Biology subjects

White, S. R.

Publications and source records attributed to White, S. R..

3 recordsLinked to original sources

bioRxiv2018

Waves of maturation and senescence in micro-structural MRI markers of human cortical myelination over the lifespan

Seminal human brain histology work has demonstrated developmental waves of myelination. Here, using a micro-structural magnetic resonance imaging (MRI) marker linked to myelin, we studied fine-grained age differences to deduce waves of growth, stability, and decline of cortical myelination over the life-cycle. In 484 participants, aged 8-85 years, we fitted smooth growth curves to T1- to T2-weighted ratio in each of 360 regions from one of 7 cytoarchitectonic classes. From the first derivatives of these generally inverted-U trajectories, we defined three milestones: the age at peak growth; the age at onset of a stable plateau; and the age at the onset of decline. Age at peak growth had a bimodal distribution comprising an early (pre-pubertal) wave of primary sensory and motor cortices and a later (post-pubertal) wave of association, insular and limbic cortices. Most regions reached stability in the 30s but there was a second wave reaching stability in the 50s. Age at onset of decline was also bimodal: in some right hemisphere regions, the curve declined from the 60s, but in other left hemisphere regions, there was no significant decline from the stable plateau. These results are consistent with regionally heterogeneous waves of intracortical myelinogenesis and age-related demyelination.

neuroscience↗

bioRxiv2017

Gene co-expression networks in whole blood implicate multiple interrelated molecular pathways in obese asthma

BackgroundAsthmatic children who develop obesity have poorer outcomes compared to those that do not, including poorer control, more severe symptoms, and greater resistance to standard treatment. Gene expression networks are powerful statistical tools for characterizing the underpinnings of human disease that leverage the putative co-regulatory relationships of genes to infer biological pathways altered in disease states.\n\nObjectiveThe aim of this study was to characterize the biology of childhood asthma complicated by adult obesity.\n\nMethodsWe performed weighted gene co-expression network analysis (WGCNA) of gene expression data in whole blood from 514 adult subjects from the Childhood Asthma Management Program (CAMP). We then performed module preservation and association replication analyses in 418 subjects from two independent asthma cohorts (one pediatric and one adult).\n\nResultsWe identified a multivariate model in which four gene co-expression network modules were associated with incident obesity in CAMP (each P < 0.05). The module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-{kappa}B signaling, and Hedgehog signaling. The network structures of each of the four obese asthma modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in obese subjects in both replication cohorts (each P < 0.05).\n\nConclusionsOur gene co-expression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of obese asthma.\n\nKey MessagesO_LIWe hypothesized that individuals with asthma complicated by obesity had distinct blood gene expression signatures.\nC_LIO_LIGene co-expression network analysis implicated several inflammatory biological pathways in one form of obese asthma.\nC_LI\n\nCapsule SummaryThis work addresses a knowledge gap about the molecular relationship between asthma and obesity, suggesting that an endotype of obese asthma, known as asthma complicated by obesity, is underpinned by coherent biological mechanisms.\n\nAbbreviations

genomics↗

bioRxiv2017

Assessing dynamic functional connectivity in heterogeneous samples

Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brains functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.

neuroscience↗