Michela Pievani

This study shows that functional connectivity patterns remain unique across stages of Alzheimer's disease but change in their configuration, suggesting potential for individualized brain fingerprinting in clinical settings.

This study identifies cognitive components and their relationship with brain network connectivity in Alzheimer's disease and frontotemporal dementia, revealing convergent and divergent patterns.

This study applies deep autoencoders to structural neuroimaging data from Alzheimer's and mild cognitive impairment patients to quantify deviations from healthy norms and compare performance with traditional classifiers.

The paper examines how evolutionary changes in human brain connectivity overlap with schizophrenia-related changes, finding that human-specific connections are more involved in schizophrenia pathology.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.