CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
Comput Methods Programs Biomed. 2023 Apr;232:107427. doi: 10.1016/j.cmpb.2023.107427. Epub 2023 Feb 24.
Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of brain connectivity using artificial intelligence and network analysis techniques since their study requires large amounts of data over large spatial and temporal scales. For example, to discriminate states that would otherwise be indistinguishable from the human eye. This paper aims to identify the different brain states that appear concerning the intriguing seizure type of epileptic spasms. Once these states have been differentiated, an attempt is made to understand their corresponding brain activity.
The representation of brain connectivity can be done by graphing the topology and intensity of brain activations. Graph images from different instants within and outside the actual seizure are used as input to a deep learning model for classification purposes. This work uses convolutional neural networks to discriminate the different states of the epileptic brain based on the appearance of these graphs at different times. Next, we apply several graph metrics as an aid to interpret what happens in the brain regions during and around the seizure.
Results show that the model consistently finds distinctive brain states in children with epilepsy with focal onset epileptic spasms that are indistinguishable under the expert visual inspection of EEG traces. Furthermore, differences are found in brain connectivity and network measures in each of the different states.
Computer-assisted discrimination using this model can detect subtle differences in the various brain states of children with epileptic spasms. The research reveals previously undisclosed information regarding brain connectivity and networks, allowing for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. From our data, we speculate that the prefrontal, premotor, and motor cortices could be more involved in a hypersynchronized state occurring in the few seconds immediately preceding the visually evident EEG and clinical ictal features of the first spasm in a cluster. On the other hand, a disconnection in centro-parietal areas seems a relevant feature in the predisposition and repetitive generation of epileptic spasms within clusters.
癫痫是一种由神经元异常放电引起的脑部疾病，导致癫痫发作。这些电信号的性质和空间分布使得癫痫成为使用人工智能和网络分析技术研究脑连接的领域，因为这些研究需要大量的数据，且数据具有较大的空间和时间尺度。例如，要区分那些仅凭肉眼无法区分的状态。本文旨在识别与癫痫痉挛这种引人入胜的癫痫发作类型有关的不同脑状态。一旦这些状态被区分出来，就试图理解它们对应的大脑活动。
脑连接的表示可以通过绘制脑激活的拓扑结构和强度来实现。将发作内和发作外不同时刻的脑图像用作深度学习模型的输入，以进行分类目的。这项工作使用卷积神经网络来根据这些图在不同时间的出现来区分癫痫大脑的不同状态。接下来，我们应用几种图指标来辅助解释在癫痫发作期间和周围的脑区中发生的情况。
结果表明，该模型能够一致地发现局灶性癫痫发作性癫痫痉挛患儿的独特脑状态，这些状态在 EEG 轨迹的专家视觉检查下是无法区分的。此外，在每个不同状态的脑连接和网络测量中都发现了差异。
使用这种模型的计算机辅助区分可以检测到患有癫痫性痉挛的儿童在各种脑状态之间的细微差异。该研究揭示了有关脑连接和网络的以前未公开的信息，从而更好地理解这种特定发作类型的病理生理学和演变特征。根据我们的数据，我们推测在前额、运动前和运动皮质中可能存在更强烈的同步状态，这种状态发生在簇状第一个痉挛的视觉上明显的 EEG 和临床发作特征之前的几秒钟内。另一方面，在中枢-顶叶区域中存在的断开似乎是簇状中癫痫痉挛的易发性和重复性发生的一个相关特征。
