Multiscale Dynamics of the Frontotemporal Connectome in Refractory Epilepsy

摘要 癫痫神经科学中的一个基本知识缺口涉及癫痫发作的不同倾向， 24小时昼夜节律周期。例如，目前还不知道为什么一些额叶癫痫患者可能只会抓住 在睡觉的时候了解癫痫网络在昼夜节律时间尺度上的动力学对于 改善所有治疗失败的癫痫患者的大部分治疗前景。电流 癫痫的网络结构基于结构MRI或静息态（RS）fMRI。这些模式 在固定的时间尺度上揭示单个实验时间点，并且不解决时空动态 缉获网络的性质。在慢性颅内记录中癫痫发作周期性的报告并没有对 整个癫痫网络，只记录癫痫发作的发生，而不是其致病网络的改变。我们 长期目标是了解癫痫的网络动力学，以推进治疗。我们的目标是， 脑内立体脑电图（SEEG）信号，是建立一个动态的神经生理 基于SEEG的昼夜节律周期中额颞脑区域的连接体。我们的核心假设 癫痫网络的拓扑结构具有特定的昼夜节律依赖性，这种依赖性可以 在更长的时间尺度上进行调节，包括通过抗惊厥药物。我们这个项目的理由是， 癫痫网络的网络通路、带宽和昼夜节律状态依赖性的知识将 激发新的癫痫神经调节方法（针对特定频带中的大脑区域， 24小时循环）。这种见解也可能推动新的网络启发消融手术方法。我们将 追求两个具体目标：（i）确定基于SEEG的额颞叶皮层连接组学， 昼夜警觉状态;（ii）确定癫痫网络动态的亚昼夜特征， 额颞叶皮层在我们的临床设施中，使用来自患者的连续多日SEEG记录， 我们会同时致力达致这些目标。我们将按照患者的警戒状态组织数据，并使用分析 工具部署在以前的工作中，我们将描述癫痫样额颞叶皮层网络， 在多个时间尺度上的相互作用，并参考24小时和亚昼夜周期。我们将确定关键 网络漏洞锁定昼夜节律周期，并验证我们的结果与发作发作的比较 区域和度量的空间分布，例如致痫性指数。我们的建议是创新的， 因为我们将超越基于成像的连接组学的静态性质， 大脑网络结构的描述。我们的贡献将是巨大的，通过帮助解决一个科学问题， 在癫痫神经科学的谜语，同时提出了潜在的新的治疗难治性癫痫。更 一般来说，我们的工作将为“构建大脑地图”、“观察大脑的活动”和“促进人类发展”提供信息 神经科学的优先领域的NIH脑倡议。

ABSTRACT A fundamental knowledge gap in epilepsy neuroscience concerns the varying propensity for seizures over the 24h circadian cycle. For example, it is not known why some patients with frontal lobe epilepsy may only seize while asleep. Understanding the dynamics of epilepsy networks on circadian time scales is essential for improving therapeutic prospects of the substantial fraction of epilepsy patients who fail all treatments. Current network architectures of epilepsy are based on structural MRI or resting state (rs) fMRI. These modalities reveal single experimental time points at fixed time scales and do not address the spatiotemporally dynamic nature of seizure networks. Reports of seizure periodicity in chronic intracranial recordings do not sample the whole epileptic network and only document seizure occurrence, not their causative network alterations. Our long-term goal is to understand network dynamics in epilepsy to advance therapies. Our objective here, using the intracerebral stereo-electroencephalographic (SEEG) signal, is to build a dynamic neurophysiological SEEG-based connectome of the frontotemporal brain regions over the circadian cycle. Our central hypothesis is that the topology of epileptic networks has specific circadian dependence, and that such dependence can be modulated on longer time scales, including by anticonvulsant drugs. Our rationale for this project is that knowledge of the network pathways, bandwidths and circadian state-dependence of epileptic networks will inspire new neuromodulatory approaches to epilepsy (targeting brain regions in specific frequency bands and their 24h cycles). Such insight may also drive new network-inspired ablative surgical approaches. We will pursue two specific aims: (i) determine the SEEG-based connectomics of frontotemporal cortex across circadian vigilance states; and (ii) Identify the infradian characteristics of epilepsy network dynamics in frontotemporal cortex. Working with continuous multi-day SEEG recordings from patients at our clinical facility, we will pursue these aims in parallel. We will organize the data by patient vigilance state, and using analytic tools deployed in prior work, we will describe epileptiform frontotemporal cortical networks, and their interaction at multiple time scales and with reference to the 24h and infradian cycles. We will identify key network vulnerabilities locked to the circadian cycle and validate our results with comparisons with ictal onset areas and the spatial distribution of metrics such as epileptogenicity index. Our proposal is innovative, because we will move beyond the static nature of imaging-based connectomics to add the dimension of time to descriptions of brain network architecture. Our contribution will be significant, by helping solve a scientific riddle in epilepsy neuroscience while suggesting potential new treatments for refractory epilepsy. More generally, our work will inform the ‘building brain maps’, ‘observing the brain in action’, and ‘advancing human neuroscience’ priority areas of the NIH BRAIN initiative.