Fusion of generative adversarial networks and non-negative tensor decomposition for depression fMRI data analysis

Objective:

This study introduces a novel approach, F-GAN-NTD, which integrates Generative Adversarial Networks (GANs) with Non-negative Tensor Decomposition (NTD) theory to enhance the analysis of functional Magnetic Resonance Imaging (fMRI) data related to depression.

Methods:

F-GAN-NTD is applied to extract nonlinear non-negative factors from multidimensional fMRI tensor data, utilizing Deep-NTD technology to generate factor matrices that capture latent structures and dynamic features. A multi-view neural network architecture processes these factor matrices from all modalities simultaneously, enabling comprehensive pattern discrimination between depression patients and healthy controls. The method is tested on the Closed Eyes Depression fMRI (CEDF) and Strategic Research Program for Brain Sciences (SRPBS) datasets.

Results:

The F-GAN-NTD method demonstrates significant improvements in fMRI data classification, outperforming traditional approaches. It also effectively restores incomplete fMRI tensor data and reveals abnormal brain network connections, offering insights into the pathophysiological mechanisms of depression.

Conclusions:

F-GAN-NTD enhances the extraction of meaningful features from fMRI data, improving classification performance and providing a deeper understanding of depression-related brain abnormalities. The integration across modalities contributes to a more comprehensive analysis of depression.