The sheer complexity of the brain has forced the neuroscience community and specifically the neuroimaging experts to transit from the smaller brain datasets to much larger hard-to-handle ones. The primary goal of flagship projects such as the BRAIN Initiative and Human Brain Project is to gain a better understanding of the human brain and to treat the neurological and psychiatric disorders through the cutting-edge technologies in the biomedical imaging field. In the context of fMRI, the primary challenge is obtaining meaningful results from the intrinsic complex structure of large fMRI data and lack of clear insight into the underlying neural activities. However, archiving, analyzing, and sharing the fast-growing neuroimaging datasets posed significant challenges. New computational methods and technologies have emerged in the domain of Big Data but have not been fully adapted for use in neuroimaging. In this dissertation, I introduce my efforts toward creating a comprehensive platform to store, to manage and to process such datasets. I further present my GPU-based deep learning solution for distributed data processing that employs TensorFlow, Apache Spark, and Hadoop using cloud computing services. Finally, I demonstrate the significant performance gains of our platform enabling data-driven extraction of hierarchical information from massive fMRI data using a distributed deep convolutional autoencoder model.
