  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.
