# Tools for parameterizing and visualizing electrophysiological rhythmic and arrhythmic features

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $64,342

## Abstract

Project Summary
Cognition requires the dynamic coordination of neural ensembles across multiple brain regions. This is one of
the biggest neuroscientific questions: how do neural populations form transient communication networks in the
service of cognition? One exciting candidate mechanism by which this occurs is through the coupling of neural
oscillations between brain regions. These oscillations are a ubiquitous feature of electrophysiology, occurring
across species. Despite their wide study, recent work has highlighted many pitfalls in analyzing oscillations,
largely centered around three major issues: 1) Oscillations should be measured relative to the aperiodic (1/f)
background because, strictly speaking, oscillations are defined as any regions of the power spectrum that rise
above the 1/f background, which has itself been shown to be dynamic in relation to both cognition and disease;
2) Most tools for extracting and quantifying oscillations assume that they are sinusoidal despite the fact that
they rarely ever are. Further, those non-sinusoidal features may carry critical physiological information; 3)
Traditional methods can conflate bursting and non-bursting oscillations, despite the rapidly mounting evidence
that the two oscillatory modes are distinct, and may even play different functional roles.
In this project we will significantly expand upon the success of our neural data analysis toolboxes to allow for
time-resolved analysis of aperiodic neural activity. We will test the validity of our tools against real and
simulated data. This extension is specifically designed to address the first major oscillation analysis issue
outlined above. After testing, this extension will be incorporated into the existing analytic toolboxes developed
previously from this grant. We will then leverage this new extension to show, in proof-of-concept, how it can be
used to uncover novel results in human visual working memory encoding. All of these will be done using open-
source tools, built to industry standards of software development, in a transparent manner.
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## Key facts

- **NIH application ID:** 10442179
- **Project number:** 3R01GM134363-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Bradley T. Voytek
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $64,342
- **Award type:** 3
- **Project period:** 2019-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10442179

## Citation

> US National Institutes of Health, RePORTER application 10442179, Tools for parameterizing and visualizing electrophysiological rhythmic and arrhythmic features (3R01GM134363-03S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10442179. Licensed CC0.

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