Multiscale Dynamics of the Frontotemporal Connectome in Refractory Epilepsy

摘要

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.