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Title
Japanese: 
English:Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling 
Author
Japanese: 岡本直己, 赤間啓之.  
English: Naoki Okamoto, Hiroyuki Akama.  
Language English 
Journal/Book name
Japanese: 
English:Frontiers in Neuroinformatics 
Volume, Number, Page Volume 15       
Published date Dec. 1, 2021 
Publisher
Japanese: 
English:Frontiers Media 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
File
Official URL https://www.frontiersin.org/articles/10.3389/fninf.2021.709179/full
 
DOI https://doi.org/10.3389/fninf.2021.709179
Abstract 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.

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