Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea.
Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom.
J Neural Eng. 2023 Jul 14;20(4). doi: 10.1088/1741-2552/acdffd.
. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.
. 最近基于事件的瞬态神经活动分析将爆发性振荡描述为一种神经特征，它将动态神经状态与认知和行为联系起来。基于这一观点，我们的研究旨在：(1) 使用合成信号比较不同信噪比和事件持续时间下常见爆发检测算法的效果；(2) 为具有未定义性质的真实数据集选择最佳算法制定一个策略性指导方针。我们使用包含多个频率爆发的模拟数据集来测试爆发检测算法的稳健性。为了系统地评估它们的性能，我们使用了一种称为“检测置信度”的度量标准，以平衡的方式量化分类准确性和时间精度。鉴于经验数据中的爆发特性通常事先未知，我们随后提出了一种选择规则，用于为给定数据集识别最佳算法，并验证其在从暴露于自然威胁的雄性小鼠的外侧杏仁核记录的局部场电位上的应用。我们的基于模拟的评估表明，爆发检测取决于事件持续时间，而准确确定爆发起始时间更容易受到噪声水平的影响。对于真实数据，根据选择规则选择的算法表现出更高的检测和时间精度，尽管其统计显著性在不同频段之间有所不同。值得注意的是，人类视觉筛选选择的算法与规则推荐的算法不同，这意味着算法的人类先验和数学假设之间可能存在潜在的不匹配。因此，我们的研究结果强调，瞬态爆发的精确检测从根本上受到所选算法的影响。所提出的算法选择规则提供了一种潜在的可行解决方案，同时也强调了源自算法设计和数据集之间不稳定性能的固有局限性。因此，本研究警告不要仅依赖启发式方法，主张在爆发检测研究中仔细选择算法。
