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Maria J. Ledesma-Carbayo

Publications and source records attributed to Maria J. Ledesma-Carbayo.

2 recordsLinked to original sources

Kinematic analysis of cell lineage reveals coherent and robust mechanical deformation patterns in zebrafish gastrulation

Digital cell lineages reconstructed from 3D+time imaging data provide unique information to unveil mechanical cues and their role in morphogenetic processes. Our methodology based on a kinematic analysis of cell lineage data reveals deformation patterns and quantitative morphogenetic landmarks for a new type of developmental table. The characteristic spatial and temporal length scales of mechanical deformation patterns derived from a continuous approximation of cell displacements indicate a compressible fluid-like behavior of zebrafish gastrulating tissues. The instantaneous deformation rate at the mesoscopic level of the cells neighborhood is spatially and temporally heterogeneous. The robustness of mechanical patterns results from their cumulative history along cell trajectories. Unsupervised classification of mechanical descriptor profiles was used to assess the homogeneity of biomechanical cues in cell populations. Further clustering of cell trajectories according to their cumulative mesoscopic biomechanical history during gastrulation revealed ordered and coherent spatiotemporal patterns comparable to that of the embryonic fate map.

Systems Biology

Surface-driven registration method for the structure-informed segmentation of diffusion MR images

Current methods for processing diffusion MRI (dMRI) to map the connectivity of the human brain require precise delineations of anatomical structures. This requirement has been approached either segmenting the data in native dMRI space or mapping the structural information from T1-weighted (T1w) images. The characteristic features of diffusion data in terms of signal-to-noise ratio, resolution, as well as the geometrical distortions caused by the inhomogeneity of magnetic susceptibility across tissues hinder both solutions. Unifying the two approaches, we propose regseg, a surface-to-volume nonlinear registration method that segments homogeneous regions within multivariate images by mapping a set of nested reference-surfaces. Accurate surfaces are extracted from a T1w image of the subject, using as target image the bivariate volume comprehending the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) maps derived from the dMRI dataset. We first verify the accuracy of regseg on a general context using digital phantoms. Then we establish an evaluation framework using undistorted dMRI data from the Human Connectome Project (HCP) and known deformations derived from real inhomogeneity fieldmaps. We analyze the performance of regseg computing the misregistration error of the surfaces estimated after being mapped with regseg onto 16 datasets from the HCP. The distribution of errors shows a 95% CI of 0.56-0.66 mm, that is below the dMRI resolution (1.25 mm, isotropic). Finally, we cross-compare the proposed tool against a nonlinear b0-to-T2w registration method, thereby obtaining a significantly lower misregistration error with regseg. Therefore, we demonstrate that regseg allows the accurate mapping of structural information in dMRI space, enabling the application of new structure-informed techniques in the connectome extraction.

Bioengineering