Mullen Tim, Acar Zeynep Akalin, Worrell Gregory, Makeig Scott
Department of Cognitive Science and Swartz Center for Computational Neuroscience, Institute for Neural Computation, UCSD, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1411-4. doi: 10.1109/IEMBS.2011.6090332.
Mapping the dynamics and spatial topography of brain source processes critically involved in initiating and propagating seizure activity is critical for effective epilepsy diagnosis, intervention, and treatment. In this report we analyze neuronal dynamics before and during epileptic seizures using adaptive multivariate autoregressive (VAR) models applied to maximally-independent (ICA) sources of intracranial EEG (iEEG, ECoG) data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. We visualize the spatial distribution of causal sources and sinks of ictal activity on the cortical surface (gyral and sulcal) using a novel combination of multivariate Granger-causal and graph-theoretic metrics combined with distributed source localization by Sparse Bayesian Learning applied to a multi-scale patch basis. This analysis reveals and visualizes distinct, seizure stage-dependent shifts in inter-component spatiotemporal dynamics and connectivity including the clinically-identified epileptic foci.
描绘在引发和传播癫痫活动中起关键作用的脑源过程的动力学和空间地形，对于有效的癫痫诊断、干预和治疗至关重要。在本报告中，我们使用自适应多元自回归（VAR）模型分析癫痫发作前和发作期间的神经元动力学，该模型应用于从植入人类患者硬膜下电极记录的颅内脑电图（iEEG，ECoG）数据的最大独立（ICA）源。我们使用多元格兰杰因果关系和图论指标的新颖组合，结合应用于多尺度斑块基础的稀疏贝叶斯学习进行分布式源定位，可视化皮质表面（脑回和脑沟）上发作活动的因果源和汇的空间分布。该分析揭示并可视化了组件间时空动力学和连通性中不同的、依赖于发作阶段的变化，包括临床确定的癫痫病灶。
