Fabio Sambataro

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

This study uses machine learning to predict post-surgical structural rearrangements in brain tumors by analyzing preoperative functional and diffusion MRI data.

This paper proposes a constrained autoregressive model to estimate effective brain connectivity by integrating structural and functional data, showing improved performance in community detection and autism classification.

This study shows polyglutamine-expanded androgen receptor disrupts muscle function by altering calcium dynamics and excitation-contraction coupling gene expression, which can be reversed by treatment.

This paper introduces a method to identify multivariate brain network differences between groups using sparse connectivity analysis, applied to human and mouse connectomes.

This paper evaluates the test-retest reliability of task-based and resting-state fMRI and CBF measures in healthy volunteers, finding large variability and suggesting activation extent as a reliability estimate for new paradigms.

The paper investigates how cerebral blood flow changes in response to drugs targeting different neurotransmitter systems and finds a reliable spatial link between blood flow changes and receptor densities.