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

Glacial cycles promote greater dispersal, which can help explain larger clutch sizes, in north temperate birds

Earth’s glacial history and patterns in the life history traits of the planet’s avifauna suggest the following interpretations of how recent geological history has affected these key characteristics of the biota: 1) Increased colonizing ability has been an important advantage of increased dispersal, and life history strategies are better categorized by dispersive colonizing ability than by their intrinsic growth rates; 2) Birds of the North Temperate Zone show a greater tendency to disperse, and they disperse farther, than tropical or south temperate birds; 3) Habitat changes associated with glacial advance and retreat selected for high dispersal ability, particularly in the North; and 4) Selection for greater dispersal throughout the unstable Pleistocene has also resulted in other well-recognized life history contrasts, especially larger clutch sizes in birds of North Temperate areas

Microsystems Integration Towards Point-of-Care Monitoring of Clozapine Treatment for Adherence, Efficacy, and Safety

Schizophrenia is a challenging and complex disorder with 30–50% of patients not responding to first line antipsychotic treatment. Clozapine is the only antipsychotic approved by the FDA for treatment-resistant schizophrenia and is the most effective antipsychotic medication currently available. Yet, clozapine remains underutilized because of the requirements for frequent invasive and burdensome monitoring to 1) titrate doses to achieve effective blood levels, as well as 2) monitor white blood cells on a weekly basis for the first six months due to risk of agranulocytosis, a rare but potentially fatal side effect of clozapine. These blood draws, and the time lag in receiving reports from central labs, can add several more visits to the caregivers' treatment plan, which may not be feasible for the patient nor the treatment team. This contributes to a very low prescription rate for clozapine, making it one of the most underutilized evidence-based treatments in the field of mental health. The objective of this work is to progress toward a point-of-care approach to monitor both white blood cells and clozapine within a clinical setting. This would significantly lower the burden associated with clozapine treatment by allowing both tests to be performed rapidly during a single doctor's office visit or at the pharmacy. Specifically, I have developed and studied novel clozapine detection schemes based on electrochemical signal amplification in chitosan-based films. Moreover, I have investigated impedance cytometry coupled with hydrodynamic focusing and osmotic lysis to provide label- and reagent-free differential white blood cell counting capabilities. Finally, I have integrated the components in a microsystem capable of concurrent sensing of both biomarkers in whole blood samples. This proof-of-concept device lays the foundation for a fully integrated and automated lab-on-a-chip for point-of-care or even at-home testing to ensure treatment adherence, efficacy, and safety. This will allow for broader use of clozapine by increasing convenience to patients as well as medical professionals, thus improving the lives of people affected by schizophrenia through personalized medicine

False Discovery Rate and Localizing Power

False discovery rate (FDR) is commonly used for correction for multiple testing in neuroimaging studies. However, when using two-tailed tests, making directional inferences about the results can lead to vastly inflated error rate, even approaching 100% in some cases. This happens because FDR only provides weak control over the error rate, meaning that the proportion of error is guaranteed only globally over all tests, not within subsets, such as among those in only one or another direction. Here we consider and evaluate different strategies for FDR control with two-tailed tests, using both synthetic and real imaging data. Approaches that separate the tests by direction of the hypothesis test, or by the direction of the resulting test statistic, more properly control the directional error rate and preserve FDR benefits, albeit with a doubled risk of errors under complete absence of signal. Strategies that combine tests in both directions, or that use simple two-tailed p-values, can lead to invalid directional conclusions, even if these tests remain globally valid. To enable valid thresholding for directional inference, we suggest that imaging software should allow the possibility that the user sets asymmetrical thresholds for the two sides of the statistical map. While FDR continues to be a valid, powerful procedure for multiple testing correction, care is needed when making directional inferences for two-tailed tests, or more broadly, when making any localized inference

Forage silica and water content control dental surface texture in guinea pigs and provide implications for dietary reconstruction

Recent studies have shown that phytoliths are softer than dental enamel but still act as abrasive agents. Thus, phytolith content should be reflected in dental wear. Because native phytoliths show lower indentation hardness than phytoliths extracted by dry ashing, we propose that the hydration state of plant tissue will also affect dental abrasion. To assess this, we performed a controlled feeding experiment with 36 adult guinea pigs, fed exclusively with three different natural forages: lucerne, timothy grass, and bamboo with distinct phytolith/silica contents (lucerne < grass < bamboo). Each forage was fed in fresh or dried state for 3 weeks. We then performed 3D surface texture analysis (3DST) on the upper fourth premolar. Generally, enamel surface roughness increased with higher forage phytolith/silica content. Additionally, fresh and dry grass feeders displayed differences in wear patterns, with those of fresh grass feeders being similar to fresh and dry lucerne (phytolith-poor) feeders, supporting previous reports that "fresh grass grazers" show less abrasion than unspecialized grazers. Our results demonstrate that not only phytolith content but also properties such as water content can significantly affect plant abrasiveness, even to such an extent that wear patterns characteristic for dietary traits (browser-grazer differences) become indistinguishable

Fast and powerful heritability inference for family-based neuroimaging studies.

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study

Transport coefficients of multi-particle collision algorithms with velocity-dependent collision rules

Detailed calculations of the transport coefficients of a recently introduced particle-based model for fluid dynamics with a non-ideal equation of state are presented. Excluded volume interactions are modeled by means of biased stochastic multiparticle collisions which depend on the local velocities and densities. Momentum and energy are exactly conserved locally. A general scheme to derive transport coefficients for such biased, velocity dependent collision rules is developed. Analytic expressions for the self-diffusion coefficient and the shear viscosity are obtained, and very good agreement is found with numerical results at small and large mean free paths. The viscosity turns out to be proportional to the square root of temperature, as in a real gas. In addition, the theoretical framework is applied to a two-component version of the model, and expressions for the viscosity and the difference in diffusion of the two species are given.Comment: 31 pages, 8 figures, accepted by J. Phys. Cond. Matte

Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation.

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (&gt;175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms

Efficacy of approach bias modification as an add-on to smoking cessation treatment: study protocol for a randomized-controlled double-blind trial

Background Although effective treatments for smoking cessation are available, long-term abstinence is the exception rather than the norm. Accordingly, there is a need for novel interventions that potentially improve clinical outcome. Although implicit information processing biases, for example approach biases for smoking-related stimuli, are ascribed a dominant role in the maintenance of tobacco dependence, these biases are hardly targeted in current treatment. Past research has shown that so-called Approach Bias Modification (AppBM) trainings, aiming to modify this bias, lead to improved long-term abstinence in abstinent alcoholic inpatients when delivered as an add-on to treatment-as-usual. Findings on the efficacy of AppBM in smoking have been inconsistent. The present large-scale clinical trial pursues two goals. First, it aims to investigate the efficacy of AppBM as an add-on to treatment-as-usual in a representative sample of adult smokers. Second, possible mechanisms of change are investigated. Methods The study is a randomized-controlled, double-blind, parallel-group superiority trial. We aim at a final sample of at least 336 adult smokers. Participants are allocated with a 1:1:1 allocation ratio to one of the following conditions: (1) treatment-as-usual + AppBM, (2) treatment-as-usual + Sham, (3) treatment-as-usual only. During the add-on training, participants are presented smoking-related and positive pictures and are instructed to respond by either pushing or pulling a joystick, depending on the tilt of the pictures (5 ○ to the left/right). During AppBM, all smoking-related pictures are tilted in the direction that is associated with pushing, thereby aiming to train an avoidance bias for smoking. All positive pictures are tilted in the direction associated with pulling. During Sham, the contingency is 50/50. Participants are assessed before and after the intervention and at a 6-month follow-up. The primary outcome is prolonged abstinence, and secondary outcomes include smoking-related variables and psychological distress. Additionally, the motivational significance of smoking-related stimuli (i.e., approach bias, valence) is assessed with different experimental tasks (Approach-Avoidance Task; Single Target Implicit Association Test) and psychophysiological measures. Discussion This is the first large-scale clinical trial investigating the efficacy of AppBM as an add-on in smokers including a TAU only condition. Additionally, it is the first study to systematically investigate potential mechanisms mediating the effects of treatment on clinical outcome