# Functional regionalization of the brain revealed by multi-modal neural and genomics dataintegration

> **NIH LM R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2026 · $369,000

## Abstract

Project Summary/Abstract
In the past decade, genome-wide characterization of gene expression in cells dissociated from biological
tissues has transformed the understanding of cell types that build organs in a variety of organisms. However,
to define the precise arrangement of cell types within a tissue or organ, analysis of large-scale spatial
transcriptomics and integration with other spatial datasets such as neural connectivity patterns is needed. To
achieve these integrative analyses of multiple highly dimensional datasets (e.g., hundreds of genes in cellular
resolution, measured across the whole organ), all datasets need to be brought together in the same common
coordinate system and new computational algorithms that can handle the complexity of data need to be
developed. Studies from the Allen Institute for Brain Science and the Broad Institute are now providing the first
whole-brain spatially resolved transcriptomics datasets and providing an integrative view of the cells that make
up the brain and their spatial location. One opportunity that these new datasets provide is to define a
completely data-driven anatomic parcellation/atlas of the mouse brain. Such a parcellation will be an enormous
resource for the systems and molecular neuroscience communities both in formulating new hypotheses for the
mechanisms of brain function and investigating existing results. However, current methods are unable to
accommodate the scale, complexity, and inherently multimodal nature of integrating these spatial cellular and
molecular taxonomies with the wealth of other data (such as connectomics, proteomics, and functional). In this
proposal, we aim to utilize new developments in the field of machine learning to address this need for the
development of an unsupervised computational algorithm that can synthesize disparate and large datasets of
the mouse brain into the next generation of reference anatomical parcellation/atlas. Specifically, we propose a
novel deep learning fram

## Key facts

- **NIH application ID:** 11349675
- **Project number:** 5R01LM014619-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Reza  Abbasi Asl
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** LM
- **Fiscal year:** 2026
- **Award amount:** $369,000
- **Award type:** 5
- **Project period:** 2025-06-10T00:00:00 → 2029-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11349675

## Citation

> US National Institutes of Health, RePORTER application 11349675, Functional regionalization of the brain revealed by multi-modal neural and genomics dataintegration (5R01LM014619-02). Retrieved via AI Analytics 2026-07-20 from https://api.ai-analytics.org/grant/nih/11349675. Licensed CC0.

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