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Home Projects 1st call (2008) 9. Multiscale cortical surface registration for high precision functional mapping and connectivity analysis

9. Multiscale cortical surface registration for high precision functional mapping and connectivity analysis

Dr. J.-P. Thiran (EPFL), Dr. M. Gross (ETHZ) - PhD student: Dominique Zosso

Project finished in November 2012.

Statistical group studies are a recognised methodology in brain imaging, in particular in functiona imaging. Functional Magnetic Resonance (fMRI) is one of the most important tools in this domain, generating an impressive amount of research as attested by the hundreds of papers published every year. One of the key steps in the statistical analysis of fMRI data is the registration of the images of the different subjects into a common reference, aiming at compensating the inter-individual anatomical variability. The current methods in this domain almost exclusively involve global voxelbased registration, i.e. registration methods aiming at deforming the whole image volume to make it similar to a reference brain. Unfortunately, those volume registration techniques are not able to cope with the large complexity of the cortical surface, so that there is no guarantee that specific gyri or sulci will be properly registered by these methods. Though, such a very precise cortical surface registration is needed for precise brain functional mapping by fMRI group studies. As an example, in a recent work, we have shown that precise cortical registration can reveal very important activation zones that were never described by fMRI studies using global state-of-the-art registration methods. We have indeed demonstrated the presence of several non-primary auditory areas specialised for sound recognition and sound localisation around the primary auditory cortex in humans [1, 2]. This had never been shown in vivo before. The registration algorithm used in this work involved the manual placing of landmarks to guide the registration. Achieving the same level of registration precision but fully automatically will obviously open new perspectives in brain functional mapping. This is one of the motivations of this project.
Moreover, recently there has been an increasing interest for brain connectivity analysis thanks to the development of effective diffusion MRI sequences, such as Diffusion Tensor MRI (DTI) or Diffusion Spectrum MRI (DSI), and associated image algorithms analysis, namely tractography. These new tools allow inferring information about the brain architecture (at least in the white matter). Our group played a significant role in this context [3]. The research in this domain is now moving from individual subject analysis to group studies [4]. In this context, we have recently proposed a methodology to
study the global brain circuitry by a labelled connectivity matrix. The entries of this matrix correspond to small regions of interest covering the whole cortical surface and its cells contain connectivity measures between the corresponding regions of interest, obtained with diffusion MR tractography. By statistical analysis of the connectivity matrix of patients and control subjects, this approach has an important potential in revealing for instance connectivity abnormalities in the early stages of some important pathologies such as schizophrenia, multiple sclerosis, epilepsy, etc. But to be effective, this approach needs a precise matching between the regions of interest defined on the cortical surface of each subject in the study. Here again, a precise cortical surface registration is thus the key challenge, that we will tackle in this project.

[1] D. Viceic, Postlesional Plasticity in central auditory processing: MRI and fMRI image registration and statistical signal processing, PhD thesis, EPFL Signal Processing Institute, 2007.
[2] D. Viceic, et al., Human auditory belt areas specialized in sound recognition: a functional magnetic resonance imaging study. Neuroreport, 2006. 17(16):1659-62.
[3] P. Hagmann, et al., DTI mapping of human brain connectivity: statistical fibre tracking and virtual dissection. Neuroimage, 2003. 19(3):545-54.
[4] P. Hagmann, et al., Hand preference and sex shape the architecture of language networks. Hum Brain Mapp, 2006. 27(10):828-35.

Contact: Dominique Zosso