Factorized Binary Search: change point detection in the network structure of multivariate high-dimensional time series

03/10/2021
by   Martin Ondrus, et al.
0

Functional magnetic resonance imaging (fMRI) time series data presents a unique opportunity to understand temporal brain connectivity, and models that uncover the complex dynamic workings of this organ are of keen interest in neuroscience. Change point models can capture and reflect the dynamic nature of brain connectivity, however methods that translate well into a high-dimensional context (where p>>n) are scarce. To this end, we introduce factorized binary search (FaBiSearch), a novel change point detection method in the network structure of multivariate high-dimensional time series. FaBiSearch uses non-negative matrix factorization, an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. In addition, we propose a new method for network estimation for data between change points. We show that FaBiSearch outperforms another state-of-the-art method on simulated data sets and we apply FaBiSearch to a resting-state and to a task-based fMRI data set. In particular, for the task-based data set, we explore network dynamics during the reading of Chapter 9 in Harry Potter and the Sorcerer's Stone and find that change points across subjects coincide with key plot twists. Further, we find that the density of networks was positively related to the frequency of speech between characters in the story. Finally, we make all the methods discussed available in the R package fabisearch on CRAN.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 9

page 10

page 16

03/01/2019

Detecting changes in the covariance structure of functional time series with application to fMRI data

Functional magnetic resonance imaging (fMRI) data provides information c...
04/26/2019

Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects

This paper studies change-points in human brain functional connectivity ...
03/13/2020

An Evaluation of Change Point Detection Algorithms

Change point detection is an important part of time series analysis, as ...
05/23/2021

Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package

Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics o...
10/20/2017

Nonparametrically estimating dynamic bivariate correlation using visibility graph algorithm

Dynamic conditional correlation (DCC) is a method that estimates the cor...
11/14/2019

Estimation of dynamic networks for high-dimensional nonstationary time series

This paper is concerned with the estimation of time-varying networks for...
04/07/2020

Latent Network Structure Learning from High Dimensional Multivariate Point Processes

Learning the latent network structure from large scale multivariate poin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.