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Integrative learning for population of dynamic networks with covariates
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic approaches for estimating a population of dynamic ne...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851385/ https://www.ncbi.nlm.nih.gov/pubmed/34022384 http://dx.doi.org/10.1016/j.neuroimage.2021.118181 |
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author | Kundu, Suprateek Ming, Jin Nocera, Joe McGregor, Keith M. |
author_facet | Kundu, Suprateek Ming, Jin Nocera, Joe McGregor, Keith M. |
author_sort | Kundu, Suprateek |
collection | PubMed |
description | Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic approaches for estimating a population of dynamic networks that are able to borrow information across multiple heterogeneous samples in an unsupervised manner and guided by covariate information. Specifically, we develop a Bayesian product mixture model that imposes independent mixture priors at each time scan and uses covariates to model the mixture weights, which results in time-varying clusters of samples designed to pool information. The computation is carried out using an effcient Expectation-Maximization algorithm. Extensive simulation studies illustrate sharp gains in recovering the true dynamic network over existing dynamic connectivity methods. An analysis of fMRI block task data with behavioral interventions reveal subgroups of individuals having similar dynamic connectivity, and identifies intervention-related dynamic network changes that are concentrated in biologically interpretable brain regions. In contrast, existing dynamic connectivity approaches are able to detect minimal or no changes in connectivity over time, which seems biologically unrealistic and highlights the challenges resulting from the inability to systematically borrow information across samples. |
format | Online Article Text |
id | pubmed-8851385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-88513852022-02-17 Integrative learning for population of dynamic networks with covariates Kundu, Suprateek Ming, Jin Nocera, Joe McGregor, Keith M. Neuroimage Article Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic approaches for estimating a population of dynamic networks that are able to borrow information across multiple heterogeneous samples in an unsupervised manner and guided by covariate information. Specifically, we develop a Bayesian product mixture model that imposes independent mixture priors at each time scan and uses covariates to model the mixture weights, which results in time-varying clusters of samples designed to pool information. The computation is carried out using an effcient Expectation-Maximization algorithm. Extensive simulation studies illustrate sharp gains in recovering the true dynamic network over existing dynamic connectivity methods. An analysis of fMRI block task data with behavioral interventions reveal subgroups of individuals having similar dynamic connectivity, and identifies intervention-related dynamic network changes that are concentrated in biologically interpretable brain regions. In contrast, existing dynamic connectivity approaches are able to detect minimal or no changes in connectivity over time, which seems biologically unrealistic and highlights the challenges resulting from the inability to systematically borrow information across samples. 2021-08-01 2021-05-20 /pmc/articles/PMC8851385/ /pubmed/34022384 http://dx.doi.org/10.1016/j.neuroimage.2021.118181 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ) |
spellingShingle | Article Kundu, Suprateek Ming, Jin Nocera, Joe McGregor, Keith M. Integrative learning for population of dynamic networks with covariates |
title | Integrative learning for population of dynamic networks with covariates |
title_full | Integrative learning for population of dynamic networks with covariates |
title_fullStr | Integrative learning for population of dynamic networks with covariates |
title_full_unstemmed | Integrative learning for population of dynamic networks with covariates |
title_short | Integrative learning for population of dynamic networks with covariates |
title_sort | integrative learning for population of dynamic networks with covariates |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851385/ https://www.ncbi.nlm.nih.gov/pubmed/34022384 http://dx.doi.org/10.1016/j.neuroimage.2021.118181 |
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