Permutation Inference for Canonical Correlation Analysis

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that such a simple permutation test leads to inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.Comment: 49 pages, 2 figures, 10 tables, 3 algorithms, 119 reference

Learning by building digital libraries

The implications of using digital library software in educational contexts, for both students and software developers, are discussed using two case studies of students building digital libraries

Geodatabase Development to Support Hyperspectral Imagery Exploitation

Geodatabase development for coastal studies conducted by the Naval Research Laboratory (NRL) is essential to support the exploitation of hyperspectral imagery (HSI). NRL has found that the remote sensing and mapping science community benefits from coastal classifications that group coastal types based on similar features. Selected features in project geodatabases relate to significant biological and physical forces that shape the coast. The project geodatabases help researchers understand factors that are necessary for imagery post processing, especially those features having a high degree of temporal and spatial variability. NRL project geodatabases include a hierarchy of environmental factors that extend from shallow water bottom types and beach composition to inland soil and vegetation characteristics. These geodatabases developed by NRL allow researchers to compare features among coast types. The project geodatabases may also be used to enhance littoral data archives that are sparse. This paper highlights geodatabase development for recent remote sensing experiments in barrier island, coral, and mangrove coast types

Discovering markers of healthy aging:a prospective study in a Danish male birth cohort

There is a pressing need to identify markers of cognitive and neural decline in healthy late-midlife participants. We explored the relationship between cross-sectional structural brain-imaging derived phenotypes (IDPs) and cognitive ability, demographic, health and lifestyle factors (non-IDPs). Participants were recruited from the 1953 Danish Male Birth Cohort (N=193). Applying an extreme group design, members were selected in 2 groups based on cognitive change between IQ at age ~20y (IQ-20) and age ~57y (IQ-57). Subjects showing the highest (n=95) and lowest (n=98) change were selected (at age ~57) for assessments on multiple IDPs and non-IDPs. We investigated the relationship between 453 IDPs and 70 non-IDPs through pairwise correlation and multivariate canonical correlation analysis (CCA) models. Significant pairwise associations included positive associations between IQ-20 and gray-matter volume of the temporal pole. CCA identified a richer pattern - a single "positive-negative" mode of population co-variation coupling individual cross-subject variations in IDPs to an extensive range of non-IDP measures (r = 0.75, Pcorrected < 0.01). Specifically, this mode linked higher cognitive performance, positive early-life social factors, and mental health to a larger brain volume of several brain structures, overall volume, and microstructural properties of some white matter tracts. Interestingly, both statistical models identified IQ-20 and gray-matter volume of the temporal pole as important contributors to the inter-individual variation observed. The converging patterns provide novel insight into the importance of early adulthood intelligence as a significant marker of late-midlife neural decline and motivates additional study

Improved interpretability of brain-behavior CCA with Domain-driven Dimension Reduction

Canonical Correlation Analysis (CCA) has been widely applied to study correlations between neuroimaging data and behavioral data. Practical use of CCA typically requires dimensionality reduction with, for example, Principal Components Analysis (PCA), however, this can result in CCA components that are difficult to interpret. In this paper, we introduce a Domain-driven Dimension Reduction (DDR) method, reducing the dimensionality of the original datasets and combining human knowledge of the structure of the variables studied. We apply the method to the Human Connectome Project S1200 release and compare standard PCA across all variables with DDR applied to individual classes of variables, finding that DDR-CCA results are more stable and interpretable, allowing the contribution of each class of variable to be better understood. By carefully designing the analysis pipeline and cross-validating the results, we offer more insights into the interpretation of CCA applied to brain-behavior data

Meiofauna and Trace Metals From Sediment Collections in Florida After the Deepwater Horizon Oil Spill

Sediment from the Florida Gulf continental shelf was collected from 18 sites during October and November 2010 for meiofauna and trace-metals analysis. Collections were obtained using a Shipek® grab on the National Oceanic and Atmospheric Administration ship Pisces and spanned from the head of the DeSoto Canyon to off the southern end of the Florida peninsula approximately following the 100–200-m contour. Mean abundance of the dominant meiofaunal groups (nematodes, copepods, and polychaetes) was unchanged when compared with 2007–2009 data. Nematodes and copepods correlated positively with each other, and negatively with latitude and longitude, suggesting that there were higher densities in southern Florida. These results contrast with those from 2007–2009 in that previously nematodes had no correlation with latitude or longitude in Florida. Nickel (Ni) and vanadium (V) concentrations were higher in the western Florida locations and correlated positively with increasing depth. No relationship was found between Ni, V, and meiofauna densities

Confound modelling in UK Biobank brain imaging

© 2020 Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds

Sediment Starvation Destroys New York City Marshes' Resistance to Sea Level Rise

New York City (NYC) is representative of many vulnerable coastal urban populations, infrastructures, and economies threatened by global sea level rise. The steady loss of marshes in NYC's Jamaica Bay is typical of many urban estuaries worldwide. Essential to the restoration and preservation of these key wetlands is an understanding of their sedimentation. Here we present a reconstruction of the history of mineral and organic sediment fluxes in Jamaica Bay marshes over three centuries, using a combination of density measurements and a detailed accretion model. Accretion rate is calculated using historical land use and pollution markers, through a wide variety of sediment core analyses including geochemical, isotopic, and paleobotanical analyses. We find that, since 1800 CE, urban development dramatically reduced the input of marsh stabilizing mineral sediment. However, as mineral flux decreased, organic matter flux increased. While this organic accumulation increase allowed vertical accumulation to outpace sea level, reduced mineral content causes structural weakness and edge failure. Marsh integrity now requires mineral sediment addition to both marshes and subsurface channels and borrow pits, a solution applicable to drowning estuaries worldwide. Integration of marsh mineral/organic accretion history with modeling provides parameters for marsh preservation at specific locales with sea level rise

Molecular Analysis of ATP-sensitive K Channel Gating and Implications for Channel Inhibition by ATP

The β cell KATP channel is an octameric complex of four pore-forming subunits (Kir6.2) and four regulatory subunits (SUR1). A truncated isoform of Kir6.2 (Kir6.2ΔC26), which expresses independently of SUR1, shows intrinsic ATP sensitivity, suggesting that this subunit is primarily responsible for mediating ATP inhibition. We show here that mutation of C166, which lies at the cytosolic end of the second transmembrane domain, to serine (C166S) increases the open probability of Kir6.2ΔC26 approximately sevenfold by reducing the time the channel spends in a long closed state. Rundown of channel activity is also decreased. Kir6.2ΔC26 containing the C166S mutation shows a markedly reduced ATP sensitivity: the Ki is reduced from 175 μM to 2.8 mM. Substitution of threonine, alanine, methionine, or phenylalanine at position C166 also reduced the channel sensitivity to ATP and simultaneously increased the open probability. Thus, ATP does not act as an open channel blocker. The inhibitory effects of tolbutamide are reduced in channels composed of SUR1 and Kir6.2 carrying the C166S mutation. Our results are consistent with the idea that C166 plays a role in the intrinsic gating of the channel, possibly by influencing a gate located at the intracellular end of the pore. Kinetic analysis suggests that the apparent decrease in ATP sensitivity, and the changes in other properties, observed when C166 is mutated is largely a consequence of the impaired transition from the open to the long closed state