Franco Cauda

The paper predicts structural and functional brain age gaps from MRI data to assess their predictive power for cognitive performance in healthy adults.

The paper introduces a Bayesian thresholding method for activation likelihood estimation (ALE) and demonstrates its equivalence to frequentist thresholds, showing that lower Bayesian thresholds are statistically valid but indicate weaker hypothesis support.

This study correlates gene expression of ASD candidate genes with structural brain changes in ASD using meta-analysis and gene expression maps.

This study uses meta-analysis to show that the Default Mode Network (DMN) varies spatially across different task domains, revealing a gradient from internal to external functions.

This paper uses a meta-analytic network mapping approach to show that gray matter volume increases and decreases in psychiatric disorders are statistically related across different brain regions.

The paper analyzes co-alterations in brain pathology across disorders, finding that long-distance co-alterations identify key pathological hubs and relate to functional connectivity.

The paper uses Bayes factor analysis on voxel-based morphometry data to assess the specificity of brain alterations in schizophrenia and Alzheimer's disease, finding that more disease-specific areas are altered earlier.

The paper uses multivoxel pattern analysis to show that BOLD signal variability distinguishes pain stimulation from rest conditions and reveals entropy differences across brain regions.

This paper models fMRI signal dynamics using a generalized Langevin equation with stable noise, showing a spectrum of activity modes related to Levy motion in brain networks.

The paper introduces a fuzzy c-mean clustering method to detect nodes in the insular cortex, revealing new functional connectivity patterns beyond existing models.

This paper reviews hypotheses on mindfulness meditation's effects and cerebral correlates, proposing an integration with consciousness through shared brain areas.

This study uses neuroimaging to challenge the distinction between autism, schizophrenia, and obsessive-compulsive disorders, finding common brain damage patterns and proposing a two-cluster damage network model.