Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Commun Biol. 2021 Jan 29;4(1):136. doi: 10.1038/s42003-021-01670-9.
Neurological disorders such as epilepsy arise from disrupted brain networks. Our capacity to treat these disorders is limited by our inability to map these networks at sufficient temporal and spatial scales to target interventions. Current best techniques either sample broad areas at low temporal resolution (e.g. calcium imaging) or record from discrete regions at high temporal resolution (e.g. electrophysiology). This limitation hampers our ability to understand and intervene in aberrations of network dynamics. Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays. We integrate dynamic data features from both modalities using non-negative matrix factorization to identify sequential spatiotemporal patterns of seizure onset and evolution, revealing how the temporal progression of ictal electrophysiology is linked to the spatial evolution of the recruited seizure core. This integrated analysis of multimodal data reveals otherwise hidden state transitions in the spatial and temporal progression of acute seizures. The techniques demonstrated here may enable future targeted therapeutic interventions and novel spatially embedded models of local circuit dynamics during seizure onset and evolution.
神经紊乱,如癫痫,源于脑网络的紊乱。我们治疗这些疾病的能力受到限制,因为我们无法在足够的时间和空间尺度上绘制这些网络,以进行干预。目前最好的技术要么以低时间分辨率采样广泛的区域(例如钙成像),要么以高时间分辨率记录离散区域(例如电生理学)。这一限制阻碍了我们理解和干预网络动态失常的能力。在这里,我们提出了一种技术,通过同时使用透明石墨烯微电极阵列记录高带宽微电生理记录和钙荧光,来绘制体内急性癫痫发作的起始和时空传播。我们使用非负矩阵分解整合来自两种模式的动态数据特征,以识别发作起始和演变的顺序时空模式,揭示癫痫电生理的时间进展如何与募集的发作核心的空间演变相关联。这种多模态数据的综合分析揭示了急性发作时空进展中隐藏的状态转变。这里展示的技术可能为未来的靶向治疗干预和发作起始和演变期间局部回路动力学的新型空间嵌入模型提供可能。