Timothy Rittman

This paper translates group-level neurophysiological biomarkers into individual-level phenotyping to identify mechanisms of neurodegenerative diseases using MEG data.

This paper applies Bayesian hierarchical modeling to assess regional brain atrophy in rare neurodegenerative disorders, finding significant volume loss in specific brain regions across disease groups.

The paper identifies a mid-gestational molecular mechanism linking cortical size and ADHD through genetic and single-cell RNA sequencing analyses.

This paper introduces an automated method to quantify white matter hyperintensity confluence, showing it correlates with cognitive performance and dementia subtypes beyond volume.

This study identifies genetic factors influencing dynamic brain states from fMRI data and links them to cellular processes and complex traits.

This paper uses deep learning object detection models to automate tau lesion identification in PSP brain slides, incorporating uncertainty quantification to improve reliability and clinical relevance.

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 uses machine learning to analyze magnetoencephalography data from bvFTD patients to identify pathophysiological changes and improve case-control classification.