Roberto Toro

Statistical shape analysis of large datasets based diffeomorphic iterative centroids

Published in Frontiers in Neuroscience

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based...

The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratificatio...

Published in Molecular Autism

Background The tremendous clinical and aetiological diversity among individuals with autism spectrum disorder (ASD) has been a major obstacle to the development of new treatments, as many may only be effective in particular subgroups. Precision medicine approaches aim to overcome this challenge by combining pathophysiologically based treatments wi...

Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models

Motivated by applications in neuroanatomy, we propose a novel methodology for estimating the heritability which corresponds to the proportion of phenotypic variance which can be explained by genetic factors. Estimating this quantity for neuroanatomical features is a fundamental challenge in psychiatric disease research. Since the phenotypic variati...