# Integrated functional and structural analysis of an entire column in mouse primary visual cortex

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $1,334,079

## Abstract

PROJECT SUMMARY
Neurons in the visual cortex form an intricate connectivity structure and topographic arrangement. The
structural and morphological organization of the neurons is known to constrain its functional properties. To
understand these constraints, it is necessary to generate large-scale anatomical and functional measurements
of the brain. Ongoing efforts in electron microscopy (EM) and fluorescent microscopy promise to massively
accelerate the speed of generating such data. To discover the relationship between structure and function, one
approach is to use models of computation in the brain that have both structural and functional components.
Convolutional neural networks (CNNs), and more broadly machine learning (ML), have shown an increasing
promise in modeling the functional properties of the brain. However, these CNN-based models are often not
guided by the real structure of the neurons in the brain. In this proposal, we seek to share and analyze the
largest calcium imaging dataset collected to date from an entire column in mouse primary visual cortex (V1).
We propose to integrate this dataset with a separate publicly available electron microscopy (EM) dataset of
1mm3 volume in mouse V1 using a novel topologically-plausible CNN-based model of neurons. We will use
this new model to understand how the structural organization affords function. Our approach is to bring
topological modeling to the study of the computation in neural networks. The calcium imaging dataset includes
volumetric 2-photon and 3-photon imaging in four mice. The data contains recordings of visual responses from
neurons within an 800um × 800um × ~800um region of the primary visual cortex, spanning all visual layers
from pia to white matter. We propose to use this dataset to systematically characterize the physiological
properties of spatially-selective and motion-selective neurons in an entire column in mouse V1. We will
determine whether and how the organization and size of ON and OFF subfields in spatially-selective neurons
reveal differences across visual layers. We will then test the hypothesis that the spatially-selective and motion-
selective neurons form distinct networks within V1. We propose to incorporate these two networks into
convolutional neural network model of neurons to test whether this model results in higher predictive accuracy
and better estimation of neural pattern selectivity. Finally, we propose to characterize the branching motifs in
mouse V1 using the EM reconstructions from 1mm3 volume in mouse visual cortex. We will then integrate
these motifs with the CNN-based models of neurons to accurately predict calcium imaging responses. We will
use this setup to test whether incorporating the branching motifs in a model result in more accurate prediction
of neural activity and better estimation of neural pattern selectivity. Our proposal offers a systematic approach
to integrate structural and functional datasets to understand how structure affor...

## Key facts

- **NIH application ID:** 10505417
- **Project number:** 1RF1MH128672-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Reza Abbasi Asl
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,334,079
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10505417

## Citation

> US National Institutes of Health, RePORTER application 10505417, Integrated functional and structural analysis of an entire column in mouse primary visual cortex (1RF1MH128672-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10505417. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*