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Virtual Adversarial Training-Based Deep Feature Aggregation Network From Dynamic Effective Connectivity for MCI

Yang Li, Jingyu Liu, Yiqiao Jiang

IEEE Transactions on Medical Imaging

|September 7, 2021

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This study introduces a new framework using dynamic effective connectivity (dEC) and advanced deep learning to improve brain disease identification. The method accurately classifies mild cognitive impairment stages, outperforming existing techniques.

Area of Science:

Neuroscience
Machine Learning
Medical Imaging

Background:

Resting-state fMRI reveals dynamic functional connectivity (dFC) for brain disease identification.
dFC methods overlook causal influences and struggle with complex brain network structures for deep learning.
Existing methods face challenges in capturing high-dimensional representations from brain networks.

Purpose of the Study:

To develop a novel framework for constructing dynamic effective connectivity (dEC) for improved brain network analysis.
To apply advanced deep learning techniques for robust feature extraction and representation learning from dEC.
To enhance the classification accuracy of mild cognitive impairment (MCI) stages using the proposed framework.

Main Methods:

A group constrained Kalman filter (gKF) algorithm was used to construct dEC, offering a directional interaction understanding.

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A virtual adversarial training convolutional neural network (VAT-CNN) was employed for robust local feature extraction from dEC.

High-order connectivity weight-guided graph attention networks (cwGAT) aggregated dEC features for effective high-level representations.

Main Results:

The proposed framework achieved high classification accuracies: 90.9% (NC vs. EMCI), 89.8% (EMCI vs. LMCI), and 82.7% (NC vs. EMCI vs. LMCI).
The VAT strategy effectively improved model robustness and prevented overfitting.
The cwGAT significantly enhanced high-level feature representations compared to conventional GAT.

Conclusions:

The developed framework provides a more comprehensive understanding of dynamic brain networks than dFC methods.
The novel VAT-CNN and cwGAT integration offers superior feature extraction and representation learning for brain network analysis.
This approach demonstrates significant potential for accurate and robust classification of mild cognitive impairment stages.