Evidence map›Paper›PMID 40800821›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2025

Burst estimation through atomic decomposition (BEAD): A toolbox to find oscillatory bursts in brain signals.

Abhishek Anand, Chandra Murthy, Supratim Ray

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In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Abhishek AnandElectrical Communication Engineering, Indian Institute of Science, Bengaluru, Karnataka, India.
Chandra MurthyElectrical Communication Engineering, Indian Institute of Science, Bengaluru, Karnataka, India.
Supratim RayCentre for Neuroscience, Indian Institute of Science, Bengaluru, Karnataka, India.ORCID https://orcid.org/0000-0002-1968-1382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies have shown that brain signals often show oscillatory bursts of short durations, which have been linked to various aspects of computation and behavior. Traditional methods often use direct spectral estimators to estimate the power of brain signals in spectral and temporal domains, from which bursts are identified. However, direct spectral estimators are known to be noisy, such that even stable oscillations may appear bursty. We have previously shown that the Matching Pursuit (MP) algorithm, which uses a large overcomplete dictionary of basis functions (called "atoms") to decompose the signal directly in the time domain, partly addresses this concern and robustly finds long bursts in synthetic as well as real data. However, MP is a greedy algorithm that can give non-optimal solutions and requires a large-sized dictionary. To address these concerns, we extended two other algorithms-orthogonal MP (OMP) and OMP using Multiscale Adaptive Gabor Expansion (OMP-MAGE), to perform burst duration estimation. We also develop a novel algorithm, called OMP using Gabor Expansion with Atom Reassignment (OMP-GEAR). These algorithms overcome the limitations of MP and can work with a significantly smaller dictionary size. We find that, in synthetic data, OMP, OMP-MAGE, and OMP-GEAR converge faster than MP. Also, OMP-MAGE and OMP-GEAR outperform both MP and OMP when the dictionary size is small. Finally, OMP-GEAR significantly outperforms OMP-MAGE when the bursts are overlapping. Importantly, the burst durations obtained using MP and OMP with a very large-sized dictionary are comparable with that obtained using OMP-MAGE with a much smaller-sized dictionary in real data obtained from two monkeys passively viewing static gratings which induced gamma bursts in the primary visual cortex. OMP-GEAR yields slightly smaller burst durations, but all the estimated burst durations are still significantly larger than the duration estimated using traditional methods. These results suggest that gamma bursts are longer than previously reported. Raw data from two monkeys, as well as codes for both traditional and new methods, are publicly available as part of this toolbox.

Indexed as

burst detectioncompressed sensinggammalocal field potentialmatching pursuitovercomplete representations

Identifiers

PMID40800821
PMCPMC12330832

What Socratic holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.