Paola Galdi

The paper introduces SiGn, an open-source fMRI phantom that generates dynamic signals for quality assurance, validated through scanning and analysis.

This study investigates the neural correlates of dream recall frequency using multimodal neuroimaging in 258 individuals, finding associations between DRF and brain structure and function.

This study evaluates the Vogt-Bailey Index in task-based fMRI across different pulse sequence protocols, finding it consistently identifies motor cortex connectivity despite protocol variations.

This study uses the Vogt-Bailey index to analyze cortical microstructure gradients in preterm and term infants, finding spatially distributed alterations in preterm infants.

This paper analyzes the Vogt-Bailey index to assess local functional homogeneity in the human cortex and compares it to the ReHo algorithm, finding the VB index better detects sharp changes in local functional organization.

The paper identifies spatial variation in neighborhood correlations during volume-to-surface mapping of fMRI data and proposes a mitigation approach.

The paper uses DTI and NODDI metrics from 16 white matter tracts in preterm and term infants to identify general factors that capture shared variance and predict preterm birth outcomes.

This study finds that maternal cortisol levels are linked to differences in neonatal amygdala microstructure and connectivity, with sex-specific effects observed in girls and boys.

This study examines hierarchical complexity in neonatal and adult brain networks, finding greater complexity in term-born neonates compared to preterms, linked to connectivity patterns in intermediate tiers.

The paper uses morphometric similarity networks derived from MRI data to predict brain age and identify neuroanatomic variations in preterm and term neonates.

This paper introduces a method using clustering and stochastic rank aggregation to identify functional neuromarkers in resting-state fMRI data, finding distinct parcel rankings between healthy controls and patients with Parkinson's disease and amyotrophic lateral sclerosis.

The paper predicts general intelligence from resting-state fMRI data using a distributed brain network approach, explaining 20% of variance in intelligence scores.

The paper predicts personality traits from resting-state fMRI data, finding that openness to experience is the only reliably predicted factor.