  This work proposes an algorithm for fMRI data analysis for the classification
of ADHD disorders. There have been several breakthroughs in the analysis of
fMRI via 3D convolutional neural networks (CNNs). With these new techniques it
is possible to preserve the 3D spatial data of fMRI data. Additionally there
have been recent advances in the use of 3D generative adversarial neural
networks (GANs) for the generation of normal MRI data. This work utilizes multi
modal 3D CNNs with data augmentation from 3D GAN for ADHD prediction from fMRI.
By leveraging a 3D-GAN it would be possible to use deepfake data to enhance the
accuracy of 3D CNN classification of brain disorders. A comparison will be made
between a time distributed single modal 3D CNN model for classification and the
modified multi modal model with MRI data as well.
