癫痫是一种常见的严重的脑科疾病，其涉及到分散在大脑三维空间的相关神经网络。光声成像可以通过对脑部血氧变化的精确监测来进行相关神经活动的诊断，在神经网络分析方面有广阔的应用前景。申请人在光声成像的癫痫应用上做了大量的开创性工作。申请人用实时光声层析成像的方法进行了癫痫过程中脑部血氧变化的监测，并用格兰杰因果分析算法观察到了大脑不同区域之间和癫痫相关的强烈的相互作用。在此基础上，本项目进一步提出用三维实时光声显微成像的方法，来解决先前光声层析成像中由于不同像素间的串扰，而造成的癫痫网络分析的时间和空间分辨率受限的问题。本项目还将采用多波长的方法来得到完整的血氧变化，采用更多的样本数，并首次用GPU并行化的格兰杰因果分析算法对整个成像区域进行分析，来得到癫痫过程中相关神经网络的动态变化的。本项目将促进癫痫发病和传播机理的相关研究，并进一步推动光声成像在癫痫研究中的应用。

Epilepsy is a most common serious brain disorder, which involves epileptic networks in the three-dimensional (3D) space of the brain. Photoacoustic imaging offers a comprehensive monitoring of the cerebral hemodynamics as surrogates of the neural activities, and is a promising tool for neural network analysis. The applicant has done a lot of pioneering work in the photoacoustic application of epilepsy. He monitored the cerebral hemodynamics during epileptic seizures with real-time photoacoustic tomography, and observed strong epileptic related directinal influences among different brain regions in the following data analysis through the Granger causality method. On the basis of this, this project further proposed to utilize the method of real-time 3D photoacoustic microscopy imaging for the analysis of epileptic networks, to overcome the limited spatiotemporal resolution due to the crosstalk between pixels in photoacoustic tomography. A multiple wavelength method will be applied to provide more complete hemodynamic information, more rat experiments will be done, and a Graphic Processing Unit (GPU) parellel scheme assisted Granger causality algorithm will be employed to calculate the directional influences among different pixels in the image domain, in order to obtain the epileptic network dynamics during the seizure onsets. This project can boost the pathological study of epileptic seizure onset and propagation, and further promote the application of photoacoustic imaging in the study of epilepsy.