The cloud services for human visual-field mapping & population receptive field estimate

David Hunt, B.A. in Neuroscience Presenter
Indiana University
Indiana University
Bloomington, IN 
United States
Software Demonstrations 
Using functional magnetic resonance imaging (fMRI) collected during a fixation task, we can measure the human visual cortex and the multitude of retinotopic (visual field) maps within it [7]. The measured fMRI signal can also be used to estimate the properties of the population receptive fields (PRFs) of individual cortical locations within each map [8], providing a quantitative measure of their expected response to certain visual stimuli. These in-vivo measurements are critical for understanding the functional architecture of the human visual system, how it develops, ages and responds to disease onset. These fields require both advanced software skills and knowledge of complex stack of software libraries. Indeed multiple libraries exist to allow estimating retinotopic maps and PRF parameters in living human brains e.g.,,,, or Yet, the skills necessary to use these software libraries are highly complex, this, in turn, can limit the application of the methods to studies led by investigators committed to learning advanced coding methods.

Our work promotes FAIR principles [9] by developing a series of cloud-services that make visual-fields and PRF mapping automated, accessible, and visualizable on the open-science platform The services comprise of containerized "Apps" that process MRI data from the raw NIFTI files (for both fMRI and T1-weighted anatomy). Users can upload their own retinotopic data to be automatically processed by these apps, or they can process the various datasets available on, or itself. The easy-to-use interface allows users to take advantage of a free cloud computing infrastructure available at Finally, generates a full provenance record for the data generated by keeping track of the App types and versions used to estimate visual field maps and PRF parameters.