• Aucun résultat trouvé

Dynamical properties of fMRI functional connectivity in neuronal networks mediating consciousness

N/A
N/A
Protected

Academic year: 2021

Partager "Dynamical properties of fMRI functional connectivity in neuronal networks mediating consciousness"

Copied!
1
0
0

Texte intégral

(1)

Dynamical properties of fMRI functional connectivity in neuronal networks

mediating consciousness

Rapha¨el Li´egeois

1

, Mohamed Bahri

2

, Mattia Zorzi

1,3

, Steven Laureys

2

and Rodolphe Sepulchre

1,3

1Department of Electrical Engineering and Computer Science, University of Li`ege, Belgium 2Coma Science Group, Cyclotron Research Centre, University of Li`ege, Belgium 3Department of Engineering, Trumpington Street, University of Cambridge, United Kingdom

Email : {R.Liegeois, M.Bahri, MZorzi, Steven.Laureys}@ulg.ac.be, R.Sepulchre@eng.cam.ac.uk

1 Introduction

Induced loss of consciousness is commonly used in order to investigate the neural correlates of consciousness. Even if it has been observed in different studies that deep seda-tion is characterized by a global drop in cerebral activity, the neuronal mechanisms underlying this process are still actively explored. Recently, it has been shown in [2] that the static (i.e. considered as constant during the entire time course) functional connectivity (FC) of some neuronal net-works are affected by the change of state of consciousness whereas other networks remain unchanged. Meanwhile, the interest of considering FC as evolving in time (i.e. dynamic FC) has been highlighted and reviewed in [3].

In this work we use an autoregressive model adapted from [1] of the fMRI time courses resulting in a dynamic inter-pretation of FC. We analyze resting-state fMRI data in four different states of consciousness and show how the static interpretation of FC and its changes across stages of con-sciousness presented in [2] can be enriched when a dynami-calframework is used.

2 Methods

fMRI data was collected from 18 healthy subjects under-going four different states of consciousness: wakefulness (W), mild sedation (MS), deep sedation or unconscious-ness (U) and subsequent recovery of consciousunconscious-ness (R). The same preprocessing as in [2] was performed including 0.007-0.1Hz bandpass filtering and global signal regression. The dynamical interpretation of FC is based on an autore-gressive model of the fMRI time courses:

x(t) =

n

j=1

Aj∗ x(t − j) + ε(t) (1)

where x(t) is the fMRI volume at time t, the Ajcharacterize

the model, ε(t) is a white noise process and n is the order of the autoregressive model, denoted AR(n). This autoregres-sive model results in an extended version of the static func-tional connectivity matrix, the matrix-valued spectral den-sity: ¯ Φ(eiθ) = n

k=−n ¯ Rke−ikθ (2)

where ¯Rk= ¯R∗−k are the k-lagged connectivity matrices of

the AR(n) model (1) encoding dynamical properties of FC.

3 Results

Using an AR(1) model of the fMRI time courses we repre-sent in the next figure the static and dynamic connectivities in one node (the superior frontal sulcus) of the default mode network (DMN) and across the four states of consciousness.

Static connectivity First lagged connectivity

W MS U R W MS U R

0.2 0.3

0 0

Figure 1:Mean and StD of static (l) and dynamic (r) connectivi-ties of the DMN in the four states of consciousness

The static connectivity is computed from ¯R0corresponding

to the AR(1) model and follows the level of consciousness of the subjects, as presented in [2]. However, the trend is al-most opposite for the dynamical connectivity extracted from

¯

R1. In this case, the first lagged connectivity is higher during

unconsciousness. A possible interpretation is that the over-all connectivity between the regions considered here does not drop during unconsciousness but is delayed.

As a conclusion, we show here the utility of considering dy-namicalmodels of fMRI time courses in order to character-ize the properties of FC in a more comprehensive way and reveal features that are not captured by a static analysis.

References

[1] E. Avventi et al. (2013), ’ARMA Identification of Graphical Models’, IEEE Trans. Automat. Contr. 58(5), pp. 1167-78.

[2] P. Boveroux et al. (2010), ’Breakdown of within- and between-network Resting State Functional Magnetic Reso-nance Imaging Connectivity during Propofol-induced Loss of Consciousness’. Anesthesiology 113(5), pp.1038-53. [3] R.M. Hutchison et al. (2013), ’Dynamic functional connectivity: Promise, issues, and interpretations’, Neu-roimage 80, pp.360-78.

Figure

Figure 1: Mean and StD of static (l) and dynamic (r) connectivi- connectivi-ties of the DMN in the four states of consciousness The static connectivity is computed from ¯R 0 corresponding to the AR(1) model and follows the level of consciousness of the sub

Références

Documents relatifs

We have developed novel hierarchical Markov random eld models, based on the marginal posterior modes (MPM) criterion, that support information extraction from multi-temporal

Paralinguistic and linguistic cues are exploited by two specific dialogue managers that up- date a shared dialogue context (including dialogue history, dy- namic user profile

Dynamics of the total number of cells ρ(t) in units of 10 8 (central panel) and the mean phenotypic state of the cell population µ(t) (right panel) under the optimal drug

Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées.. Si vous n’arrivez pas

Screening the entire literature for computer-generated papers with methods by (Ginsparg, 2014; Labbé & Labbé, 2013; Nguyen, 2018) requires the harvesting of each paper in

صخلملا لقاونلا فاصنلأ ةينورتكللإا و ةيوينبلا صاوخلا ةسارد وه لمعلا اده نم فدهلا ةيئانثلا AlAs ، InAs لكش ىلع ، ةرقتسملا اهتينب ZB تارادمل ةيطخلا

Keywords Domain decomposition · Optimized boundary conditions · Navier Stokes equations · Schwarz Alternating Method.. PACS 65N55

Each of the three following sections then develops one theme of SEMA research that seems particularly relevant to guide the analysis of ENGOs’