Faculty, Staff and Student Publications
Language
English
Publication Date
1-7-2025
Journal
Biometrics
DOI
10.1093/biomtc/ujaf027
PMID
40116280
PMCID
: PMC11926586
PubMedCentral® Posted Date
3-21-2025
PubMedCentral® Full Text Version
Post-print
Abstract
The joint analysis of multimodal neuroimaging data is vital in brain research, revealing complex interactions between brain structures and functions. Our study is motivated by the analysis of a vast dataset of brain functional connectivity (FC) and multimodal structural imaging (SI) features from the UK Biobank. Specifically, we aim to investigate the effects of SI features, such as white matter microstructure integrity (WMMI) and cortical thickness, on the whole-brain functional connectome network. This analysis is inherently challenging due to the extensive structural-functional associations and the intricate network patterns present in multimodal high-dimensional neuroimaging data. To bridge methodological gaps, we developed a novel multi-level sub-graph extraction method (dense bipartite with nested unipartite graph) within a matrix(network)-on-vector regression model. This method identifies subsets of spatially specific SI features that intensely and systematically influence FC sub-networks, while effectively suppressing false positives in large-scale datasets. Applying our method to a multimodal neuroimaging dataset of 4242 participants ffrom the UK Biobank, we evaluated the effects of whole-brain WMMI and cortical thickness on resting-state FC. Our findings indicate that the WMMI in corticospinal tracts and inferior cerebellar peduncle significantly affect functional connections of sensorimotor, salience, and executive sub-networks, with an average correlation of 0.81 ($p < 0.001$).
Keywords
Humans, Connectome, Brain, Neuroimaging, Regression Analysis, Male, White Matter, Female, United Kingdom, Magnetic Resonance Imaging, Multimodal Imaging, brain connectome, brain structure, dense clique, functional connectivity, multi-level graph
Published Open-Access
yes
Recommended Citation
Lu, Tong; Zhang, Yuan; Lyzinski, Vince; et al., "Evaluating the Effects of High-Throughput Structural Neuroimaging Predictors on Whole-Brain Functional Connectome Outcomes via Network-Based Matrix-on-Vector Regression" (2025). Faculty, Staff and Student Publications. 4384.
https://digitalcommons.library.tmc.edu/uthmed_docs/4384
Comments
This article has been corrected. See Biometrics. 2025 Aug 6;81(3):ujaf111.