Multivariate varying coefficient model for functional responses

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1045 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Using MicroPET Imaging in Quantitative Verification of Acupuncture Effect in Ischemia Stroke Treatment

While acupuncture has survived several thousand years’ evolution of medical practice, its function still remains as a myth from the view point of modern medicine. Our goal in this paper is to quantitatively understand the function of acupuncture in ischemia stroke treatment. We carried out a comparative study using the Sprague Dawley rat animal model. We induced the focal cerebral ischemia in the rats using the middle cerebral artery occlusion (MCAO) procedure. For each rat from the real acupuncture group (n = 40), sham acupoint treatment group (n = 54), and blank control group (n = 16), we acquired 3-D FDG-microPET images at baseline, after MCAO, and after treatment (i.e., real acupuncture, sham acupoint treatment, or resting according to the group assignment), respectively. After verifying that the injured area is in the right hemisphere of the cerebral cortex in the brain by using magnetic resonance imaging(MRI) and triphenyl tetrazolium cchloride (TTC)-staining, we directly compared the glucose metabolism in the right hemisphere of each rat. We carried out t-test and permutation test on the image data. Both tests demonstrated that acupuncture had a more positive effect than non-acupoint stimulus and blank control (P < 0.025) in increasing the glucose metabolic level in the stroke-injured area in the brain, while there was no statistically significant difference between non-acupoint stimulus and blank control (P>0.15). The immediate positive effect of acupuncture over sham acupoint treatment and blank control is verified using our experiments. The long-term benefit of acupuncture needs to be further studied

4. La réflexion de Tocqueville sur les mœurs

En 1835, Alexis de Tocqueville, philosophe, historien et homme politique français, achève à l’âge de trente ans son ouvrage De la démocratie en Amérique, avant de publier en 1851 Souvenirs, somme de réflexions sur la révolution populaire de 1848 qui vient d’avoir lieu. En 1856, au crépuscule de sa vie, il publie L’Ancien Régime et la Révolution où il livre une analyse à la fois plus profonde et plus distanciée de la révolution de 1789, expliquant les raisons pour lesquelles le régime despotiq..

5. Civilisés ou barbares : l’Orient vu par un penseur du xixe siècle

Nous aimerions aborder dans la présente contribution la question de la civilisation et de la barbarie à travers les représentations de l’Orient chez les penseurs occidentaux du xxe siècle. Nous nous intéresserons plus particulièrement au cas d’un des penseurs anglais majeurs de cette époque, John Stuart Mill, afin de mieux saisir comment l’Orient était perçu par l’Occident. Cette question semble revêtir une importance plus grande encore depuis les réflexions et controverses suscitées dans les..

Optimal treatment allocation for efficient policy evaluation in sequential decision making

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimator

Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data

Nearest-Neighbor Sampling Based Conditional Independence Testing

The conditional randomization test (CRT) was recently proposed to test whether two random variables X and Y are conditionally independent given random variables Z. The CRT assumes that the conditional distribution of X given Z is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data. The aim of this paper is to develop a novel alternative of CRT by using nearest-neighbor sampling without assuming the exact form of the distribution of X given Z. Specifically, we utilize the computationally efficient 1-nearest-neighbor to approximate the conditional distribution that encodes the null hypothesis. Then, theoretically, we show that the distribution of the generated samples is very close to the true conditional distribution in terms of total variation distance. Furthermore, we take the classifier-based conditional mutual information estimator as our test statistic. The test statistic as an empirical fundamental information theoretic quantity is able to well capture the conditional-dependence feature. We show that our proposed test is computationally very fast, while controlling type I and II errors quite well. Finally, we demonstrate the efficiency of our proposed test in both synthetic and real data analyses.Comment: Accepted at AAAI 2023; 9 Pages, 3 Figures, 2 Table

Electroacupuncture at PC6 (Neiguan) Improves Extracellular Signal-Regulated Kinase Signaling Pathways Through the Regulation of Neuroendocrine Cytokines in Myocardial Hypertrophic Rats

Electroacupuncture (EA) therapy has been widely accepted as a useful therapeutic technique with low or no risk in the clinical prevention of cardiac hypertrophy. However, the signaling transduction mechanism underlying this effect remains unclear. The current study investigates the effects of EA on the signaling pathways of myocardial hypertrophy (MH) in rats. Up to 40 3-month-old Sprague-Dawley (SD) rats were randomly divided into normal, model, PC6 (Neiguan), and LI4 (Hegu) groups, with ten rats in each group. All the rats except for the normal group received 3 mg/kg·d of isoprinosine hydrochloride (ISO) injection into the back skin. The rats in the PC6 and LI4 groups received EA for 14 days. On the 15th day, electrocardiograms were recorded, and the ultrastructure of the myocardial cells was observed. The myocardial hypertrophy indices (MHIs), electrocardiograph (ECG), ultrastructure observation, levels of plasma angiotensin II (Ang II) and endothelin (ET), as well as protein expression of extracellular signal-regulated kinase (ERK), and phosphorylation extracellular signal regulating kinase (p-ERK) in the left ventricular myocardial tissue were measured. The results indicated that EA can improve cardiac function in MH rats by modulating upstream neuroendocrine cytokines that regulate the ERK signaling pathways

Two-stage empirical likelihood for longitudinal neuroimaging data

Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and the normal brain. The main objective of this paper is to develop a two-stage adjusted exponentially tilted empirical likelihood (TETEL) for the spatial analysis of neuroimaging data from longitudinal studies. The TETEL method as a frequentist approach allows us to efficiently analyze longitudinal data without modeling temporal correlation and to classify different time-dependent covariate types. To account for spatial dependence, the TETEL method developed here specifically combines all the data in the closest neighborhood of each voxel (or pixel) on a 3-dimensional (3D) volume (or 2D surface) with appropriate weights to calculate adaptive parameter estimates and adaptive test statistics. Simulation studies are used to examine the finite sample performance of the adjusted exponential tilted likelihood ratio statistic and TETEL. We demonstrate the application of our statistical methods to the detection of the difference in the morphological changes of the hippocampus across time between schizophrenia patients and healthy subjects in a longitudinal schizophrenia study.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS480 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Structural and Maturational Covariance in Early Childhood Brain Development

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development