Deep learning of material transport in complex neurite networks

Abstract Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical me...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Angran Li, Amir Barati Farimani, Yongjie Jessica Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6139bc52b9b4468d86ab3e1d426b10bc
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6139bc52b9b4468d86ab3e1d426b10bc
record_format	dspace
spelling	oai:doaj.org-article:6139bc52b9b4468d86ab3e1d426b10bc2021-12-02T15:00:39ZDeep learning of material transport in complex neurite networks10.1038/s41598-021-90724-32045-2322https://doaj.org/article/6139bc52b9b4468d86ab3e1d426b10bc2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90724-3https://doaj.org/toc/2045-2322Abstract Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and $$120 \sim 330$$ 120 ∼ 330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.Angran LiAmir Barati FarimaniYongjie Jessica ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Angran Li Amir Barati Farimani Yongjie Jessica Zhang Deep learning of material transport in complex neurite networks
description	Abstract Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and $$120 \sim 330$$ 120 ∼ 330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.
format	article
author	Angran Li Amir Barati Farimani Yongjie Jessica Zhang
author_facet	Angran Li Amir Barati Farimani Yongjie Jessica Zhang
author_sort	Angran Li
title	Deep learning of material transport in complex neurite networks
title_short	Deep learning of material transport in complex neurite networks
title_full	Deep learning of material transport in complex neurite networks
title_fullStr	Deep learning of material transport in complex neurite networks
title_full_unstemmed	Deep learning of material transport in complex neurite networks
title_sort	deep learning of material transport in complex neurite networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6139bc52b9b4468d86ab3e1d426b10bc
work_keys_str_mv	AT angranli deeplearningofmaterialtransportincomplexneuritenetworks AT amirbaratifarimani deeplearningofmaterialtransportincomplexneuritenetworks AT yongjiejessicazhang deeplearningofmaterialtransportincomplexneuritenetworks
_version_	1718389140110704640

Deep learning of material transport in complex neurite networks

Ejemplares similares