🐭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.

C-Compiled code

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>.

./run_feat <infile> <outfile>

If an <outfile is not provided then the output is sent to the stdout.

Python

Access the efficient python implementation of feature-set by running the following code:

Terminal - Setup

python3 setup_P3.py build
python3 setup_P3.py install

You can then test the features with

python3 test_features.py

The module is also available for python2:

Terminal - Setup

python2 setup.py build
python2 setup.py install

You can then test the features with

python2 test_features.py

The features are then accessible through a module which can be imported. Namely import catchaMouse16provides access to the features.

Example usage

import catchaMouse16
import numpy as np
ts_data = np.rand(100,1)
print(catchaMouse16.SC_FluctAnal_2_dfa_50_2_logi_r2_se2(ts_data))

MATLAB

Open MATLAB, navigate to catchaMouse16/MATLAB/, and run mexAll. This brings the catchaMouse16 features into the namespace.

Example usage

ts_data = randn(100,1) % column vector
catchaMouse16_all(ts_data)

Individual features can be accessed as catchaMouse16_{feature}.

Julia

Installation:

using Pkg
Pkg.add("CatchaMouse16")

Usage:

using CatchaMouse16
X = randn(1000, 10) # an Array{Float64, 2} with 10 time series
f = catchaMouse16[:AC_nl_035](X) # a 1×10 Matrix{Float64} (for a single feature)
F = catchaMouse16(X) # a 16×10 FeatureMatrix{Float64} (for 16 features)

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.

Interpretable name	Feature name	Description
nonlin_autocorr_035	AC_nl_035	$\langle x_t^2 \,x_{t-3}\, x_{t-6} \rangle_t$
nonlin_autocorr_036	AC_nl_036	$\langle x_t^2\, x_{t-3}\, x_{t-5}\rangle_t$
nonlin_autocorr_112	AC_nl_112	$\langle x_t\, x_{t-1}^2\, x_{t-2} \rangle_t$
ami3_10bin	CO_HistogramAMI_even_10bin_ami3	Automutual information at lag 3 using a 10-bin histogram estimation method
ami3_2bin	CO_HistogramAMI_even_2bin_ami3	Automutual information at lag 3 using a 2-bin histogram estimation method
increment_ami8	IN_AutoMutualInfoStats_diff_20_gaussian_ami8	Mutual information at time lag 8 using Gaussian esti- mator
dfa_longscale_fit	SC_FluctAnal_2_dfa_50_2_logi_r2_se2	Timescale–fluctuation curve using DFA
noise_titration	CO_AddNoise_1_even_10_ami_at_10	Automutual information at lag 1 after adding white noise at a SNR of 1
prediction_scale	FC_LoopLocalSimple_mean_stderr_chn	Change in prediction error from a mean forecaster using prior windows of data
floating_circle	CO_TranslateShape_circle_35_pts_std	Variability of time-series points inside a circle translated across the time domain
walker_crossings	PH_Walker_momentum_5_w_momentumzcross	Statistics of a simulated mechanical particle driven by the time series
walker_diff	PH_Walker_biasprop_05_01_sw_meanabsdiff	Statistics of a simulated mechanical particle driven by the time series
stationarity_min	SY_DriftingMean50_min	Minimum mean across 50 segments divided by mean variance in segments
stationarity_floating_circle	CO_TranslateShape_circle_35_pts_statav4_m	StatAv of the statistics of local time-series shapes
outlier_corr	DN_RemovePoints_absclose_05_ac2rat	Change in lag-2 autocorrelation from removing 50% of the time-series values closest to the mean
outlier_asymmetry	ST_LocalExtrema_n100_diffmaxabsmin	Asymmetry in extreme local events