Dalal Sarang S, Guggisberg Adrian G, Edwards Erik, Sekihara Kensuke, Findlay Anne M, Canolty Ryan T, Knight Robert T, Barbaro Nicholas M, Kirsch Heidi E, Nagarajan Srikantan S
Mental Processes and Brain Activation Lab, INSERM U821, 69675 Bron, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:4941-4. doi: 10.1109/IEMBS.2007.4353449.
The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. We present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and a high gamma band (65-90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. We also revealed observed high gamma activity in the cerebellum. The proposed algorithm is highly parallelizable and runs efficiently on modern high performance computing clusters. This method enables non-invasive five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics.
由于缺乏从无创电生理数据重建此类活动的充分验证方法，人类大脑区域皮质振荡的时空动态仍知之甚少。我们提出了一种新颖的自适应空间滤波算法，该算法针对从脑磁图（MEG）和脑电图（EEG）数据进行稳健的源时频重建进行了优化。该方法的有效性通过来自自定节奏手指运动任务的真实MEG数据得到了证明。该算法可靠地揭示了感觉运动皮层中β波段（12 - 30赫兹）和高γ波段（65 - 90赫兹）的调制。通过跨受试者统计比较和来自两名癫痫患者的颅内脑电描记术（ECoG）数据验证了该性能。我们还在小脑中发现了观察到的高γ活动。所提出的算法具有高度可并行性，并且在现代高性能计算集群上运行高效。该方法能够对大脑中的空间、时间和频率活动进行无创五维成像，并使其适用于广泛的人类皮质动力学研究。
