# Infrastructure for hyperaligning fMRI data and estimating functional topographies

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2024 · $641,619

## Abstract

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.

## Key facts

- **NIH application ID:** 10848375
- **Project number:** 5R01MH127199-03
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Maria I Gobbini
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $641,619
- **Award type:** 5
- **Project period:** 2022-08-23 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10848375

## Citation

> US National Institutes of Health, RePORTER application 10848375, Infrastructure for hyperaligning fMRI data and estimating functional topographies (5R01MH127199-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10848375. Licensed CC0.

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