Luca Passamonti

This study investigates brain age differences in youth with conduct disorder compared to controls, finding accelerated brain maturation in those with adolescence-onset CD.

The paper investigates attentional biases to angry faces in individuals in remission from depression using functional MRI and drift diffusion modeling, finding persistent biases linked to brain activity in emotional regulation regions.

This paper introduces a deep graph neural network combining GNNs and TCNs to model spatio-temporal dynamics in rs-fMRI data, showing explainable neurobiological insights.

This paper develops a deep learning architecture for fusing multiple 3D neuroimaging modalities into informative latent embeddings, demonstrating their ability to cluster subjects into separable phenotypic strata.

This study creates a probabilistic atlas of the human locus coeruleus using ultra-high field MRI, revealing spatial gradients and providing a resource for future research.

This study investigates the relationship between neuroinflammation and protein aggregation in frontotemporal dementia using PET imaging and finds a strong positive correlation between these processes.

The paper uses dynamic causal models and pharmaco-magnetoencephalography to study GABAergic dynamics in human frontotemporal networks and identifies drug effects on neuronal function.

This study examines the relationship between microglial activation and white matter changes in dementia with Lewy bodies using PET and DTI imaging.

This study investigates how neuroinflammation interacts with functional brain connectivity in Alzheimer's disease and its impact on cognitive performance.

This paper develops a parameter-efficient deep learning model to predict conversion from mild cognitive impairment to Alzheimer’s disease using multimodal data, achieving high accuracy and specificity.

This paper develops a machine learning tool to interpret cognitive differences using brain features from neuroimaging data.