The Epidermal Growth Factor Domain of the Mutation Does Not Appear to Influence Disease Progression in CADASIL When Brain Volume and Sex Are Taken into Account.

BACKGROUND AND PURPOSE: By studying the evolution of brain volume across the life span in male and female patients, we aimed to understand how sex, brain volume, and the epidermal growth factor repeat domain of the mutation, the 3 major determinants of disability in CADASIL, interact in driving disease evolution. MATERIALS AND METHODS: We used validated methods to model the evolution of normalized brain volume with age in male and female patients using nonparametric regression in a large, monocentric cohort with prospectively collected clinical and high-resolution MR imaging data. We used k-means clustering to test for the presence of different clinical course profiles. RESULTS: We included 229 patients (mean age, 53 [SD, 12] years; 130 women). Brain volume was larger in women (mean size, 1024 [SD, 62] cm3 versus 979 [SD, 50] cm3; P < .001) and decreased regularly. In men, the relationship between brain volume and age unexpectedly suggested an increase in brain volume around midlife. Cluster analyses showed that this finding was related to the presence of a group of older male patients with milder symptoms and larger brain volumes, similar to findings of age-matched women. This group did not show specific epidermal growth factor repeat domain distribution. CONCLUSIONS: Our results demonstrate a detrimental effect of male sex on brain volume throughout life in CADASIL. We identified a subgroup of male patients whose brain volume and clinical outcomes were similar to those of age-matched women. They did not have a specific distribution of the epidermal growth factor repeat domain, suggesting that yet-unidentified predictors may interact with sex and brain volume in driving disease evolution.

Mapping the asynchrony of cortical maturation in the infant brain: A MRI multi-parametric clustering approach.

While the main neural networks are in place at term birth, intense changes in cortical microstructure occur during early infancy with the development of dendritic arborization, synaptogenesis and fiber myelination. These maturational processes are thought to relate to behavioral acquisitions and the development of cognitive abilities. Nevertheless, in vivo investigations of such relationships are still lacking in healthy infants. To bridge this gap, we aimed to study the cortical maturation using non-invasive Magnetic Resonance Imaging, over a largely unexplored period (1-5 post-natal months). In a first univariate step, we focused on different quantitative parameters: longitudinal relaxation time (T1), transverse relaxation time (T2), and axial diffusivity from diffusion tensor imaging (λ//) These individual maps, acquired with echo-planar imaging to limit the acquisition time, showed spatial distortions that were first corrected to reliably match the thin cortical ribbon identified on high-resolution T2-weighted images. Averaged maps were also computed over the infants group to summarize the parameter characteristics during early infancy. In a second step, we considered a multi-parametric approach that leverages parameters complementarity, avoids reliance on pre-defined regions of interest, and does not require spatial constraints. Our clustering strategy allowed us to group cortical voxels over all infants in 5 clusters with distinct microstructural T1 and λ// properties The cluster maps over individual cortical surfaces and over the group were in sound agreement with benchmark post mortem studies of sub-cortical white matter myelination, showing a progressive maturation of 1) primary sensori-motor areas, 2) adjacent unimodal associative cortices, and 3) higher-order associative regions. This study thus opens a consistent approach to study cortical maturation in vivo.

A framework based on sulcal constraints to align preterm, infant and adult human brain images acquired in vivo and post mortem.

Robust spatial alignment of post mortem data and in vivo MRI acquisitions from different ages, especially from the early developmental stages, into standard spaces is still a bottleneck hampering easy comparison with the mainstream neuroimaging results. In this paper, we test a landmark-based spatial normalization strategy as a framework for the seamless integration of any macroscopic dataset in the context of the Human Brain Project (HBP). This strategy stems from an approach called DISCO embedding sulcal constraints in a registration framework used to initialize DARTEL, the widely used spatial normalization approach proposed in the SPM software. We show that this strategy is efficient with a heterogeneous dataset including challenging data as preterm newborns, infants, post mortem histological data and a synthetic atlas computed from averaging the ICBM database, as well as more commonly studied data acquired in vivo in adults. We then describe some perspectives for a research program aiming at improving folding pattern matching for atlas inference in the context of the future HBP's portal.

Spatial normalization of brain images and beyond.

The deformable atlas paradigm has been at the core of computational anatomy during the last two decades. Spatial normalization is the variant endowing the atlas with a coordinate system used for voxel-based aggregation of images across subjects and studies. This framework has largely contributed to the success of brain mapping. Brain spatial normalization, however, is still ill-posed because of the complexity of the human brain architecture and the lack of architectural landmarks in standard morphological MRI. Multi-atlas strategies have been developed during the last decade to overcome some difficulties in the context of segmentation. A new generation of registration algorithms embedding architectural features inferred for instance from diffusion or functional MRI is on the verge to improve the architectural value of spatial normalization. A better understanding of the architectural meaning of the cortical folding pattern will lead to use some sulci as complementary constraints. Improving the architectural compliance of spatial normalization may impose to relax the diffeomorphic constraint usually underlying atlas warping. A two-level strategy could be designed: in each region, a dictionary of templates of incompatible folding patterns would be collected and matched in a way or another using rare architectural information, while individual subjects would be aligned using diffeomorphisms to the closest template. Manifold learning could help to aggregate subjects according to their morphology. Connectivity-based strategies could emerge as an alternative to deformation-based alignment leading to match the connectomes of the subjects rather than images.

A combination of atlas-based and voxel-wise approaches to analyze metabolic changes in autoradiographic data from Alzheimer's mice.

Murine models are commonly used in neuroscience research to improve our knowledge of disease processes and to test drug effects. To accurately study brain glucose metabolism in these animals, ex vivo autoradiography remains the gold standard. The analysis of 3D-reconstructed autoradiographic volumes using a voxel-wise approach allows clusters of voxels representing metabolic differences between groups to be revealed. However, the spatial localization of these clusters requires careful visual identification by a neuroanatomist, a time-consuming task that is often subject to misinterpretation. Moreover, the large number of voxels to be computed in autoradiographic rodent images leads to many false positives. Here, we proposed an original automated indexation of the results of a voxel-wise approach using an MRI-based 3D digital atlas, followed by the restriction of the statistical analysis using atlas-based segmentation, thus taking advantage of the specific and complementary strengths of these two approaches. In a preliminary study of transgenic Alzheimer's mice (APP/PS1), and control littermates (PS1), we were able to achieve prompt and direct anatomical indexation of metabolic changes detected between the two groups, revealing both hypo- and hypermetabolism in the brain of APP/PS1 mice. Furthermore, statistical results were refined using atlas-based segmentation: most interesting results were obtained for the hippocampus. We thus confirmed and extended our previous results by identifying the brain structures affected in this pathological model and demonstrating modified glucose uptake in structures like the olfactory bulb. Our combined approach thus paves the way for a complete and accurate examination of functional data from cerebral structures involved in models of neurodegenerative diseases.

Validation of MRI-based 3D digital atlas registration with histological and autoradiographic volumes: an anatomofunctional transgenic mouse brain imaging study.

Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to test drug effects. To accurately study neuroanatomy and brain function in small animals, histological staining and ex vivo autoradiography remain the gold standards to date. These analyses are classically performed by manually tracing regions of interest, which is time-consuming. For this reason, only a few 2D tissue sections are usually processed, resulting in a loss of information. We therefore proposed to match a 3D digital atlas with previously 3D-reconstructed post mortem data to automatically evaluate morphology and function in mouse brain structures. We used a freely available MRI-based 3D digital atlas derived from C57Bl/6J mouse brain scans (9.4T). The histological and autoradiographic volumes used were obtained from a preliminary study in APP(SL)/PS1(M146L) transgenic mice, models of Alzheimer's disease, and their control littermates (PS1(M146L)). We first deformed the original 3D MR images to match our experimental volumes. We then applied deformation parameters to warp the 3D digital atlas to match the data to be studied. The reliability of our method was qualitatively and quantitatively assessed by comparing atlas-based and manual segmentations in 3D. Our approach yields faster and more robust results than standard methods in the investigation of post mortem mouse data sets at the level of brain structures. It also constitutes an original method for the validation of an MRI-based atlas using histology and autoradiography as anatomical and functional references, respectively.