A Multimodal, Multilevel Neuroimaging Model for Investigating Brain Connectome Development

• Activity Number: 155 - Section on Statistics in Imaging Student Paper Award Winners
• Type: Topic-Contributed
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Yingtian Hu* and Mahmoud Zeydabadinezhad and Longchuan Li and Ying Guo
• Companies: Department of Biostatistics and Bioinformatics, Emory University and Department of Pediatrics, Emory University School of Medicine and Department of Pediatrics, Emory University School of Medicine and Emory University
• Keywords: Brain connectome; Multimodal neuroimaging; fMRI; dMRI; Multilevel model; Network modeling

Abstract

The advancement of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to investigate brain development. Motivated by the Philadelphia Neurodevelopmental Cohort (PNC) study, we develop a multimodal multilevel model (MMM) to infer brain maturation in white matter structural connection and intrinsic functional connection from dMRI and resting-state fMRI. MMM uses a population-level latent network modeling to model latent functional and structural connectivity states representing the underlying brain connectomes and data generative models to model observed functional and structural connectivity data across subjects based on the population-level latent connection states. A module-wise parameterization based on brain network topology overcomes the computational challenge in whole-brain connectomics. Analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing more significant white fiber connectivity growth in high-order cognitive networks and uncovering functional connectome development mainly derives from global functional integration rather than direct anatomical connections.