The cloud-services for functional network neuroscience

Joshua Faskowitz Presenter
Indiana University
Psychological and Brain Sciences
Bloomington, IN 
United States
Software Demonstrations 
Using functional magnetic resonance imaging (fMRI), we can measure the brain's distributed functional organization. Maps of fMRI activity can be used to create functional networks, which in turn can be analyzed using the tools of network science to uncover brain-wide properties such as functional community organization [10] or hub-like structure [6].
The field of Network Neuroscience exists at the intersection of human brain mappers and network science practitioners. These fields require both advanced software skills and mathematical knowledge. Whereas on the one hand, fMRI specialists learn to employ highly specialized image processing techniques and must make sure their data is artifact-free, on the other hand, network scientists focus on learning and developing innovative network science algorithms applicable across fields. To achieve expert-level knowledge in both domains is both a challenge and a barrier for investigators and trainees in either field.
Our work promotes FAIR principles [9] by addressing the challenges highlighted above. We present a series of cloud computing services that make network neuroscience more accessible by enabling the generation of functional brain networks in a streamlined and intuitive manner. The services comprise of containerized "Apps" that process MRI data from the raw NIFTI files (for both fMRI and T1-weighted anatomy) to node-by-node functional connectivity matrices. These services can be run automatically on the various datasets available on, BIDS data hoster on, or on user-uploaded data via a point-and-click web-interface. The interface allows users to take advantage of a powerful distributed cloud computing infrastructure via Finally, generates a full provenance record for the data generated by keeping track of Apps (and versions) used to build the brain networks, supporting the aim of computational reproducibility [5].