Abstract：
OBJECTIVE: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.
METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.
RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.
CONCLUSIONS: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.
METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.
RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.
CONCLUSIONS: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.
摘要：
目的：探讨高频振荡（HFO）和长程时间相关性（LRTC）在癫痫术前评估中的实用性。
方法：对59例耐药癫痫患者进行MEG波纹检测，包括5例患有顶叶癫痫（PLE），21患有额叶癫痫（FLE），14例颞叶外侧癫痫（LTLE），和19伴有颞叶内侧癫痫（MTLE），以确定癫痫发生区（EZ）。将结果与临床MEG报告和切除面积进行比较。随后，通过去趋势波动分析（DFA）和90个大脑皮层区域的5个条带的生活/等待时间，在源水平对LRTC进行了定量。将具有较大DFA指数和标准化的生命等待生物标志物的大脑区域与切除结果进行比较。
结果：与MEG传感器级数据相比，波纹源更频繁地位于切除区域内。此外，来源水平分析显示，DFA指数和等待生命的生物标志物的比例较高，排名相对较高，主要分布在切除区域内(p<0.01)。此外，这两个LRCT指数在五个不同的频带与EZ相关。
结论：HFO和来源水平LRTC与EZ相关。整合HFO和LRTC可能是术前评估癫痫的有效方法。
方法：对59例耐药癫痫患者进行MEG波纹检测，包括5例患有顶叶癫痫（PLE），21患有额叶癫痫（FLE），14例颞叶外侧癫痫（LTLE），和19伴有颞叶内侧癫痫（MTLE），以确定癫痫发生区（EZ）。将结果与临床MEG报告和切除面积进行比较。随后，通过去趋势波动分析（DFA）和90个大脑皮层区域的5个条带的生活/等待时间，在源水平对LRTC进行了定量。将具有较大DFA指数和标准化的生命等待生物标志物的大脑区域与切除结果进行比较。
结果：与MEG传感器级数据相比，波纹源更频繁地位于切除区域内。此外，来源水平分析显示，DFA指数和等待生命的生物标志物的比例较高，排名相对较高，主要分布在切除区域内(p<0.01)。此外，这两个LRCT指数在五个不同的频带与EZ相关。
结论：HFO和来源水平LRTC与EZ相关。整合HFO和LRTC可能是术前评估癫痫的有效方法。
