Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; h...

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Autores principales:	Naoki Okamoto, Hiroyuki Akama
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	deep learning resting functional connectivity MRI harmonization leave-one-site-out cross-validation ABIDE Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/e42e2713bca34346812a69bfb7b2d8bf
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spelling	oai:doaj.org-article:e42e2713bca34346812a69bfb7b2d8bf2021-12-01T21:27:09ZExtended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling1662-519610.3389/fninf.2021.709179https://doaj.org/article/e42e2713bca34346812a69bfb7b2d8bf2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.709179/fullhttps://doaj.org/toc/1662-5196Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by transfer learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility.Naoki OkamotoHiroyuki AkamaHiroyuki AkamaFrontiers Media S.A.articledeep learningresting functional connectivity MRIharmonizationleave-one-site-out cross-validationABIDENeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning resting functional connectivity MRI harmonization leave-one-site-out cross-validation ABIDE Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	deep learning resting functional connectivity MRI harmonization leave-one-site-out cross-validation ABIDE Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Naoki Okamoto Hiroyuki Akama Hiroyuki Akama Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling
description	Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by transfer learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility.
format	article
author	Naoki Okamoto Hiroyuki Akama Hiroyuki Akama
author_facet	Naoki Okamoto Hiroyuki Akama Hiroyuki Akama
author_sort	Naoki Okamoto
title	Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling
title_short	Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling
title_full	Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling
title_fullStr	Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling
title_full_unstemmed	Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling
title_sort	extended invariant information clustering is effective for leave-one-site-out cross-validation in resting state functional connectivity modeling
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e42e2713bca34346812a69bfb7b2d8bf
work_keys_str_mv	AT naokiokamoto extendedinvariantinformationclusteringiseffectiveforleaveonesiteoutcrossvalidationinrestingstatefunctionalconnectivitymodeling AT hiroyukiakama extendedinvariantinformationclusteringiseffectiveforleaveonesiteoutcrossvalidationinrestingstatefunctionalconnectivitymodeling AT hiroyukiakama extendedinvariantinformationclusteringiseffectiveforleaveonesiteoutcrossvalidationinrestingstatefunctionalconnectivitymodeling
_version_	1718404604609167360

Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

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