# DDALAB: Identifying Latent States from Neural Recordings with Nonlinear Causal Analysis

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $1,240,738

## Abstract

Summary
The goal of this proposal is to develop DDALAB, a software platform that will make it possible for researchers
to identify latent cortical states and analyze the flow of information in large populations of neurons using Delay
Differential Analysis (DDA). Although DDA can be used to analyze any time series data, we will initially focus
on EEG recordings from the scalp and iEEG data recordings directly from the brain. In addition to developing
software making it easy for an investigator to analyze their own recordings, we will also develop interfaces with
recordings stored in OpenNeuro archive, a data repository funded the The BRAIN Initiative. These data can be
analyzed and visualized with the DDALAB running on local computers or imported directly from OpenNeuro into
the NEMAR resource and processed via the Neuroscience Gateway (NSG) at the San Diego Supercomputer
Center (SDSC) for High Performance Computing (HPC). We propose to integrate DDALAB into the existing
ecosystem supported by the BRAIN Initiative.
Delay Differential Analysis (DDA) is a nonlinear, time-domain technique that fits time series waveforms, which
complements commonly used frequency domain techniques based on linear Fourier analysis. DDA has a number
of advantages for analyzing brain recordings:
• DDA is able to extract nonlinear features in recordings that are invisible to linear techniques.
• Neural recordings and other time series can be accurately fit with a few low-order time-delayed polynomial
terms, typically having only 3 parameters. This reduces overfilling and makes DDA insensitive to most
artifacts, allowing DDA to be used for online analysis of raw recordings without preprocessing.
• The output of DDA is a highly compressed version of the time series because noise and artifacts are
ignored. DDA extracts and distills brain signals from raw data for later analysis.
• Much less data are required to specify a model compared with machine learning.
• The same set of DDA models fits recordings across subjects, suggesting that DDA is capturing fundamental
properties of cortical dynamics.
• Fewer time points are needed in a moving window compared with spectral windows, improving the time
resolution.
DDALAB will provide data analysis for identifying latent changes in cortical states and visualization tools that can
be used to extract estimates for the directed flow of information between brain areas. These methods can be
applied by the research community at large to analyze a wide range of brain recordings and to develop better
treatments for patients with brain diseases. The software developed in this proposal will be openly available
through GitHub with an Open Source Software license. Users will not have to buy commercial software or
depend on proprietary data formats.

## Key facts

- **NIH application ID:** 10643212
- **Project number:** 1RF1MH132664-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** TERRENCE J SEJNOWSKI
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,240,738
- **Award type:** 1
- **Project period:** 2023-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10643212

## Citation

> US National Institutes of Health, RePORTER application 10643212, DDALAB: Identifying Latent States from Neural Recordings with Nonlinear Causal Analysis (1RF1MH132664-01). Retrieved via AI Analytics 2026-07-10 from https://api.ai-analytics.org/grant/nih/10643212. Licensed CC0.

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