OBJECTIVE: Identification of patient-specific epileptogenic networks is critical to designing successful treatment strategies. Multiple noninvasive methods have been used to characterize epileptogenic networks. However, these methods lack the spatiotemporal resolution to allow precise localization of epileptiform activity. We used intracranial recordings, at much higher spatiotemporal resolution, across a cohort of patients with mesial temporal lobe epilepsy (MTLE) to delineate features common to their epileptogenic networks. We used interictal rather than seizure data because interictal spikes occur more frequently, providing us greater power for analyzing variances in the network. METHODS: Intracranial recordings from 10 medically refractory MTLE patients were analyzed. In each patient, hour-long recordings were selected for having frequent interictal discharges and no ictal events. For all possible pairs of electrodes, conditional probability of the occurrence of interictal spikes within a 150-millisecond bin was computed. These probabilities were used to construct a weighted graph between all electrodes, and the node degree was estimated. To assess the relationship of the highly connected regions in this network to the clinically identified seizure network, logistic regression was used to model the regions that were surgically resected using weighted node degree and number of spikes in each channel as factors. Lastly, the conditional spike probability was normalized and averaged across patients to visualize the MTLE network at group level. RESULTS: We generated the first graph of connectivity across a cohort of MTLE patients using interictal activity. The most consistent connections were hippocampus to amygdala, anterior fusiform cortex to hippocampus, and parahippocampal gyrus projections to amygdala. Additionally, the weighted node degree and number of spikes modeled the brain regions identified as seizure networks by clinicians. SIGNIFICANCE: Apart from identifying interictal measures that can model patient-specific epileptogenic networks, we also produce a group map of network connectivity from a cohort of MTLE patients. Wiley Periodicals, Inc.
OBJECTIVE: Identification of patient-specific epileptogenic networks is critical to designing successful treatment strategies. Multiple noninvasive methods have been used to characterize epileptogenic networks. However, these methods lack the spatiotemporal resolution to allow precise localization of epileptiform activity. We used intracranial recordings, at much higher spatiotemporal resolution, across a cohort of patients with mesial temporal lobe epilepsy (MTLE) to delineate features common to their epileptogenic networks. We used interictal rather than seizure data because interictal spikes occur more frequently, providing us greater power for analyzing variances in the network. METHODS: Intracranial recordings from 10 medically refractory MTLE patients were analyzed. In each patient, hour-long recordings were selected for having frequent interictal discharges and no ictal events. For all possible pairs of electrodes, conditional probability of the occurrence of interictal spikes within a 150-millisecond bin was computed. These probabilities were used to construct a weighted graph between all electrodes, and the node degree was estimated. To assess the relationship of the highly connected regions in this network to the clinically identified seizure network, logistic regression was used to model the regions that were surgically resected using weighted node degree and number of spikes in each channel as factors. Lastly, the conditional spike probability was normalized and averaged across patients to visualize the MTLE network at group level. RESULTS: We generated the first graph of connectivity across a cohort of MTLE patients using interictal activity. The most consistent connections were hippocampus to amygdala, anterior fusiform cortex to hippocampus, and parahippocampal gyrus projections to amygdala. Additionally, the weighted node degree and number of spikes modeled the brain regions identified as seizure networks by clinicians. SIGNIFICANCE: Apart from identifying interictal measures that can model patient-specific epileptogenic networks, we also produce a group map of network connectivity from a cohort of MTLE patients. Wiley Periodicals, Inc.
Authors: Sarah E Goodale; Hernán F J González; Graham W Johnson; Kanupriya Gupta; William J Rodriguez; Robert Shults; Baxter P Rogers; John D Rolston; Benoit M Dawant; Victoria L Morgan; Dario J Englot Journal: Neurosurgery Date: 2020-06-01 Impact factor: 4.654
Authors: Shasha Wu; Naoum P Issa; Maureen Lacy; David Satzer; Sandra L Rose; Carina W Yang; John M Collins; Xi Liu; Taixin Sun; Vernon L Towle; Douglas R Nordli; Peter C Warnke; James X Tao Journal: Front Neurol Date: 2021-05-17 Impact factor: 4.003