Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.
Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom.
PLoS One. 2024 Aug 26;19(8):e0309243. doi: 10.1371/journal.pone.0309243. eCollection 2024.
Epilepsy is one of the most common neurological disorders in children. Diagnosing epilepsy in children can be very challenging, especially as it often coexists with neurodevelopmental conditions like autism and ADHD. Functional brain networks obtained from neuroimaging and electrophysiological data in wakefulness and sleep have been shown to contain signatures of neurological disorders, and can potentially support the diagnosis and management of co-occurring neurodevelopmental conditions. In this work, we use electroencephalography (EEG) recordings from children, in restful wakefulness and sleep, to extract functional connectivity networks in different frequency bands. We explore the relationship of these networks with epilepsy diagnosis and with measures of neurodevelopmental traits, obtained from questionnaires used as screening tools for autism and ADHD. We explore differences in network markers between children with and without epilepsy in wake and sleep, and quantify the correlation between such markers and measures of neurodevelopmental traits. Our findings highlight the importance of considering the interplay between epilepsy and neurodevelopmental traits when exploring network markers of epilepsy.
癫痫是儿童中最常见的神经障碍之一。在儿童中诊断癫痫可能非常具有挑战性,特别是因为它经常与自闭症和 ADHD 等神经发育状况并存。从神经影像学和脑电图数据中获得的功能大脑网络在清醒和睡眠中都显示出神经障碍的特征,并且可能支持共存神经发育状况的诊断和管理。在这项工作中,我们使用来自儿童的脑电图 (EEG) 记录,在休息的清醒和睡眠中,提取不同频带的功能连接网络。我们探讨了这些网络与癫痫诊断以及从自闭症和 ADHD 筛查工具中获得的神经发育特征测量值之间的关系。我们探索了清醒和睡眠时癫痫儿童和非癫痫儿童的网络标记之间的差异,并量化了这些标记与神经发育特征测量值之间的相关性。我们的研究结果强调了在探索癫痫的网络标记时考虑癫痫与神经发育特征之间相互作用的重要性。