Magneto-encephalography (MEG) is an imaging technique which measures neuronal activity in the brain. Even when a subject is in a resting state, MEG data show characteristic spatial and temporal patterns, resulting from electrical current at specific locations in the brain. The key pattern of interest is a 'dipole', consisting of two adjacent regions of high and low activation which oscillate over time in an out-of-phase manner. Standard approaches are based on averages over large numbers of trials in order to reduce noise. In contrast, this article addresses the issue of dipole modelling for single trial data, as this is of interest in application areas. There is also clear evidence that the frequency of this oscillation in single trials generally changes over time and so exhibits quasi-periodic rather than periodic behaviour. A framework for the modelling of dipoles is proposed through estimation of a spatiotemporal smooth function constructed as a parametric function of space and a smooth function of time. Quasi-periodic behaviour is expressed in phase functions which are allowed to evolve smoothly over time. The model is fitted in two stages. First, the spatial location of the dipole is identified and the smooth signals characterizing the amplitude functions for each separate pole are estimated. Second, the phase and frequency of the amplitude signals are estimated as smooth functions. The model is applied to data from a real MEG experiment focusing on motor and visual brain processes. In contrast to existing standard approaches, the model allows the variability across trials and subjects to be identified. The nature of this variability is informative about the resting state of the brain.

中文翻译：

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单次脑磁图实验中脑激活的准周期时空模型。


脑磁图（MEG）是一种测量大脑神经元活动的成像技术。即使受试者处于休息状态，脑磁图数据也会显示出由大脑特定位置的电流产生的特征空间和时间模式。感兴趣的关键模式是“偶极子”，由两个相邻的高激活和低激活区域组成，它们随时间以异相方式振荡。标准方法基于大量试验的平均值，以减少噪音。相比之下，本文讨论了单个试验数据的偶极子建模问题，因为这在应用领域很有趣。还有明确的证据表明，单次试验中这种振荡的频率通常会随着时间的推移而变化，因此表现出准周期性而不是周期性行为。通过估计构造为空间参数函数和时间平滑函数的时空平滑函数，提出了偶极子建模框架。准周期行为用相位函数表示，相位函数可以随时间平稳演化。该模型分两个阶段进行拟合。首先，识别偶极子的空间位置，并估计表征每个单独极点的幅度函数的平滑信号。其次，将幅度信号的相位和频率估计为平滑函数。该模型应用于来自真实 MEG 实验的数据，该实验重点关注运动和视觉大脑过程。与现有的标准方法相比，该模型可以识别试验和受试者之间的变异性。这种变异性的本质可以提供有关大脑静息状态的信息。