通过原子分解(BEAD)的突发估计:在大脑信号中发现振荡突发的工具箱。

Imaging neuroscience (Cambridge, Mass.) Pub Date : 2025-07-21 eCollection Date: 2025-01-01 DOI:10.1162/IMAG.a.86
Abhishek Anand, Chandra Murthy, Supratim Ray
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引用次数: 0

摘要

最近的研究表明,大脑信号经常表现出短时间的振荡爆发,这与计算和行为的各个方面有关。传统方法通常使用直接谱估计器来估计脑信号在谱域和时域的功率,并从中识别脉冲。然而,已知直接谱估计器是有噪声的,因此即使稳定的振荡也可能出现突发。我们之前已经展示了匹配追踪(MP)算法,该算法使用基函数(称为“原子”)的大型过完备字典直接在时域中分解信号,部分解决了这一问题,并在合成数据和真实数据中健壮地发现了长突发。然而,MP是一种贪婪算法,可能会给出非最优解,并且需要一个大容量的字典。为了解决这些问题,我们扩展了另外两种算法-正交MP (OMP)和使用多尺度自适应Gabor扩展(OMP- mage)的OMP,以执行突发持续时间估计。我们还开发了一种新的算法,称为使用Gabor扩展与原子重新分配(OMP- gear)的OMP。这些算法克服了MP的限制,可以在更小的字典大小下工作。我们发现,在合成数据中,OMP、OMP- mage和OMP- gear收敛速度快于MP。此外,当字典大小较小时,OMP- mage和OMP- gear的性能优于MP和OMP。最后,当爆发重叠时,OMP-GEAR的性能明显优于OMP-MAGE。重要的是,在两只猴子被动观看诱发初级视觉皮层伽马暴的静态光栅的真实数据中,使用MP和OMP获得的脉冲持续时间与使用OMP- mage获得的脉冲持续时间相当。OMP-GEAR的爆发持续时间略短,但所有估计的爆发持续时间仍明显大于使用传统方法估计的持续时间。这些结果表明伽马暴比之前报道的要长。来自两只猴子的原始数据,以及传统和新方法的代码,作为这个工具箱的一部分公开提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Burst estimation through atomic decomposition (BEAD): A toolbox to find oscillatory bursts in brain signals.

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.

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