Characterizing the neural dynamics underlying sensory processing is one of the central areas of investigation in systems and cognitive neuroscience. Neuroimaging techniques such as magnetoencephalography (MEG) and Electroencephalography (EEG) have provided significant insights into the neural processing of continuous stimuli, such as speech, thanks to their high temporal resolution. Existing work in the context of auditory processing suggests that certain features of speech, such as the acoustic envelope, can be used as reliable linear predictors of the neural response manifested in M/EEG. The corresponding linear filters are referred to as temporal response functions (TRFs). While the functional roles of specific components of the TRF are well-studied and linked to behavioral attributes such as attention, the cortical origins of the underlying neural processes are not as well understood. In this work, we address this issue by estimating a linear filter representation of cortical sources directly from neuroimaging data in the context of continuous speech processing. To this end, we introduce Neuro-Current Response Functions (NCRFs), a set of linear filters, spatially distributed throughout the cortex, that predict the cortical currents giving rise to the observed ongoing MEG (or EEG) data in response to continuous speech. NCRF estimation is cast within a Bayesian framework, which allows unification of the TRF and source estimation problems, and also facilitates the incorporation of prior information on the structural properties of the NCRFs. To generalize this analysis to M/EEG recordings which lack individual structural magnetic resonance (MR) scans, NCRFs are extended to free-orientation dipoles and a novel regularizing scheme is put forward to lessen reliance on fine-tuned coordinate co-registration. We present a fast estimation algorithm, which we refer to as the Champ-Lasso algorithm, by leveraging recent advances in optimization, and demonstrate its utility through application to simulated and experimentally recorded MEG data under auditory experiments. Our simulation studies reveal significant improvements over existing methods that typically operate in a two-stage fashion, in terms of spatial resolution, response function reconstruction, and recovering dipole orientations. The analysis of experimentally-recorded MEG data without MR scans corroborates existing findings, but also delineates the distinct cortical distribution of the underlying neural processes at high spatiotemporal resolution. In summary, we provide a principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.

中文翻译：

---

神经电流反应函数：连续刺激范式下 MEG 源分析的统一方法

表征感觉处理背后的神经动力学是系统和认知神经科学研究的核心领域之一。由于脑磁图 (MEG) 和脑电图 (EEG) 等神经成像技术具有较高的时间分辨率，因此对语音等连续刺激的神经处理提供了重要的见解。听觉处理方面的现有工作表明，语音的某些特征，例如声包络，可以用作 M/EEG 中表现出的神经反应的可靠线性预测器。相应的线性滤波器称为时间响应函数 (TRF)。虽然 TRF 的特定组成部分的功能作用已得到充分研究并与注意力等行为属性相关联，潜在神经过程的皮层起源还不是很清楚。在这项工作中，我们通过在连续语音处理的背景下直接从神经影像数据估计皮质源的线性滤波器表示来解决这个问题。为此，我们引入了神经电流响应函数 (NCRF)，这是一组线性滤波器，空间分布在整个皮层，可预测皮层电流，从而产生响应连续语音的观察到的正在进行的 MEG（或 EEG）数据。NCRF 估计是在贝叶斯框架内进行的，它允许统一 TRF 和源估计问题，并且还有助于合并关于 NCRF 结构特性的先验信息。为了将这种分析推广到缺乏单个结构磁共振 (MR) 扫描的 M/EEG 记录，NCRF 扩展到自由定向偶极子，并提出了一种新的正则化方案，以减少对微调坐标配准的依赖。我们提出了一种快速估计算法，我们将其称为 Champ-Lasso 算法，利用优化方面的最新进展，并通过在听觉实验下将模拟和实验记录的 MEG 数据应用于模拟和实验记录的 MEG 数据来证明其实用性。我们的模拟研究表明，在空间分辨率、响应函数重建和恢复偶极子方向方面，与通常以两阶段方式运行的现有方法相比，有显着改进。对没有 MR 扫描的实验记录的 MEG 数据的分析证实了现有的发现，但也以高时空分辨率描绘了潜在神经过程的独特皮层分布。总之，我们为 MEG 源分析提供了一个有原则的建模和估计范式，用于提取对连续刺激的电生理反应的皮层起源。