelle et une composition adimensionnelle, tel que proposé par [KAR1998] et [RAM2004]. Une interpolation linéaire d’éléments finis est utilisée pour les trois variables, après des techniques de stabilisation de discrétisation qui assurent la convergence vers une solution correcte non-oscillante. Afin d'effectuer des calculs quantitatifs de la croissance dendritique sur un grand domaine, deux ingrédients numériques supplémentaires sont nécessaires: un maillage adaptatif anisotrope et non structuré [COU2011], [COU2014] et un calcul parallèle [DIG2001], mis à disposition de la plateforme numérique utilisée (CimLib) basée sur des développements C++. L'adaptation du maillage se trouve à réduire considérablement le nombre de degrés de liberté. Les résultats des simulations en champ de phase pour les dendrites pour une solidification d'un matériau pur et d’un alliage binaire en deux et trois dimensions sont présentés et comparés à des travaux de référence. Une discussion sur les détails de l'algorithme et le temps CPU sont présentés et une comparaison avec un modèle macroscopique sont faite. Dendritic growth is computed using a phase-field model with automatic adaptation of an anisotropic and unstructured finite element mesh. Unknowns are the phase-field function, a dimensionless temperature and a dimensionless composition, as proposed by [KAR1998] and [RAM2004]. Linear finite element interpolation is used for all variables, after discretization stabilization techniques that ensure convergence towards a correct non-oscillating solution. In order to perform quantitative computations of dendritic growth on a large domain, two additional numerical ingredients are necessary: automatic anisotropic unstructured adaptive meshing [COU2011], [COU2014] and parallel implementations [DIG2001], both made available with the numerical platform used (CimLib) based on C++ developments. Mesh adaptation is found to greatly reduce the number of degrees of freedom. Results of phase-field simulations for dendritic solidification of a pure material and a binary alloy in two and three dimensions are shown and compared with reference work. Discussion on algorithm details and the CPU time are outlined and a comparison with a macroscopic model are made.
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An implementation of several well-known dynamic Functional Connectivity assessment methods. If you use this software, please cite it as below.
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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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{"references": ["Harel et al., (2023). Open design and validation of a reproducible videogame controller for MRI and MEG."]} Full documentation and files required to build the CNeuromod controller.
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Repository containing the python code used to analyse the validation data and produce the figures of the CNeuromod controller paper.
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Initial release for Wainio-Theberge et al. (2021): Dynamic relationships between spontaneous and evoked electrophysiological activity
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Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.
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Diffusion tensor imaging can be studied as a deconvolution density estimation problem on the space of positive definite symmetric matrices. We develop a nonparametric estimator for the common density function of a random sample of positive definite matrices. Our estimator is based on the Helgason-Fourier transform and its inversion, the natural tools for analysis of compositions of random positive definite matrices. Under smoothness conditions on the density of the intrinsic error in the random sample, we derive bounds on the rates of convergence of our nonparametric estimator to the true density.
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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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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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La croissance dendritique est calculée en utilisant un modèle champ de phase avec adaptation automatique anisotrope et non structurées d’un maillage éléments finis. Les inconnues sont la fonction champ de phase, une température adimensionnelle et une composition adimensionnelle, tel que proposé par [KAR1998] et [RAM2004]. Une interpolation linéaire d’éléments finis est utilisée pour les trois variables, après des techniques de stabilisation de discrétisation qui assurent la convergence vers une solution correcte non-oscillante. Afin d'effectuer des calculs quantitatifs de la croissance dendritique sur un grand domaine, deux ingrédients numériques supplémentaires sont nécessaires: un maillage adaptatif anisotrope et non structuré [COU2011], [COU2014] et un calcul parallèle [DIG2001], mis à disposition de la plateforme numérique utilisée (CimLib) basée sur des développements C++. L'adaptation du maillage se trouve à réduire considérablement le nombre de degrés de liberté. Les résultats des simulations en champ de phase pour les dendrites pour une solidification d'un matériau pur et d’un alliage binaire en deux et trois dimensions sont présentés et comparés à des travaux de référence. Une discussion sur les détails de l'algorithme et le temps CPU sont présentés et une comparaison avec un modèle macroscopique sont faite. Dendritic growth is computed using a phase-field model with automatic adaptation of an anisotropic and unstructured finite element mesh. Unknowns are the phase-field function, a dimensionless temperature and a dimensionless composition, as proposed by [KAR1998] and [RAM2004]. Linear finite element interpolation is used for all variables, after discretization stabilization techniques that ensure convergence towards a correct non-oscillating solution. In order to perform quantitative computations of dendritic growth on a large domain, two additional numerical ingredients are necessary: automatic anisotropic unstructured adaptive meshing [COU2011], [COU2014] and parallel implementations [DIG2001], both made available with the numerical platform used (CimLib) based on C++ developments. Mesh adaptation is found to greatly reduce the number of degrees of freedom. Results of phase-field simulations for dendritic solidification of a pure material and a binary alloy in two and three dimensions are shown and compared with reference work. Discussion on algorithm details and the CPU time are outlined and a comparison with a macroscopic model are made.
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An implementation of several well-known dynamic Functional Connectivity assessment methods. If you use this software, please cite it as below.
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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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{"references": ["Harel et al., (2023). Open design and validation of a reproducible videogame controller for MRI and MEG."]} Full documentation and files required to build the CNeuromod controller.
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Repository containing the python code used to analyse the validation data and produce the figures of the CNeuromod controller paper.