Olivier Coulon

Structural Group Analysis of Functional Activation Maps

We present here a new method for cerebral activation detection over a group of subjects. This method is performed using individual activation maps of any sort. It aims at processing a group analysis while preserving individual information and at overcoming as far as possible limitations of the spatial normalization used to compare different subjects. We designed it such that it provides the individual occurrence of the activations detected at a group level. The localization can then be performed on the individual anatomy of each subject. The analysis starts with a hierarchical multiscale object-based description of each individual map. These descriptions are then compared, rather than comparing the images directly. The analysis is thus performed at an object level instead of voxel by voxel. It is made using a comparison graph, on which a labeling process is performed. The label field on the graph is modeled by a Markov random field, which allows us to introduce high-level rules of interrogation of the data. The process has been evaluated on simulated data and real data from a PET protocol.

Multiscale Measures in Linear Scale-Space for Characterizing Cerebral Functional Activations in 3D PET Difference Images

A multiscale representation of PET difference images is presented in order to characterize functional cerebral activations. This method is based on the linear scale-space representation of our 3-dimensional images, and derived from the scale-space primal sketch [1]. Objects are extracted at each level of scale, and linked across the scales to get objects in the whole scale-space. Several measures are associated to them and we show how these measures can be used to establish a hierarchy between these objects in order to characterize functional activations.

Sulci as Landmarks

When spatially normalizing images of the human cerebral cortex, the folding pattern is often used as a proxy for architecture. However, the variability of the folding pattern across individuals creates a lot of difficulties. Furthermore, the mechanisms underlying gyrification are still under exploration, and the links between folding and architecture are unclear outside primary areas. New computational methods focused on cortical sulci have been designed to support the research programs aiming at improving the role of sulci as architectural landmarks. They provide automatic recognition of the sulci, sulcal-based spatial normalization, and models of the variability of the shape of the sulci.

Structural Group Analysis of Functional Maps

We present here a new method for cerebral activation detection. This method is performed on individual activation maps of any sort and aims at performing a multi-subject group analysis while preserving individual information and overcoming problems induced by spatial normalisation. The analysis is made through a multi-scale object-based description of individual maps. It is these structural descriptions which are compared, rather than the images themselves. The comparison is made through a graph, on which a labelling process is performed. The label field on the graph is modelled by a Markov random field, which allows us to introduce high-level rules of data interrogation.

Place de l'anatomie dans la cartographie fonctionnelle du cerveau

Lessor des techniques dimagerie fonctionnelle depuis une quinzaine dannees a profondement modifie la maniere daborder la cartographie fonctionnelle du cerveau humain. Il a conduit a lemergence de vastes projets confrontes a la manipulation denormes quantites de donnees anatomo-fonctionnelles. Ces projets impliquent la possibilite de comparer des individus et de thesauriser les resultats obtenus en matiere de localisation anatomique par rapport a un referentiel.

Group Analysis of Individual Activation Maps Using 3D Scale-Space Primal Sketches and a Markovian Random Field

We present here a new method of cerebral activation detection. This method is applied on individual activation maps of any sort. It aims at processing a group analysis while preserving individual information and at overcoming as far as possible problems induced by spatial normalization used to compare different subject. The analysis is made through a multi-scale object-based description of the individual maps and these descriptions are compared, rather than comparing directly the images in a stereotactic space. The comparison is made using a graph, on which a labeling process is performed. The label field on the graph is modeled by a Markovian random field, which allows us to introduce high-level rules of interrogation of the data.