If you want to play along at home, you can read the README and install the hctsa software package (Matlab): We will work through a range of applications using fMRI (mouse and human) and EEG (human) time-series datasets, including how to: (i) determine the relationship between structural connectivity and fMRI dynamics in mouse and human (ii) understand the effects of targeted brain stimulation using DREADDs using mouse fMRI and (iii) classify seizure dynamics and extract sleep-stage information from EEG. I will demonstrate how hctsa can be used to extract useful information from various neural time-series datasets. In this tutorial, I will introduce highly comparative time-series analysis, implemented as the software package hctsa, which partially automates the selection of useful time-series analysis methods from an interdisciplinary library of over 7000 time-series features.
However, the choice of analysis methods in any given study is typically subjective, leaving open the possibility that alternative methods might yield better understanding or performance for a given task. Across this interdisciplinary literature of thousands of time-series analysis methods, each method gives unique information about the measured dynamics. There are myriad ways to quantify different types of structure in the univariate dynamics of any individual component of a neural system, including methods from statistical time-series modeling, the physical nonlinear time-series analysis literature, and methods derived from information theory. T7: Massive open datasets of neural dynamics, from microscale neuronal circuits to macroscale population-level recordings, are becoming increasingly available to the computational neuroscience community.
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