  In recent years, analyzing task-based fMRI (tfMRI) data has become an
essential tool for understanding brain function and networks. However, due to
the sheer size of tfMRI data, its intrinsic complex structure, and lack of
ground truth of underlying neural activities, modeling tfMRI data is hard and
challenging. Previously proposed data-modeling methods including Independent
Component Analysis (ICA) and Sparse Dictionary Learning only provided a weakly
established model based on blind source separation under the strong assumption
that original fMRI signals could be linearly decomposed into time series
components with corresponding spatial maps. Meanwhile, analyzing and learning a
large amount of tfMRI data from a variety of subjects has been shown to be very
demanding but yet challenging even with technological advances in computational
hardware. Given the Convolutional Neural Network (CNN), a robust method for
learning high-level abstractions from low-level data such as tfMRI time series,
in this work we propose a fast and scalable novel framework for distributed
deep Convolutional Autoencoder model. This model aims to both learn the complex
hierarchical structure of the tfMRI data and to leverage the processing power
of multiple GPUs in a distributed fashion. To implement such a model, we have
created an enhanced processing pipeline on the top of Apache Spark and
Tensorflow library, leveraging from a very large cluster of GPU machines.
Experimental data from applying the model on the Human Connectome Project (HCP)
show that the proposed model is efficient and scalable toward tfMRI big data
analytics, thus enabling data-driven extraction of hierarchical neuroscientific
information from massive fMRI big data in the future.
