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16 juillet 2019

Multivariate Multifractal Analysis of Human Brain Dynamics in Magnetoencephalography (MEG)

Catégorie : Post-doctorant

Offre de Post-Doc à pourvoir dès que possoble :

Multivariate Multifractal Analysis of Human Brain Dynamics in Magnetoencephalography (MEG)

Research theme: statistical signal processing, MEG, EEG, cognitive neuroscience.

Duration: 1 year (salary commensurate with experience).

Teams: CEA/NeuroSpin (INRIA Parietal & INSERM) & (SISYPHE, CNRS/ENS Lyon).

Advisors: Philippe Ciuciu (philippe.ciuciu@cea.fr, +33 1 6908 7785) and Patrice Abry (patrice.abry@ens-lyon.fr, +33 4 72728493).

Collaborator: Virginie vanWassenhove (virginie.van.wassenhove@gmail.com, CEA/NeuroSpin & INSERM) for applications in cognitive neurosciences.

Location: The candidate will be hired by ENS Lyon through the ANR Multifracs project and located at ENS Lyon and/or at NeuroSpin Saclay, depending on scientific developments.

Application: Interested candidates should send their CV, a motivation letter and at least 2 references.




Research topic: The Human brain is a complex biological system endowed with cognitive complexity.

A sound interfacing between cognitive neuroscience and sophisticated quantification of complex

systems requires the elaboration of signal processing techniques providing explanatory power for the

functional description of neural systems. In this context, the renewed interest in scale-free neural

activity has been sparked by several lines of evidence: first, ongoing neural oscillations recorded in

the resting brain have a deterministic impact on the neural responses evoked by the presentation of

stimuli [Thivierge08; Sadaghiani09]. Second, the interregional coherence of activity fluctuations

(or functional connectivity) in the resting brain is commensurate with the synchronization of low frequency

neural oscillations (< 2 Hz) namely, the scale-free portion of the power spectrum. Third,

scale-free descriptions of neural activity are correlated with the scale-free quantification of behavioral

outcomes [Monto08]. These converging evidence for the importance of scale-free properties in neural

systems have been observed with various functional neuroimaging techniques such as functional magnetic

resonance imaging (fMRI) [Ciuciu12; Ciuciu14] or magnetoencephalography (MEG) [Zilber14;

LaRocca_JNM_18]. This line of research is not without major challenges: functional neuroimaging

data are noisy, multivariate, organized in complex networks. As such, novel statistical signal processing

methods are necessary and we contend that the multifractality approach as well as some dedicated

measure of functional connectivity operating in the scale-free regime provide promising venues.

In the last decade, multifractal analysis has received significant appeal in the mathematics and signal

processing communities. In particular, the Leader-based Multifractal (WLMF) framework [Wendt07;

Wendt09] has emerged as the most accurate strategy for characterizing univariate scale-free time series.

Using the WLMF formalism, we showed that MEG activity displays refined modulations of scalefree

properties that are functionally meaningful in the context of learning and plasticity. Notably,

a post-training decrease of long memory and an increase in multifractality were observed suggesting

the existence of mesoscopic biomarkers of functional plasticity [Zilber14; LaRocca_sub2JN_19].

Concomitantly, the weighted Phase Lag Index (wPLI) has emerged as one of the most robust quantification

of phase synchronization in M/EEG time series [Vinck2011]. Although the wPLI measure

is currently used in oscillatory frequency regimes, we have recently extended its definition in

the wavelet domain to deal with the scale-free regime [LaRocca_EUSIPCO18] and thus propose

a fractal connectivity estimator. On the above mentioned MEG database, the wavelet-based wPLI

measure has allowed to uncover modifications of functional connectivity induced by training.


Work plan. The goal of the project is to perform multivariate scale-free analysis of MEG data. 30

healthy individuals performing a timing task will be recorded with MEG. The experimental protocol

was conceived in collaboration with the Cognition & Brain Dynamics team (Dir: V. van Wassenhove)

as an extension of recent work (see [Polti2018] for the general context in experimental psychology;

see [Kononowicz2019] and [Grabot2017] for the neuroscientific aspects).

Two main analytical tracks will be explored using scale-free indices:


Multivariate self-similarity. Based on a recently devised multivariate self-similarity model, a

multivariate procedure for the joint estimation of Hurst parameters has been assessed [AbryDidier2018].

The latter will be applied to MEG source estimates collected during rest and during timing.

The co-existence of distributed Hurst exponents will be used as a proxy for the number

of independently activated neural sources (following our recent working hypothesis elaborated

in [LaRocca_JNM_18]). The joint estimation procedure will be applied to MEG time series,

and also tested on the envelope of the signals (following Hilbert transform) in oscillatory regimes.


Multivariate multifractality. The multivariate multifractal formalism proposed in [jaffard2018acha]

enables to quantify dependencies between time series at higher statistical orders, i.e., beyond

cross-correlation. This framework will be used to construct a new index of functional connectivity

between remote brain regions. As for self-similarity, these tools will be applied to MEG time

series and to the envelope of oscillatory activity to test several means of constructing functional

connectivity indices.


Implementation & interpretation. The candidate will implement data analysis in collaboration

with the different members of the consortium using a dedicated Python package (univariate

multifractal analysis1). Statistical analyses will be performed at the group-level using the MNEPython



Skills. Candidates strongly motivated by exploratory and multidisciplinary research topics, with

relevant background in statistical signal processing, wavelet theory and/or cognitive neuroscience will

be appreciated. Skills in and practice of Python and Matlab are expected as well as preliminary

experience with time-resolved neuroimaging data analysis.

Keywords. Statistical signal processing, scale-free, 1/f, MEG, EEG, timing, time perception, Python,


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