Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox

An important goal in neuroscience is to elucidate the causal relationships between the brain’s different regions. This can help reveal the brain’s functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-ar...

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Autores principales:	Takuto Okuno, Alexander Woodward
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	vector auto-regressive deep neural network directed functional connectivity fMRI Granger causality Alzheimer’s disease Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/621b9159783047f0b3eac840900bd4e4
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id	oai:doaj.org-article:621b9159783047f0b3eac840900bd4e4
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spelling	oai:doaj.org-article:621b9159783047f0b3eac840900bd4e42021-11-30T14:40:01ZVector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox1662-453X10.3389/fnins.2021.764796https://doaj.org/article/621b9159783047f0b3eac840900bd4e42021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.764796/fullhttps://doaj.org/toc/1662-453XAn important goal in neuroscience is to elucidate the causal relationships between the brain’s different regions. This can help reveal the brain’s functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies. Therefore, we propose a new framework based on a vector auto-regressive deep neural network (VARDNN) architecture. Our approach consists of a set of nodes, each with a deep neural network structure. These nodes can be mapped to any spatial sub-division based on the data to be analyzed, such as anatomical brain regions from which representative neural signals can be obtained. VARDNN learns to reproduce experimental time series data using modern deep learning training techniques. Based on this, we developed two novel directed functional connectivity (dFC) measures, namely VARDNN-DI and VARDNN-GC. We evaluated our measures against a number of existing functional connectome estimation measures, such as partial correlation and multivariate Granger causality combined with large dimensionality counter-measure techniques. Our measures outperformed them across various types of ground truth data, especially as the number of nodes increased. We applied VARDNN to fMRI data to compare the dFC between 41 healthy control vs. 32 Alzheimer’s disease subjects. Our VARDNN-DI measure detected lesioned regions consistent with previous studies and separated the two groups well in a subject-wise evaluation framework. Summarily, the VARDNN framework has powerful capabilities for whole brain dFC estimation. We have implemented VARDNN as an open-source toolbox that can be freely downloaded for researchers who wish to carry out functional connectome analysis on their own data.Takuto OkunoAlexander WoodwardFrontiers Media S.A.articlevector auto-regressive deep neural networkdirected functional connectivityfMRIGranger causalityAlzheimer’s diseaseNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vector auto-regressive deep neural network directed functional connectivity fMRI Granger causality Alzheimer’s disease Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	vector auto-regressive deep neural network directed functional connectivity fMRI Granger causality Alzheimer’s disease Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Takuto Okuno Alexander Woodward Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox
description	An important goal in neuroscience is to elucidate the causal relationships between the brain’s different regions. This can help reveal the brain’s functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies. Therefore, we propose a new framework based on a vector auto-regressive deep neural network (VARDNN) architecture. Our approach consists of a set of nodes, each with a deep neural network structure. These nodes can be mapped to any spatial sub-division based on the data to be analyzed, such as anatomical brain regions from which representative neural signals can be obtained. VARDNN learns to reproduce experimental time series data using modern deep learning training techniques. Based on this, we developed two novel directed functional connectivity (dFC) measures, namely VARDNN-DI and VARDNN-GC. We evaluated our measures against a number of existing functional connectome estimation measures, such as partial correlation and multivariate Granger causality combined with large dimensionality counter-measure techniques. Our measures outperformed them across various types of ground truth data, especially as the number of nodes increased. We applied VARDNN to fMRI data to compare the dFC between 41 healthy control vs. 32 Alzheimer’s disease subjects. Our VARDNN-DI measure detected lesioned regions consistent with previous studies and separated the two groups well in a subject-wise evaluation framework. Summarily, the VARDNN framework has powerful capabilities for whole brain dFC estimation. We have implemented VARDNN as an open-source toolbox that can be freely downloaded for researchers who wish to carry out functional connectome analysis on their own data.
format	article
author	Takuto Okuno Alexander Woodward
author_facet	Takuto Okuno Alexander Woodward
author_sort	Takuto Okuno
title	Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox
title_short	Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox
title_full	Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox
title_fullStr	Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox
title_full_unstemmed	Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox
title_sort	vector auto-regressive deep neural network: a data-driven deep learning-based directed functional connectivity estimation toolbox
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/621b9159783047f0b3eac840900bd4e4
work_keys_str_mv	AT takutookuno vectorautoregressivedeepneuralnetworkadatadrivendeeplearningbaseddirectedfunctionalconnectivityestimationtoolbox AT alexanderwoodward vectorautoregressivedeepneuralnetworkadatadrivendeeplearningbaseddirectedfunctionalconnectivityestimationtoolbox
_version_	1718406507347836928

Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox

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