ELECTROPHYSIOLOGICAL FEATURE EXTRACTION TOOLBOX (ElecFeX)

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that
  1. has an intuitive graphical user interface,
  2. provides customizable measurements for a wide range of electrophysiological features,
  3. processes large-size datasets effortlessly via batch analysis, and
  4. yields formatted output for further analysis.
We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

The paper that describes ElexFeX in detail is available here:
X. Ma, L. Miraucourt, H. Qiu, M. Xu, E.P. Cook, A. Krishnaswamy, R. Sharif-Naeini and A. Khadra. ElecFeX is a User-Friendly Toolbox for Efficient Feature Extraction from Single-Cell Electrophysiological Recordings. Cell Reports Methods, 4, 100791, 2024.

Detailed description of the software is available here and here.

PARAMETRIC CHARACTERIZATION OF CALCIUM SIGNALS

We have developed a MATLAB-based algorithm in the form of a software that allows for the meta analysis of calcium signals, i.e., the automated characterization of single and multi-peaked transient calcium responses from time series data. Signals are decomposed into drift and response components and the entirety of the signal is reconstructed using a mathematical model of a transient response and a drift. The software measures the time of onset, activation time (10% to 90% of total amplitude), amplitude of response, full width at half maximum and decay time constant of a transient non-oscillatory response. For oscillatory multipeaked responses, the algorithm attempts to separate coherent oscillations from those that are completely stochastic. For the full set of oscillations as well as the coherent subset, the algorithm then reports the number of oscillations, period of oscillation (averaged), standard deviation of the period, magnitude (averaged), full width half maximum (averaged), duty cycle (averaged), and the length of oscillatory persistence. Time series data should be stored in an excel sheet, and the whole document can be processed with the MATLAB command: characterizeDocument('example.xlsx').

The paper that describes the mathematical theory associated with the algorithm is available here:
L. Mackay, N. Mikolajewicz, S.V. Komarova and A. Khadra. Systematic Characterization of Dynamic Parameters of Intracellular Calcium Signals. Frontier in Physiology, 7, 525, 2016.

Detailed description of the software is available here.