2025/07/15 by Imran Alam, Brendan Harris, Patrick Cahill +4 · 2 voices
Neuroscience · Computer Science · #Functional Brain Connectivity Studies #Neural dynamics and brain function #Time Series Analysis and Forecasting
paper · pdf · doi:10.52294/001c.140433
The interdisciplinary time-series analysis literature encompasses thousands of statistical features for quantifying interpretable properties of dynamical data. For any given application, however, it is likely that only a small subset of informative time-series features is required to capture the dynamical quantities of interest. While comprehensive libraries of time-series features have been developed, it is therefore useful to construct reduced and computationally efficient subsets for specific applications. In this work, we demonstrate a systematic process to deduce such a reduced set, focusing on the problem of distinguishing changes to functional magnetic resonance imaging (fMRI) time series induced by various experimental manipulations to excitatory and inhibitory neural activity in mouse cortical circuits. We reduce a comprehensive library of over 7000 candidate time-series features down to a subset of 16 features, which we call <I>catchaMouse16</I>, that aims to both: (i) accurately characterize biologically relevant properties of fMRI time series; and (ii) minimize inter-feature redundancy. The <I>catchaMouse16</I> feature set accurately classifies experimental perturbations of neuronal activity from fMRI recordings and generalizes well to new mouse and human resting-state fMRI datasets, where it tracks spatial variations in excitatory and inhibitory cortical cell densities—often with greater statistical power than the full <I>hctsa</I> feature set. We provide an efficient, open-source implementation of the <I>catchaMouse16</I> feature set in C (achieving an approximately 60 times speed-up relative to the native Matlab code of the same features), with wrappers for Python and Matlab. This work demonstrates a procedure to reduce a large candidate time-series feature set down to the key statistical properties inherent to mouse fMRI dynamics, which can in turn be used to efficiently quantify and interpret informative dynamical patterns in neural time series.