Epilepsy Unit, IRCCS E. Medea Scientific Institute, Treviso, Italy.
Department of General Psychology, University of Padova, Padova, Italy.
Epilepsia. 2023 May;64(5):1278-1288. doi: 10.1111/epi.17551. Epub 2023 Mar 2.
Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large-scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls.
We defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients.
We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey-Osterrieth Complex Figure Test).
Our results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.
大尺度无规则激活爆发，即神经元爆发，已被用于描述全脑活动，因为它们的存在通常与最佳动力学相关。癫痫的特征是大脑网络动力学的大范围改变。在这里，我们利用神经元爆发来描述颞叶癫痫（TLE）患者和匹配对照组之间脑电图（EEG）基础活动的差异，这些活动不受癫痫发作和/或发作间期棘波的影响。
我们将神经元爆发定义为当源重建的 EEG 信号在任何区域的 z 得分超过特定阈值时开始，并在所有区域恢复到基线时结束。该技术避免了数据处理或信号平稳性的假设，专注于信号的非周期性、无标度成分。我们计算了个体爆发转移矩阵，以跟踪任何两个区域之间爆发传播的概率，将其与患者和对照组进行比较，并与患者的记忆表现相关联。
我们观察到在与 TLE 相关的功能和结构上重要的区域中，存在显著边缘聚类的稳健拓扑，例如内嗅皮层、下顶叶和梭状回、下颞叶和前扣带回皮层。我们检测到过渡矩阵中内嗅皮层的中心性与长期记忆表现（延迟回忆 Rey-Osterrieth 复杂图形测试）之间存在显著相关性。
我们的结果表明，在静息状态下，TLE 中大规模神经元爆发的传播模式发生了改变，这表明在癫痫诊断中有潜在的应用。此外，传播模式与记忆表现之间的关系支持了神经元爆发的神经生理学相关性。
