Ben D. FulcherT7: Massive open datasets of neural dynamics, from microscale neuronal circuits to macroscale population-level recordings, are becoming increasingly available to the computational neuroscience community. 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. Across this interdisciplinary literature of thousands of time-series analysis methods, each method gives unique information about the measured dynamics. 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.
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. I will demonstrate how
hctsa can be used to extract useful information from various neural time-series datasets. 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.
Tutorial Website
Software tools[1] If you want to play along at home, you can read the README and install the
hctsa software package (Matlab):
https://github.com/benfulcher/hctsa[2]
hctsa documentation:
https://hctsa-users.gitbook.io/hctsa-manual/
References and background reading[1] B.D. Fulcher, N. S. Jones.
hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5(5): 527 (2017). https://doi.org/10.1016/ j.cels.2017.10.001
[2] 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, 20130048 (2013). https://doi.org/10.1098/rsif.2013.0048
---Attendence Instructions (ZOOM)---
Topic: CNS 2020 Tutorial: Characterizing neural dynamics using highly comparative time-series analysis
Time: Jul 18, 2020 08:00 PM Canberra, Melbourne, Sydney
Join URL:
https://uni-sydney.zoom.us/j/92853577660?pwd=eWJydWczR3pRUkhkQ05QS3N0bjNIZz09
Password: 743296
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