Network-Imaging in Genetic Epilepsies

• Applicant: Professor Dr. Niels Focke
• Subject Area: Clinical Neurology; Neurosurgery and Neuroradiology
• Term: from 2016 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 320459628

Approximately 30% of epilepsies have a suspected or proven primarily genetic etiology. In rare, so called monogenic epilepsies a causal mutation can be identified, offering the unique opportunity to study the pathophysiological cascade from the genetic/molecular basis to the clinical phenotype in detail. For several of these mutations, the neurophysiological deficits could already be clarified, i.e. a reduction of sodium currents in inhibitory interneurons for SCN1A loss-of-function mutations and a defect of pre-synaptic transmitter release for STX1B. However, the consequences of these molecular alterations on large-scale human neuronal networks in-vivo have not been investigated. In a small pilot study, we could show structural and functional network differences in subjects with SCN1A loss-of-function mutations. In contrast to these monogenic forms of epilepsy, the genetic underpinnings of the most common genetic epilepsies, the so called genetic generalized epilepsy (IGE/GGE), are poorly understood. However, based on own results and recently published studies, network imaging can nonetheless detect structural and functional differences in IGE/GGE. Within this proposal, we will evaluate network imaging in common IGE/GGE as well as in two clinically similar monogenic epilepsies and their siblings to get a first insight on large-scale network alterations in human brains in different forms of genetic epilepsies and the consequences of known specific genetic mutations. To this end, we will acquire a comprehensive paradigm of functional and structural network imaging in a cohort of 50 subjects with established, monogenic epilepsy: namely SCN1A and STX1B loss-of function, each with a different, well-defined pathophysiological mechanism. Both are associated with a fever-associated epilepsy syndrome (GEFS+) including un-affected mutation carriers. We will also analyze 60 IGE/GGE patients and 30 unaffected siblings from our clinical and scientific database and acquire a matched control data set from the general population as well as intrafamilial controls. We will use graph theory analysis to generate objective measures of functional and structural network architecture, diffusion-tensor imaging measures of micro-structural integrity and working-memory task-based fMRI/MEG. We will use group comparisons including clinical covariables to distinguish effects of the underlying genetic causes from those of the epilepsy. Clinical severity will be taken into account in an ordinal scale from unaffected gene carriers to severe epilepsy. As next step, we will use machine learning to test whether network patterns for generalized genetic epilepsies can be identified in within- and across group comparisons (monogenic epilepsy, IGE/GGE and siblings). This could enable imaging-based sub-stratification of IGE/GGE.

• DFG Programme: Research Grants