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Aim: We used functional magnetic resonance imaging (fMRI) to precisely measure both the localization and size of the cortical projections of artificial scotomas in healthy subjects as well as the size of a reversible retinal scotoma in a patient with central serous chorioretinopathy (CSCR)., Methods: Using a 3T MRI scanner, anatomical and functional data were acquired on two healthy subjects and a patient with CSCR. Retinotopic maps were first reconstructed using phase mapping techniques. Next, a block paradigm consisting of a grey background alternating with a full-field, flickering checkerboard was used to stimulate the complete central (19.5 degrees) visual field. A condition with artificial peri-foveal scotomas of different sizes and eccentricities was interleaved in healthy subjects. Differential maps were computed to obtain the cortical representation (size and location) of artificial scotomas. Full-field functional data were also acquired in the CSRC patient, at the acute stage and after recovery., Results: Cortical projections of each scotoma were identified using differential maps and carefully characterized with quantitative analysis: the measured cortical positions of the inactivated cortical zones were compared with the known radius and eccentricity values in the scotomas in the visual field. We also compared the size of the inactivated cortical zones to the known size of scotomas. However, we found a consistent relationship between the size of the scotomas and their cortical projections, albeit with the absolute size smaller than expected from known cortical magnification factors. The cortical deactivation zone was also observed in the CRSC patient, which disappeared at recovery stage., Conclusions: Cortical retinotopic mapping can be performed successfully for both artificial and retinal scotomas. This study can serve as a basis for the future investigation of cortical plasticity in the visual cortex.

Unlabelled: INTRODUCTION. We used high-field (3T) functional magnetic resonance imaging (fMRI) to map the retinotopic organization of human cortical areas., Methods: Retinotopic maps were reconstructed using existing mapping techniques. Stimuli were made of a rotating wedge stimulus, which provided angular coordinate maps, and an expanding or contracting ring, which provided eccentricity coordinate maps. Stimuli consisted of a grey background alternating with a flickering checkerboard. A Brucker 3T scanner equipped with a head coil and a custom optical system was used to acquire sets of echoplanar images of 20 occipital coronal slices within a RT of 2.111 ms and an 8 mm3 voxel resolution. Surface models of each subject's occipital lobes were constructed using the Brainvisa software from a sagittal T1-weighted image with a 1 mm3 voxel resolution. The cortical models were then inflated to obtain unfolded surfaces. Statistical analyses of the functional data were made under SPM99, and the response amplitudes were finally assigned to the cortical reconstructed surfaces., Results: We identified boundaries between different early visual areas (V1, V2, V3) using eccentricity and polar angle retinotopic maps and detection of reversals in the representation of the polar angle. Both complete maps and reversals corresponding to the V1/V2 borders were clearly visible with a single recording session. Also, we were able to compare data from subjects across various fMRI acquisitions and found that there was a strong correlation between maps acquired at different sessions for the same subject., Conclusions: We developed a quick (<40 min) retinotopic cortical area mapping method at 3T, which makes it possible to study the cortical remapping in patients with retinal scotomas.

We survey the recent activities of the Odyssée Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG, and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical magnetic resonance (MR) images. Anatomical connectivity can be extracted from diffusion tensor magnetic resonance images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter (WM) as geodesics in some Riemannian space. We then go to the statistical modeling of functional magnetic resonance imaging (fMRI) signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part that we then use to perform clustering of voxels in a way that is inspired by the theory of support vector machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in the framework of image statistics and partial differential equations (PDEs) with an eye on registering fMRI to the anatomy. The paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases.