Andrea Mechelli

TL;DR: The full hemodynamic model is presented, how its associated Volterra kernels can be derived, and the model's validity in relation to empirical nonlinear characterizations of evoked responses in fMRI and other neurophysiological constraints are addressed.

TL;DR: Using network analysis of DTI data from healthy volunteers, and meta-analyses of published MRI studies in 26 brain disorders, Crossley et al. show that lesions across disorders tend to be concentrated at hubs.

TL;DR: Support-Vector-Machine has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data, and those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease and autistic spectrum disorder are reviewed.

TL;DR: It is shown that learning a second language increases the density of grey matter in the left inferior parietal cortex and that the degree of structural reorganization in this region is modulated by the proficiency attained and the age at acquisition.

TL;DR: The combined use of Bayes factors and DCM allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.