# Nonlinear Causal Analysis of Neural Signals

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $346,135

## Abstract

Abstract
The goal of this research is to develop new multivariate data analysis techniques for neural recordings that
reveal causal dependencies between recording sites. Delay Differential Analysis (DDA) is a robust and efficient
nonlinear time-domain algorithm for time series data that complements linear spectral methods. DDA combines
delay and differential embeddings in nonlinear dynamical systems to discriminate between different normal and
abnormal cortical states with high temporal resolution and insensitivity to artifacts. The proposed research
generalizes Granger causality for linear systems by developing a cross-dynamical version of DDA (CD-DDA) to
measure the flow of information between brain areas. This is an important problem for which existing approaches
are inadequate. CD-DDA will be applied first to simulations of cortical network models with Hodgkin-Huxley
neurons, where causal influence can be controlled and the efficacy of CD-DDA can be validated. In collaboration
with Sydney Cash at the Massachusetts General Hospital, CD-DDA will then be applied to electrocorticography
(ECoG) recordings from human epilepsy patients with implanted grids of electrodes. We previously analyzed
these recordings with DDA, which revealed differences between cortical states leading up to seizures, abrupt
shifts at the onsets of the seizures and altered cortical states long after the seizures. These ECoG recordings
will be re-analyzed using CD-DDA, which should reveal how communication between cortical areas reconfigures
before seizures. We also have access to many hours of interictal recordings, which will give us the opportunity
to establish a baseline for how information flows in cortical circuits during more normal cortical activity. We will
make the software for all of the DDA algorithms we have developed openly available. These new algorithms will
have many other applications for analyzing neural signals online in other brain areas and from other neural time
series, including calcium fluorescence imaging from single cells, dendrites and synapses and recordings using
voltage-sensitive dyes.

## Key facts

- **NIH application ID:** 9999583
- **Project number:** 5R01EB026899-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** TERRENCE J SEJNOWSKI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $346,135
- **Award type:** 5
- **Project period:** 2018-09-22 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999583

## Citation

> US National Institutes of Health, RePORTER application 9999583, Nonlinear Causal Analysis of Neural Signals (5R01EB026899-03). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/9999583. Licensed CC0.

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