BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification

• URL: http://arxiv.org/abs/2103.08494v1
• Date: Wed, 10 Mar 2021 23:44:53 GMT
• Title: BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
• Authors: Chao Li, Yiran Wei, Xi Chen
• Abstract summary: Alzheimer's disease is the most common age-related dementia. Brain MRI offers a noninvasive biomarker to detect brain aging. Alzheimer's disease is the most common age-related dementia.
• Score: 9.312868504719193
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks. Structural connectivity matrices between separated brain regions are constructed using tractography on diffusion MRI data. The BrainNetGAN model is trained to generate fake brain connectivity matrices, which are expected to reflect latent distribution of the real brain network data. Finally, a convolutional neural network classifier is proposed for binary dementia classification. Numerical results show that the binary classification performance in the testing set was improved using the BrainNetGAN augmented dataset. The proposed methodology allows quick synthesis of an arbitrary number of augmented connectivity matrices and can be easily transferred to similar classification tasks.

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