# Computational Neuroanatomy

> **NIH NIH U19** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $364,595

## Abstract

Project 5. Abstract
 Computational Neuroanatomy (Yoav Freund, lead; Friedman, Karten, Kleinfeld)
 Anatomical atlases play an essential role for characterization of circuitry by collation of “Components”
which in turn enables reverse engineering of these circuits. Control of orofacial actions is coordinated by
distinct populations of brain stem premotor neurons, which are arranged into relatively small clusters and can
be limited to domains as small as 200 to 300 µm in extent. Further, for many orofacial motor actions, premotor
neuronal clusters are present at multiple levels of the brainstem and do not conform to the boundaries
previously defined by available atlases, including the Paxinos atlases and the Allen Brain Common Coordinate
Framework atlas.
 We propose to construct a Trainable Texture-based Digital Atlas from digitized stacks of brain images
obtained by tape-transfer of serial cryosections through the brain (Core 2 - Precision Histology) to enable
mapping of the brainstem premotor interface modulation of orofacial motor actions. The atlas design allows
labeled cells, projections and recording sites to be accurately and automatically aligned across different brains.
Our Trainable Texture-based Digital Atlas makes use of identification of landmarks based on texture features
of Nissl stained cytoarchitecture. The landmarks are identified by expert anatomists and are used to create
training sets for machine learning. Machine learning is used to train texture detectors to distinguish between
different cytoarchitectural textures in order to automate landmark identification that is consistent with the
original manual landmark annotations by anatomists. This process and the automated alignment of new brains
is performed in three dimensions
 The Trainable Texture-based Digital Atlas is implemented on a computer cloud server (Core 3 - Data
Science). This enables us to integrate experimental results across all of the project participants and data from
others outside our project. Thus the Digital Atlas is platform-independent. Our data management is designed to
facilitate accessibility of the atlas, of meta data that describes experimental output, and of mappings back to all
slices in each brain, which is expected to take at most a few Gbytes. All users will be able to efficiently browse
the Digital Atlas and meta-data. It will also be possible retrieve subsets of images from full brain stacks for
validation of raw data.
 Our particular focus is on the brainstem. Yet the system is general and can be expanded to the entire
central nervous system; indeed, a new graduate student has begun work on a joint project with Dr. Einman
Azim (Salk Institute), to extend the atlas to the spinal cord, another CNS region with challenging
cytoarchitectural borders for subregion parcellation.

## Key facts

- **NIH application ID:** 10199078
- **Project number:** 5U19NS107466-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Yoav Shai Freund
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $364,595
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10199078

## Citation

> US National Institutes of Health, RePORTER application 10199078, Computational Neuroanatomy (5U19NS107466-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10199078. Licensed CC0.

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