# Image-based modeling of functional connectivity in neural networks at single-cell resolution

> **NIH NIH K25** · UT SOUTHWESTERN MEDICAL CENTER · 2020 · $128,358

## Abstract

Project Summary/Abstract
Calcium fluorescence imaging has opened unprecedented opportunities to investigate how neurons are wired in
circuits that plastically process information in the brain. Recent advances in microscopy and genetically encoded
calcium indicators allow us to record in real time the transient rises of intracellular Ca2+ for a large population of
neurons during their electrical activity. However, little is known about mechanisms of information processing in
neural circuits at the single neuron level. Even though cutting-edge technologies are capable of optically probing
thousands of neurons firing in relation to stimulation or behavior output, we are still unable to track the
propagation of the neuron firing events. The key barrier to progress is the lack of computational technologies in
image and signal processing for the calcium imaging data. A common but unresolved obstacle to collect calcium
activities of neurons from acquired images is deformation of live tissues during imaging. The goal of the project
for image processing is to develop an algorithm to automatically extract accurate traces of single-neuron activity
from deforming 3D calcium images. A new approach under development generates a dynamic region-of-interest
for each jittering and blinking neuron by iteratively learning neuronal identities from local images of firing neurons.
As a next step, the goal for signal processing is to develop statistical inference frameworks that can assess the
evidence of information flows from external stimuli to sensory neurons, and between interconnected neurons.
The responsiveness of neurons upon stimulation will be statistically determined based on an autoregressive
hidden Markov model. We will identify causal hierarchy among neuronal activities using Granger-causality
inference, in order to reconstruct the functional connectivity networks for large-scale neuronal populations.
Subsequent graph theoretical quantification of the connectivity networks at the single-neuron level will enable us
to differentiate wiring architectures of neural circuits under different molecular conditions.
The long-term career goal of the candidate, Dr. Noh, is to establish an independent research program specialized
in image-based stochastic modeling of dynamic nervous systems by translating his expertise in statistics and
time series analysis. The training objective of this proposal is to allow Dr. Noh to make a unique contribution to
computational methods for complex neuroimaging data and its dynamics, and to train Dr. Noh to gain the ability
to conduct hypothesis-driven research for neuroscience by himself. The proposed training is guided by Gaudenz
Danuser and Julian Meeks, who are leaders in the fields of computational cell biology and neurobiology,
respectively. Being engaged in diverse environment of informatics/experiments and neurobiology, Dr. Noh will
immerse himself into neuroscience, acquire experiential learning of neuroimaging experiments, ...

## Key facts

- **NIH application ID:** 10054899
- **Project number:** 1K25EB028854-01A1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Jungsik Noh
- **Activity code:** K25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $128,358
- **Award type:** 1
- **Project period:** 2020-09-21 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10054899

## Citation

> US National Institutes of Health, RePORTER application 10054899, Image-based modeling of functional connectivity in neural networks at single-cell resolution (1K25EB028854-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10054899. Licensed CC0.

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