[visionlist] One year Post doc position in Caen (France) on time series analysis for the neural characterization of different levels of wakefulness.

[Luc Brun]

We are seeking an outstanding postdoctoral research fellow with 
experience in deep learning / machine learning to work with us at Caen 
University, France
on a project investigating the analysis of multi-modal time series for 
the characterization of brain functional connectivity in different 
levels of wakefulness.

Basic information:

Host institution: ENSICAEN, University of Caen Normandy and CNRS, GREYC 
laboratory (UMR 6072)

Gross Salary: between 2339 and 3268 euros per month according to 
experience (charges included)

Duration: 12 months.

Starting date: from January 2020

Advantages: Possibility of French courses, participation in transport 
costs, possibility of restoration on site.


Background

Brain activity can be recorded in humans either by techniques based on 
the metabolic functioning of the neuron, such as Positron Emission 
Tomography
(PET) or Magnetic Resonance Imaging (MRI), or by techniques based on the 
electrical functioning of the neuron, such as electroencephalography 
(EEG) or
magnetoencephalography (MEG). If the first type of measurement allows to 
obtain recordings with an interesting spatial resolution, the second 
type allows
a higher temporal resolution. To summarize, these two approaches are 
both imperfect but complementary.

In this project, we are interested by this double approach in the 
context of the human characterization of different levels of wakefulness 
(from full awake
to deep sleep). Data in animals indicate that the transition from 
wakefulness to sleep causes changes in the relationships between 
different brain structures.
Sensory inputs via the thalamus are inhibited leading to a decrease in 
thalamo-cortical links in favor of intra-cortical relations. These 
modifications progres-
sively isolate the cortex in order to facilitate the descent into sleep. 
In terms of connectivity, these modifications are reflected in EEG by a 
clear decrease in global long-distance connections which are 
progressively replaced by an intensification of local cortico-cortical 
connectivity [1]. On the other hand, MRI
connectivity analyses tend to show that the spatial extent of the 
networks is preserved during the early phases of sleep [2]. It is 
necessary to explore this ap-
parent paradox in order to better understand the cerebral mechanisms at 
play during the descent into sleep but also more broadly to explore the 
networks of
human consciousness. . .


Work plan

The project is based on data from a cohort being currently acquired. It 
includes EEG and MRI acquisitions performed while the subjects are 
falling asleep for
a nap. In a first step, the candidate will study the dynamic evolution 
of the functional connectivity measured in EEG as a function of the 
correlation metric
(spectral coherence, synchronization probability, phase synchronization 
method, etc.). In a second step, these results will have to be compared 
to those obtained in MRI by taking into account the physical 
characteristics of the different signals.


In both cases (EEG/MRI) the correlations between the different areas 
will be measured using positive defined matrices measuring the 
correlation of the signals. For the EEG, these correlations can be 
measured directly from the temporal signals or from time-frequency 
analyses. In a first step, and for the EEG, we will have to characterize 
the matrices corresponding to the different phases of sleep using the 
calculation of averages on the variety of positive defined matrices[3]. 
In a second step, we will try (in EEG as well as in fMRI) to design 
recurrent networks on such matrices [4] in order to automatically 
classify the sleep phases as the acquisition progresses.

Candidate profile

• The candidate must have a recent Ph.D. (within 5 years) in Computer 
Science (or Applied Mathematics) in the field of Machine Learning.
• Knowledge and experience within Deep Learning frameworks is recommended.
• The candidate will perform research and algorithmic developments and 
solid programming skills are required.
• Interpersonal skills and the ability to work well individually or as a 
member of a project team are recommended.

• Good written and verbal communication skills are required, the 
candidate has to be fluent in spoken French or English and written 
English. Working language can be English or French.


Location

Caen, France in the GREYC UMR CNRS laboratory. Situated in the Normandy 
region of France close to the sea and about 240km west of Paris the city 
still has
many old quarters, a population of around 120,000 the city area has 
roughly 250,000 inhabitants.

Application

Interested candidates should submit their application to

• luc.brun at ensicaen.fr and
• olivier.etard at unicaen.fr

Please include in your application email one Curriculum Vitae, one 
statement of research letter explaining your interest and your skills 
for this position, and
2 reference letters (all in a single pdf file). Applications will be 
admitted until the position is filled.



References

1 M. Bouchard, J.-M. Lina, P.-O. Gaudreault, J. Dubé, N. Gosselin, et J. 
Car-
rier, EEG connectivity across sleep cycles and age, Sleep, p. zsz236, nov.
2019, doi: 10.1093/sleep/zsz236.
2 E. Tagliazucchi et E. J. W. van Someren, The large-scale functional con-
nectivity correlates of consciousness and arousal during the healthy and
pathological human sleep cycle, NeuroImage, vol. 160, p. 55-72, oct.
2017, doi: 10.1016/j.neuroimage.2017.06.026.
3 Nguyen, Xuan Son, Brun, Luc, Lezoray, Olivier & Bougleux, Sebastien. A
Neural Network Based on SPD Manifold Learning for Skeleton-Based
Hand Gesture Recognition. In The IEEE Conference on Computer Vi-
sion and Pattern Recognition (CVPR) , June 2019 .
4 Nguyen, Xuan Son, Brun, Luc, Lezoray, Olivier & Bougleux, Sébastien.
Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with
Neural Networks. In Proceedings of the 14th IEEE International Confer-
ence on Automatic Face and Gesture Recognition (FG 2019) 2019 .