Investigating Epileptic and Functional Networks in Patients with Drug Resistant Focal Epilepsy

National Institute Of Neurological Disorders And Stroke

Investigators

Linked publications, trials & patents

Abstract

During the past year, we have continued to make significant progress on this project. Despite the ongoing challenges of COVID-related restrictions on our activity, we have continued to collect neuroimaging and neurophysiologic data in patients undergoing clinical pre-surgical evaluations, most notably functional and structural neuroimaging, magnetoencephalography, neuropsychological testing, and epilepsy surgery. Prior to this year, much of the work of the lab was directed toward a project developing a novel machine learning method to aid in the identification of subtle epileptogenic lesions using structural magnetic resonance imaging (MRI). Identification of epileptogenic lesions has been shown to significantly improve seizure outcomes in patients undergoing epilepsy surgery, and subtle FCD lesions can be difficult to identify on visual inspection. We published a manuscript describing our approach in 2021 (Snyder et al.), and over the past year we developed a reproducible Nipype processing pipeline and made it freely available on Github. We presented this pipeline in a poster at the Organization for Human Brain Mapping annual meeting, and have continued to discuss implementing it with collaborators in several other centers. In a related project, we used the same set of image filters to develop a template-free brain tissue segmentation algorithm. Although there are numerous publicly available brain tissue segmentation procedures designed for use in structurally normal brains, many of these approaches fail to accurately segment brains with significant structural abnormalities. Accurate brain segmentation is important in our research because it serves as the basis for neurophysiologic source localization algorithms and for semi-automated creation of brain resection masks. In our patients, particularly in those with large regions of encephalomalacia, we have found that our approach appears to be more accurate than several commonly used software packages. A manuscript is in preparation describing this approach and its accuracy for brain tissue classification as well as for semi-automated creation of brain resection masks (Hayday E, Hussein RK, Abdollahi S, Inati SJ, Inati SK). While intermittent seizures are the most obvious manifestation of epilepsy, interictal functional disturbances are also well described, particularly in the region of seizure onsets. This has been observed in the form of interictal epileptiform activity (IEA) using electroencephalography (EEG) and magnetoencephalography (MEG), hypometabolism using fluorodeoxyglucose positron emission tomography (FDG-PET) imaging, and at times changes in perfusion and diffusion imaging using MRI. Therefore, in our cohort of patients we have been prospectively collecting arterial spin labeling (ASL) MRI in collaboration with Lalith Talagala in the NINDS NMR Center Core Facility. ASL MRI is a widely available, safe, noninvasive imaging modality used to study cerebral blood flow and perfusion. In our cohort of patients with temporal lobe epilepsy (TLE), we found that those with lesional epilepsy on MRI (almost all medial temporal sclerosis), show significant hypoperfusion in the medial temporal lobe, particularly in the parahippocampal gyrus, as well as the lateral temporal lobe, primarily driven by asymmetries in the anterior temporal neocortex, compared to both healthy volunteers and patients with non-lesional TLE. Although non-lesional TLE patients demonstrate similar degrees of ipsilateral brain perfusion to lesional TLE patients, they have similar asymmetry indices to healthy volunteers, making abnormalities more difficult to detect in this patient group. This appears to be due to normal or somewhat decreased perfusion contralateral to the seizure onset in non-lesional TLE patients, as well as relative hyperperfusion contralateral to seizure onsets in lesional TLE patients. Our manuscript describing these findings is currently under review (Rentzeperis R, Abdennadher M, Dembny K, Snyder K, Talagala L, Theodore WH, Zaghloul KA, Inati SK). The second goal of this project is related to exploring the relationship between propagation of epileptiform activity and underlying structural and functional networks in individual patients with epilepsy. Through our close ongoing collaboration with Kareem Zaghloul's lab in the NINDS Surgical Neurology Branch, we continue to be interested in better understanding how epileptiform activity spreads within the brain, and how these patterns of spread can be used to identify the source of this activity. In Diamond et al. 2021, we described a novel method to identify local sources of epileptiform activity during seizures using phase differences observed across electrodes during seizures. We have continued this collaboration with a manuscript currently submitted describing similarities in sequences of epileptiform activity over time, and during interictal and ictal activity. These methods describe propagation of epileptiform activity over the gray matter cortical surface. In our lab, we have used diffusion tensor imaging in our cohort of patients to incorporate additional likely pathways of white matter propagation, allowing for identification of more distant potential sources of epileptiform activity. We have submitted an abstract to the American Epilepsy Society annual meeting describing these findings, and are currently preparing a manuscript as well. Over the coming year, we also plan to make use of our extensive database of magnetoencephalography (MEG) recordings in our patient cohort, intending to adapt these source localization concepts to this non-invasive modality. MEG allows for more widespread coverage across the cortical surface and should provide complementary data to that obtained using intracranial recordings. We also plan to apply additional commonly used source localization algorithms for comparison purposes in this patient population. Finally, as part of our ongoing epilepsy imaging study, we have continued to prospectively obtain pre- and post-operative language and resting state functional MRI (fMRI) data. Several of our close collaborators have made use this data over the past year to study both language and memory. Leigh Sepeta, collaborator from Children's National Medical Center, is investigating the use of language fMRI activations to predict memory outcomes following temporal lobe epilepsy surgery, and is implementing a novel memory task to further identify potential asymmetries in memory-related activation in the mesial temporal regions. We have begun and continued several other collaborations involving both fMRI and DTI data in our patient cohort with groups within and outside of the NIH, some of which has been presented in poster formats, and hope that these will be productive and described in next year's annual report.