non-epileptic seizure; epileptic seizure; Epilepsy; Advanced Neuroimaging; EEG
Rebsamen Michael, Rummel Christian, Reyes Mauricio, Wiest Roland, McKinley Richard (2020), Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation, in Human Brain Mapping
, 41(17), 4804-4814.
Larivière Sara, Rodríguez-Cruces Raúl, Royer Jessica, Caligiuri Maria Eugenia, Gambardella Antonio, Concha Luis, Keller Simon S., Cendes Fernando, Yasuda Clarissa, Bonilha Leonardo, Gleichgerrcht Ezequiel, Focke Niels K., Domin Martin, von Podewills Felix, Langner Soenke, Rummel Christian, Wiest Roland, Martin Pascal, Kotikalapudi Raviteja, O’Brien Terence J., Sinclair Benjamin, Vivash Lucy, Desmond Patricia M., Alhusaini Saud, et al. (2020), Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study, in Science Advances
, 6(47), eabc6457-eabc6457.
Dobrocky T., Rebsamen M., Rummel C., Häni L., Mordasini P., Raabe A., Ulrich C.T., Gralla J., Piechowiak E.I., Beck J. (2020), Monro-Kellie Hypothesis: Increase of Ventricular CSF Volume after Surgical Closure of a Spinal Dural Leak in Patients with Spontaneous Intracranial Hypotension, in American Journal of Neuroradiology
, 41(11), 2055-2061.
De Stefano Pia, Carboni Margherita, Pugin Deborah, Seeck Margitta, Vulliémoz Serge (2020), Brain networks involved in generalized periodic discharges (GPD) in post-anoxic-ischemic encephalopathy, in Resuscitation
, 155, 143-151.
Jin Baudouin Zongxin, De Stefano Pia, Petroulia Valentina, Rummel Christian, Kiefer Claus, Reyes Mauricio, Schindler Kaspar, van Mierlo Pieter, Seeck Margitta, Wiest Roland (2020), Diagnosis of epilepsy after first seizure. Introducing the SWISS FIRST study, in Clinical and Translational Neuroscience
, 4(2), 2514183X20-2514183X20.
Sisodiya Sanjay M., Whelan Christopher D., Hatton Sean N., Huynh Khoa, Altmann Andre, Ryten Mina, Vezzani Annamaria, Caligiuri Maria Eugenia, Labate Angelo, Gambardella Antonio, Ives‐Deliperi Victoria, Meletti Stefano, Munsell Brent C., Bonilha Leonardo, Tondelli Manuela, Rebsamen Michael, Rummel Christian, Vaudano Anna Elisabetta, Wiest Roland, Balachandra Akshara R., Bargalló Núria, Bartolini Emanuele, Bernasconi Andrea, Bernasconi Neda, et al. (2020), The ENIGMA‐Epilepsy working group: Mapping disease from large data sets, in Human Brain Mapping
Rebsamen Michael, Suter Yannick, Wiest Roland, Reyes Mauricio, Rummel Christian (2020), Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning, in Frontiers in Neurology
, 11, 244.
WiestRoland (2019), Recent developments in imaging of epilepsy, in Curr Opin Neurol.
Diagnosis and management of patients who experience a first event with transient neurological deficit or loss of consciousness remains a challenging task for the consulting neurologist in the emergency department (ED). Between 10-15% of the population have a seizure or seizure-like event in their lifetime, which may remain isolated or marks the beginning of epilepsy. If the appropriate diagnosis is missed, outcome may be fatal (e.g. recurrent seizure with trauma, cardiogenic syncope, stroke) or costly (e.g. continuous antiepileptic treatment for psychogenic seizures). Thus, it would help tremendously to have access to hands-on tools, based on easily accessible techniques like EEG and magnetic resonance imaging (MRI), to determine the correct diagnosis and organize appropriate patient management.There is a considerable lack of techniques that support appropriate treatment decisions in the majority of patients admitted to ED or for a first questionable. EEG and MRI findings are frequently negative, or - in case of MRI - if lesions are present, they are not necessarily critical (i.e. incidental). In this proposal, we aim to prospectively collect EEG and neuroimaging data from different centers in Switzerland to investigate the prevalence of EEG- and structural MRI-abnormalities in a population-wide cohort. Since epilepsy is considered a structural and functional network disorder, it appears straightforward to investigate if aberrant structural and functional network architecture allows identifying patients with unprovoked epileptic seizures and if such alterations predispose to the development of epilepsy during follow-up. We make use of recent advances in EEG and MR signal analysis to investigate pre-disposing abnormalities that reflect preexisting epileptogenesis and/or an increased risk for seizure recurrence. We use the EEG microstates values to determine the presence of pathological brain states as potential marker of epilepsy, and combine these measures with morphometric MR and functional connectivity analysis to construct covariance networks that are the starting point for machine learning algorithms. We will also investigate the diagnostic accuracy of widespread available advanced neuroimaging techniques (DWI, perfusion imaging, SWI) to segregate epileptic and non-epileptic patients during a first admission to the ED. We further aim to introduce a new MRI technology based on measurements of local perturbations of the MR signal induced by weak electromagnetic fields that accompany neuronal signaling (phase-cycled stimulus-induced rotary saturation; pc-SIRS).Preliminary studies suggest very good performance of the outlined methods in differentiating epileptogenic from non-epileptogenic activities in patients with chronic epilepsy. With the present grant, we aim to extent these methods to patients admitted at the ED and determine their yield in differentiating early-onset epilepsy versus clinically similar events of other origins. We hypothesize that in case an event is a symptom of an underlying epilepsy, EEG microstates, EEG and MRI functional connectivity, DWI, SWI and pc-SIRS are significantly different from those parameters identified in normal subjects, patients with non-epileptic events and patients with acute symptomatic seizures (e.g. withdrawal seizures). We hope to identify methods or sets of methods with the help of deep learning methods that allow rapid and correct diagnosis of unclear events, and in a further step, analyze the presence of structural and functional abnormalities in neuroimaging data that identify the risk of recurrent seizures.By this, we aim to continue our effort on the investigation of abnormal organization of the epileptic brain that was initiated by the SPUM consortium “imaging large scale networks in epilepsy” in 2009 by the teams from Geneva and Bern, extending now to early onset epilepsy. We now extend our consortium to the Bern Biomedical Engineering (BBME) and the group of the Medical Image and Signal Processing group from Ghent, Belgium. Each of the applicants has a field of renowned expertise in the field of epilepsy, complementary with respect to the goals of the project which aims at improving the care of patients with unclear loss of consciousness.