Alessia Sarica

TL;DR: The encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent) is reported, however, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups.

TL;DR: The lesson learnt suggest that when RF was applied on multi-modal data for prediction of Alzheimer's disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date.

TL;DR: A grand challenge to objectively compare algorithms based on a clinically representative multi-center data set of three diagnostic groups, finding the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity.

TL;DR: In this paper, the authors used resting state functional MRI data from the Simons Variation in Individuals Project (VIP) database to identify variants that increase risk for neurodevelopmental disorders and suggest that deletion in 16p11.2 may lead to impaired socio-cognitive function via dysregulation of prefrontal connectivity.

TL;DR: The results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.