Functional-Coefficient Models for Multivariate Time Series in Designed Experiments: with Applications to Brain Signals

07/30/2022
by   Paolo Victor Redondo, et al.
0

To study the neurophysiological basis of attention deficit hyperactivity disorder (ADHD), clinicians use electroencephalography (EEG) which record neuronal electrical activity on the cortex. The most commonly-used metric in ADHD is the theta-to-beta spectral power ratio (TBR) that is based on a single-channel analysis. However, initial findings for this measure have not been replicated in other studies. Thus, instead of focusing on single-channel spectral power, a novel model for investigating interactions (dependence) between channels in the entire network is proposed. Although dependence measures such as coherence and partial directed coherence (PDC) are well explored in studying brain connectivity, these measures only capture linear dependence. Moreover, in designed clinical experiments, these dependence measures are observed to vary across subjects even within a homogeneous group. To address these limitations, we propose the mixed-effects functional-coefficient autoregressive (MX-FAR) model which captures between-subject variation by incorporating subject-specific random effects. The advantages of the MX-FAR model are the following: (1.) it captures potential non-linear dependence between channels; (2.) it is nonparametric and hence flexible and robust to model mis-specification; (3.) it can capture differences between groups when they exist; (4.) it accounts for variation across subjects; (5.) the framework easily incorporates well-known inference methods from mixed-effects models; (6.) it can be generalized to accommodate various covariates and factors. Finally, we apply the proposed MX-FAR model to analyze multichannel EEG signals and report novel findings on altered brain functional networks in ADHD.

READ FULL TEXT
research
11/19/2017

Coherence-based Time Series Clustering for Brain Connectivity Visualization

We develop the hierarchical cluster coherence (HCC) method for brain sig...
research
09/16/2022

Mixed Effects Spectral Vector Autoregressive Model: With Application to Brain Connectivity

The primary goal of this paper is to develop a method that quantifies ho...
research
09/24/2018

Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

This paper intends to develop tools for characterizing non-linear spectr...
research
05/18/2023

Multi-scale wavelet coherence with its applications

The goal in this paper is to develop a novel statistical approach to cha...
research
03/11/2023

Measuring Information Transfer Between Nodes in a Brain Network through Spectral Transfer Entropy

Brain connectivity reflects how different regions of the brain interact ...
research
06/13/2023

Topological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data

Topological data analysis (TDA) approaches are becoming increasingly pop...
research
05/15/2023

Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes: With Application to Brain Signals

One of the goals of neuroscience is to study interactions between differ...

Please sign up or login with your details

Forgot password? Click here to reset