# Individualized spatial topology in functional neuroimaging

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $613,505

## Abstract

Project Summary.
Neuroimaging provides unparalleled advantages in the ability to understand the neural
architecture underlying human thought, feeling, and behavior. Modern approaches
combine methodological advances in data acquisition with predictive modeling and
larger and more diverse datasets. The result is increasingly sophisticated models that
can map patterns of activity onto mental states, behaviors, and experiences. However,
there are fundamental limitations impeding progress. First, brains differ in their individual
functional topography. Second, it is difficult to make inferences about the spatial
topography of brain responses and their variability across individuals. Recent work on
inter-subject functional alignment promises to revolutionize brain systems-level modeling
of behaviors and mental states by aligning meso-scale activity patterns across
individuals. The most popular approach is hyperalignment (HA), which aligns functional
brain representations across individuals based on assumptions of a shared
representational geometry. However, a large body of work shows that much information
about functional brain representations is contained in macro-scale topographical maps.
This conventional functional topography is defined on a different, shape-preserving
topological manifold better captured using diffeomorphic alignment. We address these
issues, providing new ways of modeling topography, making topographical inferences,
and making inferences about the topological and geometrical spaces underlying brain
representations. We propose to develop diffeomorphic latent space models (DLSMs)
that preserve and provide spatial inferences on large-scale topography. Further, we will
develop a new class of HA models that place spatial constraints on the transformations,
providing fine-scale alignment of representational geometry while minimizing
topographical disruption. Finally, we will combine this enhanced HA approach with the
DLSM model to create a multi-scale framework that uses diffeomorphic transformations
to address large-scale individual differences, followed by geometric transformations to
address remaining meso-scale differences. This will allow us for the first time to
investigate the relative contributions of topological vs geometrical alignment of data in
different brain regions.

## Key facts

- **NIH application ID:** 10879920
- **Project number:** 2R01EB026549-05
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Abhirup Datta
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $613,505
- **Award type:** 2
- **Project period:** 2018-07-18 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10879920

## Citation

> US National Institutes of Health, RePORTER application 10879920, Individualized spatial topology in functional neuroimaging (2R01EB026549-05). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10879920. Licensed CC0.

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