Deep Variational Autoencoder for Mapping Functional Brain Networks

In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods to map functional brain networks (FBNs). Recently, it has been proven that deep learning (DL) can be applied on functional magnetic resonance image (fMRI) data with superb representation p...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on cognitive and developmental systems Ročník 13; číslo 4; s. 841 - 852
Hlavní autoři: Qiang, Ning, Dong, Qinglin, Ge, Fangfei, Liang, Hongtao, Ge, Bao, Zhang, Shu, Sun, Yifei, Gao, Jie, Liu, Tianming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Attention deficit hyperactivity disorder
Attention deficit hyperactivity disorder (ADHD)
Biological system modeling
Brain
Brain modeling
Classification
Data models
Datasets
Deep learning
deep learning (DL)
Feature extraction
Functional magnetic resonance imaging
generative learning
Machine learning
Magnetic resonance imaging
Mapping
Medical imaging
Representations
resting-state functional magnetic resonance image (rfMRI)
Unsupervised learning
variational autoencoder (VAE)
ISSN:2379-8920, 2379-8939
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods to map functional brain networks (FBNs). Recently, it has been proven that deep learning (DL) can be applied on functional magnetic resonance image (fMRI) data with superb representation power over the traditional machine learning methods. However, due to the lack of labeled data and the high dimension of fMRI volume images, DL suffers from overfitting in both supervised and unsupervised training processes. In this work, we proposed a novel generative model: deep variational autoencoder (DVAE) to tackle the challenge of insufficient data and incomplete supervision. The experimental results showed that the representations learned by DVAE are interpretable and meaningful compared to those learned from well-known sparse dictionary learning (SDL). Besides, the organization of some FBN patterns derived from different layers in DVAE was observed in a hierarchical fashion. Furthermore, we showed that DVAE has better performance on small dataset over autoencoder (AE). By using attention deficit hyperactivity disorder (ADHD)-200 dataset as our test bed, we constructed a DVAE-based modeling and classification pipeline in which all subjects' functional connectivities estimated by FBNs were taken as input features to train a classifier. Finally, the results achieved by our pipeline reached state-of-the-art classification accuracies on three ADHD-200 sites compared with other fMRI-based methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2379-8920
2379-8939
DOI:10.1109/TCDS.2020.3025137