Marc Fournier
McGill University

McGill Centre for Integrative Neuroscience
McConnell Brain Imaging Centre
Montreal Neurological Institute
3801 University Street, Montreal
Quebec, Canada, H3A 2B4

Research Projects

I am working as postdoctoral fellow at the McGill Centre for Integrative Neuroscience of the McConnell Brain Imaging Centre at the Montreal Neurological Institute, McGill University.

My ongoing research focus on the parcellation of the BigBrain, a high-resolution 3D model of a human brain at near cellular resolution. This reference brain model is based on histological sections reconstituted into a 3D volumetric dataset with 20µm resolution.

I previously had the opportunity to work on other brain imaging problems. I contributed to structural brain imaging applications such as tissue segmentation and the computation of brain asymmetries. I also participated to neuroscience studies in mapping brain activity using functional neuro-imaging techniques.

See here for ongoing and past work examples in brain imaging.

Research Interests


  • Structure and Anatomy
    • MRI and CT-Scan brain and skull image processing
    • Image registration, segmentation, surface extraction
    • Brain asymmetries computation and analysis
    • Human and chimpanzee brain image processing
    • Fossil skull CT-Scan imaging for paleoanthropology
    • High resolution brain atlas and head model design
  • Functional Imaging
    • fMRI, EEG and NIRS brain image processing
    • Multimodal images fusion and analysis
    • Neonate, children and adult brain mapping
    • Small animal model (rat) brain imaging
    • Applications: language study, clinical diagnosis
    • Diagnosis: epilepsy, intra-ventricular hemorrhage

    Selected Publications

    BigBrain: Automated cortical parcellation and comparison with existing brain atlases
    Fournier M., Lewis L.B., Evans A.C.
    Springer Lecture Notes in Computer Science, Vol. 10081, No. 1, 2017, 14-25.

    Abstract: Most available 3D human brain atlases provide information only at a macroscopic level, while 2D atlases are often at a microscopic level but lack 3D integration. A 3D atlas defined upon fine-grain anatomical detail of cortical layers and cells is necessary to fully understand neurobiological processes. "BigBrain," a high-resolution 3D model of a human brain at nearly cellular resolution, was released in 2013. This unique dataset enables the extraction of microscopic data for utilization in brain mapping, modeling and simulation. We propose an automated 3D cortical parcellation of the BigBrain volume into functionally-meaningful areas in order to create a modern high-resolution 3D cytoarchitectural atlas that will complement existing brain atlases. We use a distance metrics-based framework for BigBrain parcellation, and perform comparative analyses of our results with existing atlases (Brodmann and JuBrain atlases). This work has immediate application in teaching, neurosurgery, cognitive neuroscience, and imaging-based brain mapping.

    Syllabic discrimination in premature human infants prior to complete formation of cortical layers
    Mahmoudzadeh M., Dehaene-Lambertz G., Fournier M., Kongolo G., Goudjil S., Dubois J., Grebe R., Wallois F.
    Proceedings of the USA National Academy of Sciences Journal, Vol. 110, No. 12, 2013, 4846-4851.

    Abstract: The ontogeny of linguistic functions in the human brain remains elusive. Although some auditory capacities are described before term, whether and how such immature cortical circuits might process speech are unknown. Here we used functional optical imaging to evaluate the cerebral responses to syllables at the earliest age at which cortical responses to external stimuli can be recorded in humans (28- to 32-weeks gestational age). At this age, the cortical organization in layers is not completed. Many neurons are still located in the subplate and in the process of migrating to their final location. Nevertheless, we observed several points of similarity with the adult linguistic network. These results demonstrate a sophisticated organization of perisylvian areas at the very onset of cortical circuitry, 3 months before term. They emphasize the influence of innate factors on regions involved in linguistic processing and social communication in humans.

    Realistic head model design and 3D brain imaging of NIRS signals using audio stimuli on preterm neonates for intra-ventricular hemorrhage diagnosis
    Fournier M., Mahmoudzadeh M., Kazemi K., Kongolo G., Dehaene-Lambertz G., Grebe R., Wallois F.
    Springer Lecture Notes in Computer Science, Vol. 7512, No. 3, 2012, 172-179.

    Abstract: In this paper we propose an auditory stimulation and Near Infra-Red Spectroscopy (NIRS) hemodynamic changes acquisition protocol for preterm neonates. This study is designed to assess the specific characteristics of neurovascular coupling to auditory stimuli in healthy and ill neonate brains. The method could lead to clinical application in Intra-Ventricular Hemorrhage (IVH) diagnosis along with other techniques such as EEG. We propose a realistic head model creation with all useful head structures and brain tissues including the neonate fontanel for more accurate results from NIRS signals modeling. We also design a 3D imaging tool for dynamic mapping and analysis of brain activation onto the cortex surface. Results show significant differences in oxy-hemoglobin between healthy neonates and subjects with IVH.

    Automatic segmentation of newborns’ skull and fontanel from CT data using model-based variational level set
    Jafarian N., Kazemi K., Moghaddam H.A., Grebe R., Fournier M., Helfroush M.S., Gondry-Jouet C., Wallois F.
    Journal of Signal, Image and Video Processing, Vol. 8, No. 2, 2014, 377-387.

    Abstract: The newborn’s cranium is composed of flat cranial bone and fontanels forming together the envelope of the cerebral cavity. In this paper, we propose an automatic model-based method using variational level set to segment the skull and fontanels from CT images. In this approach, firstly a skull model consisting of cranial bones and fontanels is created and then used as constraint for level set evolution. Then, by removing the cranial bones from the segmented skulls, the fontanels are obtained. To verify the validity of the achieved results, automatically segmented skull and fontanels have been compared with the ones manually segmented by an expert using Dice similarity and Hausdorff dissimilarity measures, which show the good agreement between them. Furthermore, the surface areas of cranium and fontanel have been determined for these segmentations. The results for both, manual and automatic segmentation, are in good agreement.

    Surface-based method to evaluate global brain shape asymmetries in human and chimpanzee brains
    Fournier M., Combes B., Roberts N., Keller S.S., Crow J.T., Hopkins D.W., Prima S.
    IEEE International Symposium on Biomedical Imaging, Chicago, USA, 2011, 310-316.

    Abstract: In this paper we use humans and chimpanzees brain MRI databases to develop methods for evaluating global brain asymmetries. We perform brain segmentation and hemispheric surface extraction on both populations. The human brain segmentation pipeline is adapted to chimpanzees in order to obtain results of good quality. To alleviate the problems due to cortical variability we propose a mesh processing algorithm to compute the brain global shape. Surface-based global brain asymmetries are computed on chimpanzee and human subjects using individual mid-sagittal plane evaluation and population-level mean shape estimation. Asymmetry results are presented in terms of axis-wise components in order to perform more specific evaluation and comparison between the two populations.

    EM-ICP strategies for joint mean shape and correspondences estimation: applications to statistical analysis of shape and of asymmetry
    Combes B., Fournier M., Kennedy N.D., Braga J., Roberts N., Prima S.
    IEEE International Symposium on Biomedical Imaging, Chicago, USA, 2011, 1257-1263.

    Abstract: In this paper, we propose a new approach to compute the mean shape of unstructured, unlabelled point sets with an arbitrary number of points. This approach can be seen as an extension of the EM-ICP algorithm, where the fuzzy correspondences between each point set and the mean shape, the optimal non-linear transformations superposing them, and the mean shape itself, are iteratively estimated. Once the mean shape is computed, one can study the variability around this mean shape (e.g. using PCA) or perform statistical analysis of local anatomical characteristics (e.g. cortical thickness, asymmetry, curvature). To illustrate our method, we perform statistical shape analysis on human osseous labyrinths and statistical analysis of global cortical asymmetry on control subjects and subjects with situs inversus.

    Mapping the distance between the brain and the inner surface of the skull and their global asymmetries
    Fournier M., Combes B., Roberts N., Braga J., Prima S.
    SPIE Medical Imaging: Image Processing, Vol. 7962, No. 33-0Y, Orlando, USA, 2011, 1-7.

    Abstract: The primary goal of this paper is to describe i) the pattern of pointwise distances between the human brain (pial surface) and the inner surface of the skull (endocast) and ii) the pattern of pointwise bilateral asymmetries of these two structures. We use a database of MR images to segment meshes representing the outer surface of the brain and the endocast. We propose automated computational techniques to assess the endocast-to-brain distances and endocast-and-brain asymmetries, based on a simplified yet accurate representation of the brain surface, that we call the brain hull. We compute two meshes representing the mean endocast and the mean brain hull to assess the two patterns in a population of normal controls. The results show i) a pattern of endocast-to-brain distances which are symmetrically distributed with respect to the mid-sagittal plane and ii) a pattern of global endocast and brain hull asymmetries which are consistent with the well-known Yakovlevian torque.