自然刺激神经追踪中的相关性、零分布与显著性水平的建模与解释
Modeling and Interpreting Correlations, Null Distributions and Significance Levels in Neural Tracking of Natural Stimuli
AI总结：
本研究针对自然刺激神经追踪的相关性解释问题，提出刺激-响应错位的零假设选择，构建半参数模型生成零归一化追踪分数，通过EEG数据反转原始相关性结论，提供了高效的神经追踪分析方法论。
AI中文摘要：
神经追踪是指神经响应与语音、音乐、视频等连续刺激的时间锁定现象，被广泛用于研究大脑如何处理自然输入。追踪强度通常通过记录的神经响应与经数据驱动模型解码和/或编码的刺激之间的相关性来量化，该相关性常被用于比较刺激特征、模型或设置。然而，其大小不仅取决于大脑追踪刺激的强度，还受相关信号的统计特性影响；例如，携带极少语音内容信息的窄带语音包络会产生极高的相关性，仅因它更易被重构。因此，有意义的解释需将每个相关性与其零分布（即无刺激-响应关系时预期的相关性）进行比较。研究表明，用于构建零分布的常用随机化程序不可互换：每个程序隐含不同的零假设，本研究提出刺激-响应错位是最实用且合适的选择。由于可靠的零分布需大量排列，本研究引入一种半参数模型，采用Fisher变换后的正态分布，仅需3-5分钟数据即可生成准确的显著性水平，且能跨分析窗口长度预测该水平。在此基础上，本研究提出零归一化追踪分数，这是一种可解释的度量，将特征和模型置于共同尺度，与广泛使用的匹配-不匹配精度直接相关。将该框架应用于121名聆听连续语音的受试者的EEG数据时，其反转了原始相关性得出的结论，为解释神经追踪相关性提供了高效且有原则的方法论。
英文摘要：
Neural tracking - the time-locking of neural responses to continuous stimuli such as speech, music, and video - is widely used to study how the brain processes natural input. Tracking strength is typically quantified as the correlation between the recorded neural response and the stimulus, decoded and/or encoded through data-driven models, and this correlation is routinely used to compare stimulus features, models, or settings. However, its magnitude depends not only on how strongly the brain tracks the stimulus, but also on the statistical properties of the signals being correlated. For example, a smallband speech envelope carrying almost no information about speech content yields among the highest correlations, simply because it is easier to reconstruct. Meaningful interpretation therefore requires comparing each correlation to its null distribution: the correlations expected without any stimulus-response relationship. We show that the randomization procedures commonly used to construct this null distribution are not interchangeable: each implicitly encodes a different null hypothesis, and we motivate stimulus-response misalignment as the most practical and appropriate choice. Because reliable null distributions require many permutations, we introduce a semi-parametric model using the normal distribution after the Fisher transform that yields accurate significance levels from only 3-5 min of data and predicts them across analysis window lengths. Building on this, we propose the null-normalized tracking score, an interpretable measure placing features and models on a common scale, which relates directly to the widely used match-mismatch accuracy. Applied to EEG from 121 participants listening to continuous speech, the framework reverses conclusions drawn from raw correlations, providing an efficient and principled methodology for interpreting neural tracking correlations.
