Infrastructure for hyperaligning fMRI data and estimating functional topographies

PROJECT SUMMARY Shared information in cortical functional architecture is embedded in topographies that are idiosyncratic, posing a major impediment for functional brain imaging research. Hyperalignment resolves this problem by projecting information from individual brains into a common model information space.

The proposed research project will create HyperBase – research infrastructure that will enable the brain imaging research community to leverage hyperalignment to greatly enrich their data, enable analyses of shared information and individual differences embedded in idiosyncratic fine-scale cortical topographies, and create a data sharing platform for data in the

hyperaligned common model information space. The infrastructure will be an optimized, standardized template common model space based on a normative database, turnkey software tools for hyperaligning new brains and estimating individual functional topographies, and a framework for sharing hyperaligned data. These data and tools will provide community

infrastructural support for research on a broad range of topics in clinical neuroscience, brain aging, and basic cognitive neuroscience. The proposed database will consist of fMRI data in 60 participants collected during movie viewing, story listening, at rest, and during a large set of functional localizers, augmented with demographic information and cognitive and personality

test scores. Specific aims 1. Produce an optimized, standardized template for hyperalignment based on a normative database with open-source software that will allow mapping numerous functional topographies, based on standard localizer data in the normative sample, into new participant brains using only fMRI data collected while the new participants watch a movie,

listen to a story, or are at rest. 2. Adapt hyperalignment algorithms to work with a standard template and estimate functional topographies via the template and normative localizer data. Develop new hyperalignment algorithms that increase power, precision, and flexibility. 3. Create a system for sharing functional brain imaging data that are projected into the

common information space model, allowing accumulation of data in a framework that affords at a fine-grained level of detail. Hyperalign existing public datasets.