Enhancing neuroscientific workforce engagement in reproducible neuroimaging
Neuroscience is engaging at the forefront of science by dissolving disciplinary boundaries and promoting transdisciplinary research. This is a process that, in principle, can facilitate discovery by convergent efforts from theoretical, experimental and cognitive neuroscience, as well as computer science and engineering.
To assure the success of this process, the current lack of established mechanisms to guarantee reproducibility of scientific results must be overcome. Promoting open software and data sharing has become paramount to addressing this problem of reproducibility.Brain-Life addresses challenges to neuroscience reproducibility by providing integrative mechanisms for publishing data, and algorithms while embedding them with computing resources that can impact multiple scientific communities.
As a prototype, we currently host the following online services.
Initialize DWI data using dtiInit
Produce predicted connectomes with Ensemble Tractography
Measure Tract Model Prediction Error
Create adjacency matrix of brain connectome with LiFE
Identify major tracts segments using AFQ, and LiFE
Provide Quantitative Estimates of Connectome Quality
Process brain MRI images using Freesurfer (Coming Soon)
Reference Repository for Connectome Data and Derivatives
More applications will be made available once our proposal is accepted.
Who can use brain-life?
Use our services to understand the brain and behavior
Computer scientists, Engineers
Use published data and methods to advance computing and algorithms.
Mathematicians, and Others
Development of an open online platform will provide a reusable database of data derivatives. This database will reach out beyond neuroscience and allowing computer scientists, mathematical scientists and engineers interested in using the data to develop and publish their methods.
66 collaborators from global scientific communities will contribute to the project with data, software, providing technology and products as well as full use-case studies to understanding the human brain.
PI / Assistant Professor