A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development. Issue 539 (14th September 2022)

• Record Type: Journal Article
• Authors: Hu, Yingtian; Zeydabadinezhad, Mahmoud; Li, Longchuan; Guo, Ying
• Is Part Of: Journal of the American Statistical Association. Volume 117:Issue 539(2022)
• Page Start: 1134
• Page End: 1148
• Publication Date: 2022-09-14
• Subjects: Brain connectome -- dMRI -- fMRI -- Multilevel model -- Multimodal neuroimaging -- Network modeling; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2022.2055559
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Second, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Third, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.