Biology subjects

Brickman, A.

Publications and source records attributed to Brickman, A..

4 recordsLinked to original sources

bioRxiv2019

White matter regions with low microstructure in young adults are associated with white matter hyperintensities in late life

BackgroundWhite matter hyperintensities (WMH) are areas of increased signal observed on T2-weighted magnetic resonance imaging (MRI) that reflect macrostructural white matter damage frequently observed in aging. The extent to which diminished microstructure precedes or results from white matter damage is unknown. The aim of this study was to evaluate the hypothesis that white matter areas that show normatively lower microstructure are most susceptible to develop WMH. MethodsFive hundred fifty-seven older adults (age: 73.9{+/-}5.7yrs) underwent diffusion weighted imaging (DWI) and T2-weighted magnetic resonance imaging (MRI). Diffusion weighted imaging scans were processed into parametric maps of fractional anisotropy (FA) and T2-weighted MRI scans were segmented into WMH. All images were spatially normalized to standard space. A FA template was created to represent normative values from a separate, independent sample of young, healthy adults (N=49, age: 25.8{+/-}2.8yrs) and a WMH frequency template was created from the segmented WMH in the older adults. We compared FA values between areas defined as WMH with those defined as normal appearing white matter (NAWM) in the older participants. White matter hyperintensity frequency was binned (0-5%, 5-10%, 10-15%, 15-20%, >20%) and we determined whether WMH frequency bins were different by normative FA values defined in the younger group. ResultsFractional anisotropy values were lower (p<0.001) in WMH regions compared with NAWM regions in the older sample. Areas with higher WMH frequency in older adults had lower FA values in younger adults (5-10%>10-15%>15-20%; p<0.001). DiscussionLow FA values are observed in frank WMH, but FA is also normatively low in regions with high WMH frequency prior to damage. Regions with normatively lower microstructure are more susceptible to future damage from factors such as chronic hypoperfusion or pathology.

neuroscience↗

bioRxiv2018

Rare variants imputation in admixed populations: Comparison across reference panels and bioinformatics tools.

BackgroundImputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants imputation in an admixed Caribbean Hispanic population (CH). MethodsWe evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% [≤]minor allele frequency (MAF) [≤] 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: a) high-quality variant counts according to imputation tools internal indexes (e.g. IMPUTE2 "Info"[≥]80%). b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; c) Cohens kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion. ResultsSHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs 0.75% for high-quality rare and ultra-rare variants, respectively; Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g. rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05). ConclusionReference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

bioinformatics↗

bioRxiv2018

Deep structural brain lesions associated with consciousness impairment early after haemorrhagic stroke

BackgroundThe significance of deep structural lesions on level of consciousness early after intracerebral haemorrhage (ICH) is largely unknown.\n\nMethodsWe studied a consecutive series of patients with spontaneous ICH that underwent MRI within 7 days of the bleed. We assessed consciousness by testing for command following from time of MRI to hospital discharge, and determined 3-months functional outcomes using the Glasgow Outcome Scale-Extended (GOS-E). ICH and oedema volumes, intraventricular haemorrhage (IVH), and midline shift (MLS) were quantified. Presence of blood and oedema in deep brain regions previously implicated in consciousness were assessed. A machine learning approach using logistic regression with elastic net regularization was applied to identify parameters that best predicted consciousness at discharge controlling for confounders.\n\nResultsFrom 158 ICH patients that underwent MRI, 66% (N=105) were conscious and 34% (N=53) unconscious at the time of MRI. Almost half of unconscious patients (49%, N= 26) recovered consciousness by ICU discharge. Focal lesions within subcortical structures predicted persistent impairment of consciousness at discharge together with MLS, IVH, and ICH and oedema volumes (AUC 0.74; 95%-CI 0.73-0.75). Caudate nucleus, midbrain peduncle, and pontine tegmentum were implicated as critical structures. Unconscious patients predicted to recover consciousness had better 3-month functional outcomes than those predicted to remain unconscious (35% vs 0% GOS-E [≥]4; p-value=0.02).\n\nConclusionMRI lesions within key subcortical structures together with measures reflecting the mass effect of the haemorrhage (lesion volumes, IVH, MLS) obtained within one week of ICH can help predict early recovery of consciousness and 3-month functional outcome.

neuroscience↗

bioRxiv2018

A common statistical misunderstanding in Psychology and Neuroscience: Do we need normally distributed independent or dependent variables for linear regression to work?

Our ability to draw conclusions from experiments often relies on the application of inferential statistics, such as those included within the general-linear-modeling framework. The purpose of this commentary is to direct attention a common fallacy that can be observed in paper reviews and professional exchanges in the field of Psychology and Neuroscience pertaining to multiple regression analysis. The fallacy concerns the confusion of the requirements on the residuals of a linear-regression model with requirements on the dependent or independent variables themselves. A simple thought experiment, algebraic demonstration, and simulation study can demonstrate this misconception. It is our hope that consideration of the fallacy may help clarify distinctions between technical statistical issues and conceptual interpretation of research findings.

scientific communication and education↗