Section: New Results

Combining spatio-temporal CNS imaging modalities

Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach

Participants : Guillermo Gallardo, William Wells [Harvard Medical School, Boston, MA, USA] , Demian Wassermann, Rachid Deriche.

Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.

This work has been published in [6].

Spatial regularization based on dMRI to solve EEG/MEG inverse problem

Participants : Brahim Belaoucha, Théodore Papadopoulo.

In this work, we present a new approach to reconstruct dipole magnitudes of a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG). This approach is based on the structural homogeneity of the cortical regions which are obtained using diffusion MRI (dMRI). First, we parcellate the cortical surface into functional regions using structural information. Then, we use a weighting matrix that relates the dipoles’ magnitudes of sources inside these functional regions. The weights are based on the region’s structural homogeneity. Results of the simulated and real MEG measurement are presented and compared to classical source reconstruction methods.

This work has been published in [29], [11].

Large brain effective network from EEG/MEG data and dMR information

Participants : Brahim Belaoucha, Théodore Papadopoulo.

In this research, we aim at reconstructing the information flow in the brain for a given task. More than simple activations, we look at their relationship in time, so at networks constituted by nodes obtained from the parcellations of  [55] and edges coming from tractographies obtained by dMRI. In  [56] a multivariate auto-regressive model has been used to model the interactions between brain areas. Those areas are obtained using the methods depicted in paragraph 1. Then a putative network is built using connexions obtained by tractography augmented by cortico-cortical connexions (horizontal connexions between neighbor areas) which are not seen by dMRI. A two stage algorithm estimates the coefficients of the autoregressive matrices  [57]. Those matrices are constrained to be sparse, so that the non-zero coefficients can be used to estimate the effective network that was activated during the task. The method was validated using simulated data and applied to real MEG and EEG datasets.

This work has been published in [28], [11].

Inference and Visualization of Information Flow in the Visual Pathway using dMRI and EEG

Participants : Samuel Deslauriers-Gauthier, Jean-Marc Lina [ETS - Ecole de Technologie Supérieure, Montréal, CA] , Russel Buttler [SCIL, Sherbrooke University, CA] , Pierre-Michel Bernier [SCIL, Sherbrooke University, CA] , Kevin Whittingstall [SCIL, Sherbrooke University, CA] , Maxime Descoteaux [SCIL, Sherbrooke University, CA] , Rachid Deriche.

We propose a method to visualize information flow in the visual pathway following a visual stimulus. Our method estimates structural connections using diffusion magnetic resonance imaging and functional connections using electroencephalography. First, a Bayesian network which represents the cortical regions of the brain and their connections is built from the structural connections. Next, the functional information is added as evidence into the network and the posterior probability of activation is inferred using a maximum entropy on the mean approach. Finally, projecting these posterior probabilities back onto streamlines generates a visual depiction of pathways used in the network. We first show the effect of noise in a simulated phantom dataset. We then present the results obtained from left and right visual stimuli which show expected information flow traveling from eyes to the lateral geniculate nucleus and to the visual cortex. Information flow visualiza-tion along white matter pathways has potential to explore the brain dynamics in novel ways.

This work has been published in [37].

Information Flow in the White Matter During a Motor Task: A Structural Connectivity Driven Approach

Participants : Guillermo Gallardo, Demian Wassermann, Maxine Descoteaux [SCIL, Sherbrooke University, CA] , Samuel Deslauriers-Gauthier, Rachid Deriche.

Cognitive tasks emerge from the interaction of functionally specialized cortical regions . These interactions are supported by information flow through white matter fiber bundles connecting distant cortical regions. Estimating the information flow through white matter fiber bundles would therefore provide valuable information into the necessary cortical interactions to realize a task. In this work, we build a Bayesian network representing cortical regions and their connections using a structural connectivity driven parcellation derived from diffusion MRI (dMRI). We then introduce Magnetoencephalography (MEG) measurements as evidence into this network to infer the information flow between cortical regions. We show, for the first time, results on the interaction between the precentral, postcentral and occipital regions during a hand-movement task.

This work has been published in [39].