Post-doc position offer, Predilepsy Project

Post-doc position offer

Stopping Childhood Absence seizure: detection and computational modeling

Context. Epilepsy refers to a neurological disorder that affects about 1% of the general population.  Absence seizures in children are sudden, lapses of consciousness associated with lack of voluntary movements and distinctive EEG pattern spike-wave discharges (SWD) around 3 Hz. One third of children with absence seizures are pharmaco-resistant. This type of seizures is related to a focal cortical onset that produces an abnormal interplay within a basal ganglia-thalamo-cortical loop and temporarily disrupts consciousness. Absence seizures place a burden on the patient’s quality of life due to constraints experienced in daily life, for example, the inability to drive a car and difficulties experienced at school because of attention problems.

Objective. As previously shown in animal models, appropriate (in term of modality, time, duration, intensity) exteroceptive stimulation applied 1 second before seizure onset might prevent seizures occurrence and disruption of awareness. The objective is to use a similar approach using EEG, in human. We aim at developing an optimal early detection algorithm based on extraction of specific signal features and machine learning. This algorithm would trigger an external stimulation.

Methods.

Signal processing. Development of optimized signal processing algorithms with high specificity sensitivity designed for very early detection of absence seizure, tested on an EEG recordings database.

Computational modeling. A full human virtual brain model has recently been developed in the lab. This model based on physiology can produce realistic EEG signals linked to two levels of consciousness. It will be 1) modified to integrate neurophysiological features of absence epilepsy, bistability (backround – ictal) and used to understand the mechanisms underlying absence seizures. The computational model will also be used to optimize the characteristic of exteroceptive stimulation. Models will use the Neural Mass Model formalism (NMMs). They will be developed in C and/or Python. They will extend already-existing models at neural mass level. Strong background is available (https://perso.univ-rennes1.fr/fabrice.wendling/ ).

Candidate profile. The research project is at the interface between biomathematics (neuro-inspired models), and neuroscience/neurology (epilepsy). The Post-doc fellow (PhD+experience) should have a strong background in computational neuroscience with experience in bio-signal processing. Knowledge in electrophysiology and/or EEG analysis would be an asset. The post-doc fellow will join a multidisciplinary team including research scientists in biomedical engineering, neurophysiological modeling, bio-physics, signal processing, electrophysiology, neurology.

Contract

Location in the city of Rennes, France. LTSI-Inserm laboratory, University of Rennes, Campus de Beaulieu. In addition, the post-doc fellow will have the opportunity to perform visits and to actively collaborate with clinicians of the Necker Child hospital (Paris).

Contact (please provide resume, cover letter and email of 2 references)

Pascal Benquet (Pr Neurosciences LTSI, France),

[email protected]