Morris Freedman

This paper uses multi-sensory-evoked potentials and a support vector machine to classify aMCI with high accuracy, showing better performance when combining multiple modalities.

This study examines the relationship between white matter hyperintensities, cortical thickness, and empathy in neurodegenerative and cerebrovascular diseases, finding associations with specific brain regions and demographic factors.

The paper investigates the relationship between gene expression and brain atrophy in genetic frontotemporal dementia, finding correlations between atrophy patterns and specific gene sets related to vascular, glial, and neuronal functions.

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.