# Crossing space and time: uncovering the nonlinear dynamics of multimodal and multiscale brain activity

> **NIH NIH R01** · EMORY UNIVERSITY · 2020 · $1,124,452

## Abstract

The brain is a complex dynamical system, with a hierarchy of spatial and temporal scales ranging from microns
and milliseconds to centimeters and years. Activity at any given scale contributes to activity at the scales above
it and can influence activity at smaller scales. Thus a true understanding of the brain requires the ability to
understand how each level contributes to the system as a whole.
Most brain research focuses on a single scale (single unit firing, activity in a circuit), which cannot account for
the constraints imposed by activities at other scales. The goal of this proposal is to develop a framework for the
integration of multiscalar, multimodal measurements of brain activity. One of the challenges in understanding
how activity translates across scales is that features that are relevant at one scale (e.g., firing rate) do not have
clear analogues at other scales. We address this issue by defining trajectories in “state space” at each scale,
where the state space is defined by parameters and time scales appropriate to each type of data. The trajectory
of brain activity through state space can uncover features like attractor dynamics and limit cycles that
characterize the evolution of activity. Using machine learning along with new and existing multimodal
measurements of brain activity (MRI, optical, and electrophysiological), we propose to establish methods that
relate trajectories across scales while handling the mismatch in temporal sampling rates inherent in multi-scale
data. Specific aims are 1. Create and test a tool for learning how trajectories at fast scales influence activity at
slower scales. Different modalities have different inherent temporal resolutions in addition to different types
of contrast. Current methods generally downsample the faster modality in some way, losing much information
in the process. We will leverage variants on long short-term memory (LSTM) network architectures to learn the
relationship between state space trajectories acquired simultaneously with population recording and optical
imaging, and with optical imaging and fMRI. 2. Create and test a tool for learning how trajectories at slow
scales influence activity at faster scales. Leveraging the same LSTM-based approach, we will learn how
slower, larger scale activity affects activity at smaller scales, using whisker stimulation as a test case. We
anticipate inclusion of the large scale activity (measured with fMRI or optical imaging) will improve prediction
of the response at smaller scales (measured with optical imaging or population recording).
Our work will allow us to begin to answer a wide range of questions about how the brain functions (e.g., what
type of localized stimulation that will drive the brain to a desired global state? How does modulation of the
global brain state affect local information processing?) and provide guidance for future experiments by
identifying key features that influence activity across scales. By approaching the...

## Key facts

- **NIH application ID:** 10007011
- **Project number:** 1R01EB029857-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Shella D Keilholz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,124,452
- **Award type:** 1
- **Project period:** 2020-09-17 → 2024-09-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10007011

## Citation

> US National Institutes of Health, RePORTER application 10007011, Crossing space and time: uncovering the nonlinear dynamics of multimodal and multiscale brain activity (1R01EB029857-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10007011. Licensed CC0.

---

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