Cai Zhengxiang, Jiang Xiyuan, Bagić Anto, Worrell Gregory A, Richardson Mark, He Bin
bioRxiv. 2024 May 5:2024.05.02.592202. doi: 10.1101/2024.05.02.592202.
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy.
Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
癫痫是一种影响全球数百万人的神经系统疾病,在精确界定癫痫发作起源区(即引发癫痫发作的脑区)以进行有效治疗方面面临巨大挑战。高频振荡(HFOs)正成为有前景的生物标志物;然而,在单电极和单患者分辨率下区分病理性HFOs与非癫痫样活动以及理解它们在癫痫网络中的动态作用存在困难,这阻碍了其临床应用。在此,我们引入一种HFO测序方法来分析40例耐药性癫痫患者皮质区域的自发性HFOs。这种数据驱动方法自动检测到超过890万个HFOs,确定了病理性HFO网络,并揭示了长时间颅内脑电图记录中HFOs复杂的毫秒级时空动态、稳定性和功能连接性。与传统基准相比,这些HFO序列在癫痫组织定位方面有显著改善,与癫痫发作起始区的一致性提高了818.47%(平均误差:2.92毫米)。它们还使用广义线性模型根据术前信息对90%的受试者工作特征曲线下面积准确预测癫痫发作结果。重要的是,即使记录时间较短(30分钟片段的平均标准差:3.23毫米),这种映射仍然可靠。此外,HFO序列表现出独特但高度重复的时空模式,其特征是明显的同步性和从周边向参与传播的区域的主要内向信息流,表明兴奋 - 抑制平衡在HFO起始和进展中起关键作用。总之,这些发现揭示了癫痫网络的复杂组织,并突出了HFO测序作为一种转化工具在改善诊断、手术靶点定位以及最终为耐药性癫痫患者带来更好治疗结果方面的潜力。
病理性快速脑振荡像交通一样沿着不同路径传播,勾勒出反复访问的神经位点,这些位点成为癫痫网络中的关键热点。