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Sex differences in brain structure significantly influence traumatic brain injury (TBI) onset and progression, yet this area is understudied. Herein, we developed sex-specific brain anatomical (macroscale) and axonal tract (mesoscale) templates and explored the sex variations at subject level using a set of T1-MRI (609 males, 721 females) and DTI images (506 males, 594 females). The FreeSurfer, ANTs, and DSI-Studio packages were used. We investigated overall/regional volumes, DTI metrics (including fractional anisotropy (FA), mean diffusivity, and radial diffusivity), and connectivity matrix across 23 brain regions. The brain connectome was derived by multiplying the fiber tract counts and the FA values within the connecting tracts, quantifying the connection strength within each pair of regions. Our subject-wise analysis revealed significant sex based differences (Mann-Whitney p-values < 0.05) across most studied regions for all parameters. The largest sex differences in brain connections were observed in five regions: corpus callosum and right/left cortex and cerebral white matter, all stronger in females. Brain regions were typically larger in males, yet females had higher fractional volumes in the majority of regions except for CSF and ventricles, known for their cushioning effect during head impacts. Additionally, the sex-specific templates better represented their targeted sex compared to opposite or mixed-sex populations as evaluated by root-mean-square-errors when comparing the DTI metrics and connectivity from the DTI templates against the median of subjects and deformation field in registering the subjects to the T1-MRI templates. Our findings highlight the necessity of sex-specific templates in accurate brain modeling and TBI research.
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Bayesian varying‐effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury
In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi‐subject Bayesian vector autoregressive model that estimates group‐specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group‐specific networks and selection of relevant covariate effects. We show improved performance over competing two‐stage approaches on simulated data. We apply our method on resting‐state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group‐level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.
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- PAR ID:
- 10543472
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Human Brain Mapping
- Volume:
- 45
- Issue:
- 10
- ISSN:
- 1065-9471
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/hbm.26763
