🐭catchaMouse16
CAnonical Time-series CHaracteristics for Mouse fMRI
The catchaMouse16 feature set provides a useful set of features to summarize the dynamics of fMRI time-series data, with implementations for Python, MATLAB, and C.
Overview
A specific subset of 16 features from the hctsa time-series feature library designed to distinguish changes in functional Magnetic Resonance Imaging (fMRI) time series taken from mice undergoing experimental manipulations of excitatory and inhibitory neural activity in their cortical circuits.
It is a collection of features that are generated from a general pipeline (which can be accessed here) applied to mouse fMRI time-series data taken from mice. This represents a high-performing but minimally redundant data-driven subset of the full library of hctsa features, that best discriminate biologically relevant manipulations (using the DREADD technique) from non-invasive fMRI time series.
Reading more about catchaMouse16
Want to use the pipeline or the feature set? If you use catchaMouse16 in a scientific publication, please read and cite this open-access article:
Alam et al. "Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI", bioRxiv (2024).
For information on the full set of over 7000 features, see the following (open) publications:
B.D. Fulcher and N.S. Jones. hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5, 527 (2017).
B.D. Fulcher, M.A. Little, N.S. Jones Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface 10, 83 (2013).
The pipeline to reproduce to create the catchaMouse16 feature set is an adaptation of the general pipeline from C.H. Lubba, S.S. Sethi, P. Knaute, S.R. Schultz, B.D. Fulcher, N.S. Jones. catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery (2019).
Installation
For C, MATLAB, and Python, the catchaMouse16 Github repository contains source code for building native binaries that can be called from these languages. You will need to download the source code, install gsl (unix only), then follow the instructions below to build the binaries for your language.
For Julia users, these binaries have been pre-built for all platforms. You can use them by installing the CatchaMouse16.jl package.
Compile by executing the makefile inside the catchaMouse16/C
by running make
.
Compute all the time-series features for some time-series data contained in <infile>.
If an <outfile
is not provided then the output is sent to the stdout.
Feature Description
Note: All catchaMouse16 features are statistical properties of the z-scored time series - they aim to focus on the properties of the time-ordering of the data and are insensitive to the raw values in the time series.
The table belows follows the same cypher as in the pre-print with the hctsa name and an interpretable name referred to throughout the paper with corresponding descriptions.
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