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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.

An implementation of several well-known dynamic Functional Connectivity assessment methods. If you use this software, please cite it as below.

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.

{"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.

Repository containing the python code used to analyse the validation data and produce the figures of the CNeuromod controller paper.

Initial release for Wainio-Theberge et al. (2021): Dynamic relationships between spontaneous and evoked electrophysiological activity

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.

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

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.