Bayesian analysis of phase data in EEG and MEG.
Bayesian analysis of phase data in EEG and MEG.
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DOI：
10.7554/elife.84602
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发表时间：
2023-09-12
期刊：
影响因子：
7.7
通讯作者：
Houghton C
中科院分区：
文献类型：
--
作者：
Dimmock S;O'Donnell C;Houghton C
脑电图和脑磁图记录是非侵入性的，在时间上精确，使它们成为研究人类神经反应的宝贵工具。然而，这些录音是嘈杂的，这既是因为涉及的神经元电动力学产生了抑制的信号，也是因为感兴趣的神经元过程与许多其他过程竞争，从眨眼到白日做梦。对这种噪音的一个卓有成效的反应是使用特定频率的刺激，并在该频率的反应中寻找感兴趣的信号。通常，该信号涉及测量响应相位的相干性：这里，描述了一种测量相位相干性的贝叶斯方法。用神经语言学的两个例子说明了这种贝叶斯方法，并用模拟数据探索了它的性质。我们认为，贝叶斯方法比传统的统计方法更具描述性，因为它为数据如何产生提供了一个明确的、可解释的生成模型。它的数据效率也更高：与标准方法相比，它可以在参与者人数较少的情况下检测与刺激相关的差异。相位相干性是对波的测量，例如脑波，它量化了它们在固定频率下振荡行为的相似性。也就是说，虽然波可以每分钟振动相同的次数，但波相对于彼此的相对定时可以是不同的(非相干的)或相似的(相干的)。在神经科学中，科学家研究脑电波的相位一致性，以了解大脑如何对外部刺激做出反应，例如，如果它们在实验期间以固定频率出现。要做到这一点，相位相干性通常是用一个被称为“试验间相干性”(ITPC)的统计量来量化的。当ITPC等于1时，波是完全相干的，也就是说，两波之间没有移位，峰谷完全同时发生。当ITPC等于零时，波以完全随机的方式彼此移位。相位相干性也可以根据相位角和缠绕分布来模拟，相位角描述了每一波相对于零的参考角的偏移。包裹分布是相角上的概率分布，表示它们的相对可能性。包装分布具有统计量，包括平均值和方差。包络分布的方差可以用来建模相位相干性，因为它明确地表示相位角相对于平均值的相似性：方差越大，相干性越小。虽然ITPC是分析相位相干性的一种流行方法，但它是所谓的“汇总统计量”。使用ITPC的分析丢弃了试验到试验水平数据中的有用信息，而使用相角可能不会丢失这些信息。因此，Dimmock、O‘Donnell和Houghton着手确定他们是否可以创建一个直接作用于相角(而不是ITPC)的相干性模型，并产生比现有方法更好的结果。Dimmock、O‘Donnell和Houghton使用实验和模拟数据将他们的模型与ITPC进行了比较。比较表明，与非语法短语相比，他们的模型可以在比ITPC更小的样本量下检测到大脑对语法短语的夹带，并且错误阳性更少。研究大脑如何处理语言的传统工具往往会在数据中产生大量噪音，这使得分析测量数据变得困难。迪莫克、奥唐奈和霍顿证明，大脑不仅会像一些人所说的那样，对短语中单词的“惊喜因素”做出反应，还会对它们的语法类别做出反应。这项研究的这些结果将有助于分析相位相干性的科学家。通过使用该模型和其他方法来研究相位一致性，研究人员可以对他们的结果提供不同的视角，并潜在地在他们的数据中识别新的特征。这将在样本量较小的研究中特别有效，例如在试验性研究中，最大限度地利用数据是很重要的。
Electroencephalography and magnetoencephalography recordings are non-invasive and temporally precise, making them invaluable tools in the investigation of neural responses in humans. However, these recordings are noisy, both because the neuronal electrodynamics involved produces a muffled signal and because the neuronal processes of interest compete with numerous other processes, from blinking to day-dreaming. One fruitful response to this noisiness has been to use stimuli with a specific frequency and to look for the signal of interest in the response at that frequency. Typically this signal involves measuring the coherence of response phase: here, a Bayesian approach to measuring phase coherence is described. This Bayesian approach is illustrated using two examples from neurolinguistics and its properties are explored using simulated data. We suggest that the Bayesian approach is more descriptive than traditional statistical approaches because it provides an explicit, interpretable generative model of how the data arises. It is also more data-efficient: it detects stimulus-related differences for smaller participant numbers than the standard approach. Phase coherence is a measurement of waves, for example, brain waves, which quantifies the similarity of their oscillatory behaviour at a fixed frequency. That is, while the waves may vibrate the same number of times per minute, the relative timing of the waves with respect to each other may be different (incoherent) or similar (coherent). In neuroscience, scientists study phase coherence in brain waves to understand how the brain responds to external stimuli, for example if they occur at a fixed frequency during an experiment. To do this, phase coherence is usually quantified with a statistic known as ‘inter-trial phase coherence’ (ITPC). When ITPC equals one, the waves are perfectly coherent, that is, there is no shift between the two waves and the peaks and troughs occur at exactly the same time. When ITPC equals zero, the waves are shifted from each other in an entirely random way. Phase coherence can also be modelled on phase angles – which describe the shift in each wave relative to a reference angle of zero – and wrapped distributions. Wrapped distributions are probability distributions over phase angles that express their relative likelihood. Wrapped distributions have statistics, including a mean and a variance. The variance of a wrapped distribution can be used to model phase coherence because it explicitly represents the similarity of phase angles relative to the mean: larger variance means less coherence. While the ITPC is a popular method for analysing phase coherence, it is a so-called ‘summary statistic’. Analyses using the ITPC discard useful information in the trial-to-trial-level data, which might not be lost using phase angles. Thus, Dimmock, O’Donnell and Houghton set out to determine whether they could create a model of phase coherence that works directly on phase angles (rather than on the ITPC) and yields better results than existing methods. Dimmock, O’Donnell and Houghton compare their model to the ITPC using both experimental and simulated data. The comparison demonstrates that their model can detect entrainment of the brain to grammatical phrases compared to ungrammatical ones at smaller sample sizes than ITPC, and with fewer false positives. Traditional tools for studying how the brain processes language often yield a lot of noise in the data, which makes it difficult to analyse measurements. Dimmock, O’Donnell and Houghton demonstrates that the brain is not simply responding to the ‘surprise factor’ of words in a phrase, as some have suggested, but also to their grammatical category. These results of this study will benefit scientists who analyse phase coherence. By using the model in addition to other approaches to study phase coherence, researchers can provide a different perspective on their results and potentially identify new features in their data. This will be particularly powerful in studies with small sample sizes, such as pilot studies where maximising the use of data is important.
影响因子：
4.2
作者：
Oomen, Danna;Cracco, Emiel;Brass, Marcel;Wiersema, Jan R.
通讯作者：
Wiersema, Jan R.
影响因子：
4.6
作者：
Lewis AG;Schriefers H;Bastiaansen M;Schoffelen JM
通讯作者：
Schoffelen JM
影响因子：
4.6
作者：
Barzegaran E;Norcia AM
通讯作者：
Norcia AM