Biology subjects

Elliott, M. A.

Publications and source records attributed to Elliott, M. A..

3 recordsLinked to original sources

bioRxiv2018

Brain Iron Mediates the Relationship Between Cognition and Neighborhood Socioeconomic Status in Youth

Non-heme brain iron is a critical metabolic cofactor essential for healthy brain development.1 Iron deficiency is the most common nutritional disorder in the world, with greater prevalence of non-heme iron deficiency among individuals of lower socioeconomic status (SES). However, it remains unknown how brain iron accumulation during development may impact cognition. Brain iron can be measured in vivo using R2* weighted magnetic resonance imaging (MRI); prior work has established that higher R2* is associated with higher iron content.2,3 We hypothesized that more iron in the basal ganglia (BG) regions of the caudate, putamen, and pallidum would be associated with improved cognitive performance and potentially mediate the known relationship between neighborhood-level socioeconomic status (SES) and cognition.4

neuroscience↗

bioRxiv2017

Multifactorial Dynamics of White Matter Connectivity During Adolescence

Studying developmental changes in white matter connectivity is critical for understanding neurobiological substrates of cognition, learning, and neuropsychiatric disorders. This becomes especially important during adolescence when a rapid expansion of the behavioral repertoire occurs. Several factors such as brain geometry, genetic expression profiles, and higher level architectural specifications such as the presence of segregated modules have been associated with the observed organization of white matter connections. However, we lack understanding of the extent to which such factors jointly describe the brain network organization, nor have insights into how their contribution changes developmentally. We constructed a multifactorial model of white matter connectivity using Bayesian network analysis and tested it with diffusion imaging data from a large community sample. We investigated contributions of multiple factors in explaining observed connectivity, including architectural specifications, which promote a modular yet integrative organization, and brains geometric and genetic features. Our results demonstrated that the initially dominant geometric and genetic factors become less influential with age, whereas the effect of architectural specifications increases. The identified structural modules are associated with well-known functional systems, and the level of association increases with age. This integrative analysis provides a computational characterization of the normative evolution of structural connectivity during adolescence.

neuroscience↗

bioRxiv2017

Harmonization Of Multi-Site Diffusion Tensor Imaging Data

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, is counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

neuroscience↗