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.
Details are in the following (open) journal publication
A specific subset of 16 time-series features from the 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 ) 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 the background to catchaMouse16
For information on the full set of over 7000 time-series features on which catchaMouse16 was derived, see the following (open) publications:
B.D. Fulcher and N.S. Jones. . Cell Systems 5, 527 (2017).
B.D. Fulcher, M.A. Little, N.S. Jones . 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. . Data Mining and Knowledge Discovery (2019).
⌨️ Installation
For C, MATLAB, and Python, the catchaMouse16 contains source code for building native binaries that can be called from these languages. You will need to download the source code, install (unix only), then follow the instructions below to build the binaries for your language.
For Julia users, these binaries have been for all platforms. You can use them by installing the 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.
Access the efficient python implementation of feature-set by running the following code:
You can then test the features with
ℹ Feature Descriptions
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 below follows the same cypher as in with the hctsa name and an interpretable name referred to throughout the paper with corresponding descriptions.
Interpretable name
Feature name
Description