Pablo Villoslada

This paper introduces Tau-CTFP, a model for forecasting regional tau PET abnormality onset in Alzheimer's disease using connectome-informed continuous-time first-passage modeling, achieving high precision and recall in predicting abnormality onset.

The paper uses multilayer network analysis and multi-omics data to identify biological pathways in MS that explain disease severity and central nervous system damage.

This paper extends a biochemical model of circadian rhythms to include mechanical effects, showing that mechanical signals must be within an optimal range for oscillations to occur.

The paper predicts combination therapies for MS by modeling patient-specific signaling networks using phosphoproteomics data and identifies TAK1 as a co-druggable target.

This paper evaluates the fractal dimension as a marker of structural brain complexity in MRI, showing improved accuracy with scale optimization and highlighting registration's impact on FD estimates.

The paper uses fMRI to study how sadness affects interactions between cognitive and emotional brain networks, identifying the subgenual anterior cingulate cortex as a mediator.

The paper analyzes anti-correlated resting-state BOLD signals using dynamic functional connectivity and whole-brain modeling, finding hemodynamic response function drives fluctuations in connectivity and anti-correlations.