Maite Termenón

This paper uses machine learning to predict DNA origami stability in physiological conditions based on physicochemical parameters and DLS measurements.

The paper examines individual variability in the relationship between resting-state fMRI metrics and cerebrovascular reactivity (CVR) using longitudinal data from 7 subjects with 10 sessions each.

This study evaluates ICA-based denoising strategies for improving CVR estimates in multi-echo BOLD fMRI during breath-hold tasks, finding that a conservative ICA model reduces motion effects best.

The paper constructs a low-dimensional morphospace of human fMRI brain networks based on topological motifs, identifying two principal components that explain most of the variability.