Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments

Estimating spatiotemporal models for multi-subject fMRI is computationally challenging. We propose a mixed model for localization studies with spatial random effects and time-series errors. We develop method-of-moment estimators that leverage population and spatial information and are scalable to massive datasets. In simulations, subject-specific estimates of activation are considerably more accurate than the standard voxel-wise general linear model. Our mixed model also allows for valid population inference. We apply our model to cortical data from motor and theory of mind tasks from the Human Connectome Project (HCP). The proposed method results in subject-specific predictions that appear smoother and less noisy than those from the popular single-subject univariate approach. In particular, the regions of motor cortex associated with a left-hand finger-tapping task appear to be more clearly delineated. Subject-specific maps of activation from task fMRI are increasingly used in pre-surgical planning for tumor removal and in locating targets for transcranial magnetic stimulation. Our findings suggest that using spatial and population information is a promising avenue for improving clinical neuroimaging

A practical approach for uncertainty quantification of high-frequency soil respiration using Forced Diffusion chambers

This paper examines the sources of uncertainty for the Forced Diffusion (FD) chamber soil respiration (Rs) measurement technique and demonstrates a protocol for uncertainty quantification that could be appropriate with any soil flux technique. Here we sought to quantify and compare the three primary sources of uncertainty in Rs: (1) instrumentation error; (2) scaling error, which stems from the spatial variability of Rs; and (3) random error, which arises from stochastic or unpredictable variation in environmental drivers and was quantified from repeated observations under a narrow temperature, moisture, and time range. In laboratory studies, we found that FD instrumentation error remained constant as Rs increased. In field studies from five North American ecosystems, we found that as Rs increased from winter to peak growing season, random error increased linearly with average flux by about 40% of average Rs. Random error not only scales with soil flux but scales in a consistent way (same slope) across ecosystems. Scaling error, measured at one site, similarly increased linearly with average Rs, by about 50% of average Rs. Our findings are consistent with previous findings for both soil fluxes and eddy covariance fluxes across other northern temperate ecosystems that showed random error scales linearly with flux magnitude with a slope of ~0.2. Although the mechanistic basis for this scaling of random error is unknown, it is suggestive of a broadly applicable rule for predicting flux random error. Also consistent with previous studies, we found the random error of FD follows a Laplace (double‐exponential) rather than a normal (Gaussian) distribution.Keywords: variability, soil respiration, kurtosis, random error, uncertainty, measurement erro

Book Reviews

Reviews of the following books: The Spice of Popery: Converging Christianities on an Early American Frontier by Laura M. Chmielewski; Champlain\u27s Dream: The European Founding of North America by David Hackett Fischer; Indians in Eden: Wabanakis and Rustiates on Maine\u27s Mount Desert Island, 1840s-1920s by Bunny McBride and Harald E.L. Prins; Jonathan Fisher of Blue Hill, Maine: Commerce, Culture and Community on the Eastern Frontier by Kevin D. Murphy; Deering: A Social and Architectural History by William David Barry and Patricia MccGraw Anderson; Bethel, Maine: A Brief History by Stanley Russell Howe; Seated by the Sea: The Maritime History of Portland, Maine, and Its Irish Longshoremen by Michael C. Connelly; Mr. Speaker! The Life and Times of Thomas B. Reed by James Grant; Rural Unweb Mothers: An American Experience, 1870-1950 by Mazie Hough; Rural Unwed Mothers: An American Experience, 1870-1950 by Mazie Houg

Correlation of mRNA and protein levels: Cell type-specific gene expression of cluster designation antigens in the prostate

Background: Expression levels of mRNA and protein by cell types exhibit a range of correlations for different genes. In this study, we compared levels of mRNA abundance for several cluster designation (CD) genes determined by gene arrays using magnetic sorted and laser-capture microdissected human prostate cells with levels of expression of the respective CD proteins determined by immunohistochemical staining in the major cell types of the prostate - basal epithelial, luminal epithelial, stromal fibromuscular, and endothelial - and for prostate precursor/stem cells and prostate carcinoma cells. Immunohistochemical stains of prostate tissues from more than 50 patients were scored for informative CD antigen expression and compared with cell-type specific transcriptomes. Results: Concordance between gene and protein expression findings based on 'present' vs. 'absent' calls ranged from 46 to 68%. Correlation of expression levels was poor to moderate (Pearson correlations ranged from 0 to 0.63). Divergence between the two data types was most frequently seen for genes whose array signals exceeded background (> 50) but lacked immunoreactivity by immunostaining. This could be due to multiple factors, e.g. low levels of protein expression, technological sensitivities, sample processing, probe set definition or anatomical origin of tissue and actual biological differences between transcript and protein abundance. Conclusion: Agreement between these two very different methodologies has great implications for their respective use in both molecular studies and clinical trials employing molecular biomarkers.This work was supported by grant DK63630 and DK069690 from NIDDK. Additional funding came from grants CA85859, CA98699 and CA111244 from NCI, and PM50 GMO76547/Center for Systems Biology

Economic shocks and health resilience: lessons from the Russian Federation

Background: Despite extensive research on determinants of health, there is much less information on factors protecting health among those exposed to economic shocks. Using longitudinal data from the Russian Federation in the post-Soviet period, we examined individual-level factors that enhance resilience of health to economic shocks. Methods: Logistic regression analysed factors associated with good self-assessed health (SAH) and health resilience, using pooled samples from the Russia Longitudinal Monitoring Survey-Higher School of Economics (1994–2012). Results: The general population consistently reported ‘average’ SAH, indicating almost invariant trends over the years. Male gender was the strongest predictor of good SAH and health resilience. Other factors positively associated with good SAH were age, higher education, employment, residing in rural areas, living in a larger and/or non-poor household. Among unemployed and those remaining unemployed, residing in rural areas, living in a larger and/or non-poor household remained the strongest predictors of good SAH and health resilience. These same factors were also important for males with recent job loss. Conclusions: Several factors predicting good SAH in the general population also influence health resilience factors among those remaining unemployed and experiencing a job loss. Such factors help to identify those most vulnerable and aid targeting assistance during economic crises

The PSEN1, p.E318G variant increases the risk of Alzheimer’s disease in APOE-ԑ4 carriers

The primary constituents of plaques (Aβ42/Aβ40) and neurofibrillary tangles (tau and phosphorylated forms of tau [ptau]) are the current leading diagnostic and prognostic cerebrospinal fluid (CSF) biomarkers for AD. In this study, we performed deep sequencing of APP, PSEN1, PSEN2, GRN, APOE and MAPT genes in individuals with extreme CSF Aβ42, tau, or ptau levels. One known pathogenic mutation (PSEN1 p.A426P), four high-risk variants for AD (APOE p.L46P, MAPT p.A152T, PSEN2 p.R62H and p.R71W) and nine novel variants were identified. Surprisingly, a coding variant in PSEN1, p.E318G (rs17125721-G) exhibited a significant association with high CSF tau (p = 9.2 × 10(-4)) and ptau (p = 1.8 × 10(-3)) levels. The association of the p.E318G variant with Aβ deposition was observed in APOE-ε4 allele carriers. Furthermore, we found that in a large case-control series (n = 5,161) individuals who are APOE-ε4 carriers and carry the p.E318G variant are at a risk of developing AD (OR = 10.7, 95% CI = 4.7-24.6) that is similar to APOE-ε4 homozygous (OR = 9.9, 95% CI = 7.2.9-13.6), and double the risk for APOE-ε4 carriers that do not carry p.E318G (OR = 3.9, 95% CI = 3.4-4.4). The p.E318G variant is present in 5.3% (n = 30) of the families from a large clinical series of LOAD families (n = 565) and exhibited a higher frequency in familial LOAD (MAF = 2.5%) than in sporadic LOAD (MAF = 1.6%) (p = 0.02). Additionally, we found that in the presence of at least one APOE-ε4 allele, p.E318G is associated with more Aβ plaques and faster cognitive decline. We demonstrate that the effect of PSEN1, p.E318G on AD susceptibility is largely dependent on an interaction with APOE-ε4 and mediated by an increased burden of Aβ deposition

The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis.

BACKGROUND: The effects of systolic blood pressure (SBP), serum total cholesterol (TC), fasting plasma glucose (FPG), and body mass index (BMI) on the risk of cardiovascular diseases (CVD) have been established in epidemiological studies, but consistent estimates of effect sizes by age and sex are not available. METHODS: We reviewed large cohort pooling projects, evaluating effects of baseline or usual exposure to metabolic risks on ischemic heart disease (IHD), hypertensive heart disease (HHD), stroke, diabetes, and, as relevant selected other CVDs, after adjusting for important confounders. We pooled all data to estimate relative risks (RRs) for each risk factor and examined effect modification by age or other factors, using random effects models. RESULTS: Across all risk factors, an average of 123 cohorts provided data on 1.4 million individuals and 52,000 CVD events. Each metabolic risk factor was robustly related to CVD. At the baseline age of 55-64 years, the RR for 10 mmHg higher SBP was largest for HHD (2.16; 95% CI 2.09-2.24), followed by effects on both stroke subtypes (1.66; 1.39-1.98 for hemorrhagic stroke and 1.63; 1.57-1.69 for ischemic stroke). In the same age group, RRs for 1 mmol/L higher TC were 1.44 (1.29-1.61) for IHD and 1.20 (1.15-1.25) for ischemic stroke. The RRs for 5 kg/m(2) higher BMI for ages 55-64 ranged from 2.32 (2.04-2.63) for diabetes, to 1.44 (1.40-1.48) for IHD. For 1 mmol/L higher FPG, RRs in this age group were 1.18 (1.08-1.29) for IHD and 1.14 (1.01-1.29) for total stroke. For all risk factors, proportional effects declined with age, were generally consistent by sex, and differed by region in only a few age groups for certain risk factor-disease pairs. CONCLUSION: Our results provide robust, comparable and precise estimates of the effects of major metabolic risk factors on CVD and diabetes by age group