DOCSLIB.ORG

Local White Matter Architecture Defines Functional Brain Dynamics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Local White Matter Architecture Defines Functional Brain Dynamics 2018 IEEE International Conference on Systems, Man, and Cybernetics Local White Matter Architecture Defines Functional Brain Dynamics Yo Joong Choe* Sivaraman Balakrishnan Aarti Singh Kakao Dept. of Statistics and Data Science Machine Learning Dept. Seongnam-si, South Korea Carnegie Mellon University Carnegie Mellon University [email protected] Pittsburgh, PA, USA Pittsburgh, PA, USA [email protected] [email protected] Jean M. Vettel Timothy Verstynen U.S. Army Research Laboratory Dept. of Psychology and CNBC Aberdeen Proving Ground, MD, USA Carnegie Mellon University [email protected] Pittsburgh, PA, USA [email protected] Abstract—Large bundles of myelinated axons, called white the integrity of the myelin sheath is critical for synchroniz- matter, anatomically connect disparate brain regions together ing information transmission between distal brain areas [2], and compose the structural core of the human connectome. We fostering the ability of these networks to adapt over time recently proposed a method of measuring the local integrity along the length of each white matter fascicle, termed the local [4]. Thus, variability in the myelin sheath, as well as other connectome [1]. If communication efficiency is fundamentally cellular support mechanisms, would contribute to variability constrained by the integrity along the entire length of a white in functional coherence across the circuit. matter bundle [2], then variability in the functional dynamics To study the integrity of structural connectivity, we recently of brain networks should be associated with variability in the introduced the concept of the local connectome. This is local connectome. We test this prediction using two statistical ap- proaches that are capable of handling the high dimensionality of defined as the pattern of fiber systems (i.e., number of fibers, data. First, by performing statistical inference on distance-based orientation, and size) within a voxel, as well as immediate correlations, we show that similarity in the local connectome connectivity between adjacent voxels, that can be quantified between individuals is significantly correlated with similarity in using diffusion MRI (dMRI) by measuring the fiber-wise their patterns of functional connectivity. Second, by employing density of microscopic water diffusion within a voxel [1]. The variable selection using sparse canonical correlation analysis and cross-validation, we show that segments of the local connectome collection of these multi-fiber diffusion density measurements are predictive of certain patterns of functional brain dynamics. within all white matter voxels is termed the local connectome These results are consistent with the hypothesis that structural fingerprint (LCF). The LCF is a high-dimensional feature variability along axon bundles constrains communication be- vector that describes the unique configuration of the structural tween disparate brain regions. connectome along the segments of white matter pathways [5]. Index Terms—local connectome, structure-function relation- ship, high-dimensional statistics, canonical correlation analysis Thus, the LCF provides a diffusion-informed measure along the fascicles that supports inter-regional communication, rather than determining the start and end positions of a particular fiber bundle. I. INTRODUCTION Since the LCF measures the local integrity along white The function of macroscopic neural networks is constrained matter bundles that connect regions across the entire brain, by the integrity of structural connections between disparate it reflects the overall communication capacity of the brain [2]. regions. This form of long-distance (i.e., centimeters) commu- Hence, we expect to see that variations in the LCF should also nication relies on dense bundles of axons that are known as correlate with those in the dynamics of brain networks, mea- white matter [3]. To prevent degradation of action potentials sured by connectivity patterns in the resting-state functional across long distances, these fiber bundles are supported by MRI (fMRI). To formally validate this intuition, we employ the myelin sheath, non-neuronal glial cells that insulate axons statistical approaches to examine the following hypotheses: and facilitate communication along the fascicle. As a result, Hypothesis 1 Similarity in the LCF, between individuals, is associated with similarity in their functional connectivity The research was sponsored by the U.S. Army Research Laboratory, patterns measured with resting-state fMRI. including work under Cooperative Agreement Number W911NF-10-2-0022, Hypothesis 2 Variability in specific segments of the LCF is and the views espoused are not official policies of the U.S. Government. *Y. C. is currently on leave from Carnegie Mellon University, where most associated with patterns of functional connectivity in specific of this work was done. circuits. 2577-1655/18/$31.00 ©2018 IEEE 595 DOI 10.1109/SMC.2018.00110 TABLE I: Summary of Abbreviations and Notation Notation Definition LCF Local connectome fingerprint FCG Functional correlation graph HCP The Human Connectome Project (dataset) n Number of subjects (793) p Dimension of LCF vectors (433,386) q Dimension of FCG vectors (195,625) xi p-dimensional LCF vector of subject i yi q-dimensional FCG vector of subject i dX The scaled Euclidean distance between LCFs (1) Fig. 1: Pipeline for generating LCFs. See [5] for details. dY The Pearson correlation distance between FCGs (2) DX Scaled Euclidean distance matrix between n LCFs DY Pearson correlation distance matrix between n FCGs X n × p matrix containing the n LCFs as rows code for this analysis are publicly available on the same Y n × q matrix containing the n FCGs as rows website. ·1, ·2 The 1- and 2-norm of a real-valued vector Sn The permutation group on {1,...,n} A spin distribution function (SDF) sampling framework was used to provide a consistent set of directions to sample the magnitude of SDFs along axonal directions in the cerebral II. MATERIALS AND METHODS white matter. Since each voxel may have more than one axonal direction, multiple measurements were extracted from the We summarize our abbreviations and notation in Table I. SDF for voxels that contained crossing fibers, while a single A. Data Acquisition measurement was extracted for voxels with fibers in a single 1) Participants: We used publicly available dMRI and direction. The appropriate number of density measurements fMRI data from the S900 (2015) release of the Human from each voxel was sampled by the left-posterior-superior Connectome Project (HCP) [6], acquired by Washington Uni- voxel order and compiled into a sequence of scalar values. versity in St. Louis and the University of Minnesota. Out Gray matter was excluded using the ICBM-152 white matter of the 900 participants released, 841 genetically unrelated mask (MacConnell Brain Imaging Centre, McGill University, participants (370 male, ages 22-37, mean age 28.76) had Canada). The cerebellum was also excluded due to different viable dMRI datasets. Among them, n = 793 participants slice coverage in cerebellum across participants. Since had at least one viable resting-state fMRI measurement. Our the density measurement has arbitrary units, the LCF was analysis was restricted to this subsample. All data collection scaled to make the variance equal to 1 [5]. For each subject i =1,...,n i procedures were approved by the institutional review boards , we denote this high-dimensional LCF of the th p = 433, 386 x ∈ Rp at Washington University in St. Louis and the University subject, across sampled directions, as i . n of Minnesota. The post hoc data analysis was approved as The collection of all LCFs are compactly represented as a X =[x ,...,x ]T ∈ Rn×p exempt by the institutional review board at Carnegie Mellon data matrix 1 n with each LCF as a University, in accordance with 45 CFR 46.101(b)(4) (IRB row vector. Protocol Number: HS14-139). 2) Diffusion MRI Acquisition: The dMRI data were ac- 4) Functional MRI Acquisition & Processing: We analyzed quired on a Siemens 3T Skyra scanner using a 2D spin-echo the minimally processed resting-state fMRI data acquired as single-shot multiband EPI sequence with a multi-band factor part of the Human Connectome Project (HCP) [8], [6] which of 3 and monopolar gradient pulse. The spatial resolution was used a multi-band gradient echo-planar imaging protocol (see 1.25 mm isotropic (TR = 5500 ms, TE = 89.50 ms). The b- [9] for details on aquisition parameters). The dataset con- values were 1000, 2000, and 3000 s/mm2 . The total number tains volumetric NIFTI data for resting-state fMRI scans (14 of diffusion sampling directions was 90 for each of the three minutes each), motion parameters, and physiological data. shells in addition to 6 b0 images. The total scanning time was Only data for the first resting-state scan collected at the A- approximately 55 minutes. P phase encoding direction were used for analyses. Using 3) LCF Reconstruction: An outline of the pipeline for these measurements, we computed the average BOLD (blood- generating LCFs is shown in Fig. 1. The dMRI data for oxygen-level dependent) signals at each of the 626 regions each subject was reconstructed in a common stereotaxic space of interest (ROIs) [10] and regressed out the linear effects using q-space diffeomorphic reconstruction (QSDR) [7], a of the noise terms via ordinary least-squares (OLS). The 16 nonlinear registration approach that directly reconstructs water noise terms include the global signal, 12 motion parameters (6 diffusion density patterns into a common stereotaxic space estimates from a rigid-body transformation to the SBRef image at 1 mm resolution. The LCF reconstruction was conducted acquired at the start of each scan; 6 temporal derivatives of using DSI Studio (http://dsi-studio.labsolver.org), an open- these estimates), and the top-3 principal component projections source diffusion MRI analysis tool for connectome analysis. of the voxel-level white matter signals (measured at each of To compute the LCF, the axonal direction in each voxel was 2,258 voxels and 840 seconds).
Load more

Recommended publications
  • Testing the Causal Direction of Mediation Effects in Randomized Intervention Studies
    Prevention Science (2019) 20:419–430 https://doi.org/10.1007/s11121-018-0900-y Testing the Causal Direction of Mediation Effects in Randomized Intervention Studies Wolfgang Wiedermann1 & Xintong Li1 & Alexander von Eye2 Published online: 21 May 2018 # Society for Prevention Research 2018 Abstract In a recent update of the standards for evidence in research on prevention interventions, the Society of Prevention Research emphasizes the importance of evaluating and testing the causal mechanism through which an intervention is expected to have an effect on an outcome. Mediation analysis is commonly applied to study such causal processes. However, these analytic tools are limited in their potential to fully understand the role of theorized mediators. For example, in a design where the treatment x is randomized and the mediator (m) and the outcome (y) are measured cross-sectionally, the causal direction of the hypothesized mediator-outcome relation is not uniquely identified. That is, both mediation models, x → m → y or x → y → m, may be plausible candidates to describe the underlying intervention theory. As a third explanation, unobserved confounders can still be responsible for the mediator-outcome association. The present study introduces principles of direction dependence which can be used to empirically evaluate these competing explanatory theories. We show that, under certain conditions, third higher moments of variables (i.e., skewness and co-skewness) can be used to uniquely identify the direction of a mediator-outcome relation. Significance procedures compatible with direction dependence are introduced and results of a simulation study are reported that demonstrate the performance of the tests. An empirical example is given for illustrative purposes and a software implementation of the proposed method is provided in SPSS.
    [Show full text]
  • From Distance Correlation to Multiscale Graph Correlation Arxiv
    From Distance Correlation to Multiscale Graph Correlation Cencheng Shen∗1, Carey E. Priebey2, and Joshua T. Vogelsteinz3 1Department of Applied Economics and Statistics, University of Delaware 2Department of Applied Mathematics and Statistics, Johns Hopkins University 3Department of Biomedical Engineering and Institute of Computational Medicine, Johns Hopkins University October 2, 2018 Abstract Understanding and developing a correlation measure that can detect general de- pendencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation — a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments — to the Multiscale Graph Correla- tion (MGC). By utilizing the characteristic functions and incorporating the nearest neighbor machinery, we formalize the population version of local distance correla- tions, define the optimal scale in a given dependency, and name the optimal local correlation as MGC. The new theoretical framework motivates a theoretically sound ∗[email protected] arXiv:1710.09768v3 [stat.ML] 30 Sep 2018 [email protected] [email protected] 1 Sample MGC and allows a number of desirable properties to be proved, includ- ing the universal consistency, convergence and almost unbiasedness of the sample version. The advantages of MGC are illustrated via a comprehensive set of simula- tions with linear, nonlinear, univariate, multivariate, and noisy dependencies, where it loses almost no power in monotone dependencies while achieving better perfor- mance in general dependencies, compared to distance correlation and other popular methods.
    [Show full text]
  • A Comparative Study of Statistical Methods Used to Identify
    Briefings in Bioinformatics Advance Access published August 20, 2013 BRIEFINGS IN BIOINFORMATICS. page1of13 doi:10.1093/bib/bbt051 A comparative study of statistical methods used to identify dependencies between gene expression signals Suzana de Siqueira Santos, Daniel YasumasaTakahashi, Asuka Nakata and Andre¤Fujita Submitted: 18th April 2013; Received (in revised form): 10th June 2013 Abstract One major task in molecular biology is to understand the dependency among genes to model gene regulatory net- works. Pearson’s correlation is the most common method used to measure dependence between gene expression signals, but it works well only when data are linearly associated. For other types of association, such as non-linear Downloaded from or non-functional relationships, methods based on the concepts of rank correlation and information theory-based measures are more adequate than the Pearson’s correlation, but are less used in applications, most probably because of a lack of clear guidelines for their use. This work seeks to summarize the main methods (Pearson’s, Spearman’s and Kendall’s correlations; distance correlation; Hoeffding’s D measure; Heller^Heller^Gorfine measure; mutual in- http://bib.oxfordjournals.org/ formation and maximal information coefficient) used to identify dependency between random variables, especially gene expression data, and also to evaluate the strengths and limitations of each method. Systematic Monte Carlo simulation analyses ranging from sample size, local dependence and linear/non-linear and also non-functional relationships are shown. Moreover, comparisons in actual gene expression data are carried out. Finally, we provide a suggestive list of methods that can be used for each type of data set.
    [Show full text]
  • From Distance Correlation to Multiscale Graph Correlation
    From Distance Correlation to Multiscale Graph Correlation Cencheng Shen∗1, Carey E. Priebey2, and Joshua T. Vogelsteinz3 1Department of Applied Economics and Statistics, University of Delaware 2Department of Applied Mathematics and Statistics, Johns Hopkins University 3Department of Biomedical Engineering and Institute of Computational Medicine, Johns Hopkins University January 19, 2019 Abstract Understanding and developing a correlation measure that can detect general de- pendencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation — a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments — to the Multiscale Graph Correla- tion (MGC). By utilizing the characteristic functions and incorporating the nearest neighbor machinery, we formalize the population version of local distance correla- tions, define the optimal scale in a given dependency, and name the optimal local correlation as MGC. The new theoretical framework motivates a theoretically sound ∗[email protected] [email protected] [email protected] 1 Sample MGC and allows a number of desirable properties to be proved, includ- ing the universal consistency, convergence and almost unbiasedness of the sample version. The advantages of MGC are illustrated via a comprehensive set of simula- tions with linear, nonlinear, univariate, multivariate, and noisy dependencies, where it loses almost no power in monotone dependencies while achieving better perfor- mance in general dependencies, compared to distance correlation and other popular methods. Keywords: testing independence, generalized distance correlation, nearest neighbor graph 2 1 Introduction p q Given pairs of observations (xi; yi) 2 R ×R for i = 1; : : : ; n, assume they are generated by independently identically distributed (iid) FXY .
    [Show full text]
  • University of Aberdeen the Distance Standard Deviation Edelmann, Dominic; Richards, Donald ; Vogel, Daniel
    University of Aberdeen The Distance Standard Deviation Edelmann, Dominic; Richards, Donald ; Vogel, Daniel Publication date: 2017 Document Version Other version Link to publication Citation for published version (APA): Edelmann, D., Richards, D., & Vogel, D. (2017). The Distance Standard Deviation. (pp. 1-27). ArXiv. https://arxiv.org/abs/1705.05777 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 The Distance Standard Deviation Dominic Edelmann,∗ Donald Richards,y and Daniel Vogelz May 17, 2017 Abstract The distance standard deviation, which arises in distance correlation analy- sis of multivariate data, is studied as a measure of spread. New representations for the distance standard deviation are obtained in terms of Gini's mean differ- ence and in terms of the moments of spacings of order statistics. Inequalities for the distance variance are derived, proving that the distance standard deviation is bounded above by the classical standard deviation and by Gini's mean difference.
    [Show full text]
  • Comparative Evaluation of Statistical Dependence Measures
    University of Arkansas, Fayetteville ScholarWorks@UARK Theses and Dissertations 12-2020 Comparative Evaluation of Statistical Dependence Measures Eman Abdel Rahman Ibrahim University of Arkansas, Fayetteville Follow this and additional works at: https://scholarworks.uark.edu/etd Part of the Applied Statistics Commons Citation Ibrahim, E. A. (2020). Comparative Evaluation of Statistical Dependence Measures. Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3903 This Thesis is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [email protected]. Comparative Evaluation of Statistical Dependence Measures A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Statistics and Analytics by Eman Abdel Rahman Ibrahim University of Arkansas Bachelor of Arts in Mathematics, 2015 December 2020 University of Arkansas This thesis is approved for recommendation to the Graduate Council. ______________________________ Qingyang Zhang, PhD. Thesis Director _______________________________ _____________________________ Mark E. Arnold, PhD. Giovanni Petris, PhD. Committee Member Committee Member Abstract Measuring and testing dependence between random variables is of great importance in many scientific fields. In the case of linearly correlated variables, Pearson’s correlation coefficient is a commonly used measure of the correlation
    [Show full text]
  • Measuring and Testing Dependence by Correlation of Distances
    The Annals of Statistics 2007, Vol. 35, No. 6, 2769–2794 DOI: 10.1214/009053607000000505 c Institute of Mathematical Statistics, 2007 MEASURING AND TESTING DEPENDENCE BY CORRELATION OF DISTANCES By Gabor´ J. Szekely,´ 1 Maria L. Rizzo and Nail K. Bakirov Bowling Green State University, Bowling Green State University and USC Russian Academy of Sciences Distance correlation is a new measure of dependence between ran- dom vectors. Distance covariance and distance correlation are anal- ogous to product-moment covariance and correlation, but unlike the classical definition of correlation, distance correlation is zero only if the random vectors are independent. The empirical distance depen- dence measures are based on certain Euclidean distances between sample elements rather than sample moments, yet have a compact representation analogous to the classical covariance and correlation. Asymptotic properties and applications in testing independence are discussed. Implementation of the test and Monte Carlo results are also presented. 1. Introduction. Distance correlation provides a new approach to the problem of testing the joint independence of random vectors. For all dis- tributions with finite first moments, distance correlation generalizes the idea of correlation in two fundamental ways: R (i) (X,Y ) is defined for X and Y in arbitrary dimensions; (ii) R(X,Y ) = 0 characterizes independence of X and Y . R Distance correlation has properties of a true dependence measure, analogous to product-moment correlation ρ. Distance correlation satisfies 0 1, and = 0 only if X and Y are independent. In the bivariate normal≤R≤ case, isR a function of ρ, and (X,Y ) ρ(X,Y ) with equality when ρ = 1.
    [Show full text]
  • The Perfect Marriage and Much More: Combining Dimension Reduction, Distance Measures and Covariance
    The Perfect Marriage and Much More: Combining Dimension Reduction, Distance Measures and Covariance Ravi Kashyap SolBridge International School of Business / City University of Hong Kong July 15, 2019 Keywords: Dimension Reduction; Johnson-Lindenstrauss; Bhattacharyya; Distance Measure; Covariance; Distribution; Uncertainty JEL Codes: C44 Statistical Decision Theory; C43 Index Numbers and Aggregation; G12 Asset Pricing Mathematics Subject Codes: 60E05 Distributions: general theory; 62-07 Data analysis; 62P20 Applications to economics Edited Version: Kashyap, R. (2019). The perfect marriage and much more: Combining dimension reduction, distance measures and covariance. Physica A: Statistical Mechanics and its Applications, XXX, XXX-XXX. Contents 1 Abstract 4 2 Introduction 4 3 Literature Review of Methodological Fundamentals 5 3.1 Bhattacharyya Distance . .5 3.2 Dimension Reduction . .9 4 Intuition for Dimension Reduction 10 arXiv:1603.09060v6 [q-fin.TR] 12 Jul 2019 4.1 Game of Darts . 10 4.2 The Merits and Limits of Four Physical Dimensions . 11 5 Methodological Innovations 11 5.1 Normal Log-Normal Mixture . 11 5.2 Normal Normal Product . 13 5.3 Truncated Normal / Multivariate Normal . 15 5.4 Discrete Multivariate Distribution . 19 5.5 Covariance and Distance . 20 1 6 Market-structure, Microstructure and Other Applications 24 6.1 Asset Pricing Application . 25 6.2 Biological Application . 26 7 Empirical Illustrations 27 7.1 Comparison of Security Prices across Markets . 27 7.2 Comparison of Security Trading Volumes, High-Low-Open-Close Prices and Volume-Price Volatilities . 30 7.3 Speaking Volumes Of: Comparison of Trading Volumes . 30 7.4 A Pricey Prescription: Comparison of Prices (Open, Close, High and Low) .
    [Show full text]
  • Distance Correlation Coefficient
    Journal of Modern Applied Statistical Methods Volume 13 | Issue 1 Article 23 5-1-2014 Distance Correlation Coefficient: An Application with Bayesian Approach in Clinical Data Analysis Atanu Bhattacharjee Malabar Cancer Centre, Kerala, India, [email protected] Follow this and additional works at: http://digitalcommons.wayne.edu/jmasm Part of the Applied Statistics Commons, Social and Behavioral Sciences Commons, and the Statistical Theory Commons Recommended Citation Bhattacharjee, Atanu (2014) "Distance Correlation Coefficient: An Application with Bayesian Approach in Clinical Data Analysis," Journal of Modern Applied Statistical Methods: Vol. 13 : Iss. 1 , Article 23. DOI: 10.22237/jmasm/1398918120 Available at: http://digitalcommons.wayne.edu/jmasm/vol13/iss1/23 This Regular Article is brought to you for free and open access by the Open Access Journals at DigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an authorized editor of DigitalCommons@WayneState. Journal of Modern Applied Statistical Methods Copyright © 2014 JMASM, Inc. May 2014, Vol. 13, No. 1, 354-366. ISSN 1538 − 9472 Distance Correlation Coefficient: An Application with Bayesian Approach in Clinical Data Analysis Atanu Bhattacharjee Malabar Cancer Centre Kerala, India The distance correlation coefficient – based on the product-moment approach – is one method by which to explore the relationship between variables. The Bayesian approach is a powerful tool to determine statistical inferences with credible intervals. Prior information about the relationship between BP and Serum cholesterol was applied to formulate the distance correlation between the two variables. The conjugate prior is considered to formulate the posterior estimates of the distance correlations. The illustrated method is simple and is suitable for other experimental studies.
    [Show full text]
  • Package 'Multivariance'
    Package ‘multivariance’ December 10, 2020 Title Measuring Multivariate Dependence Using Distance Multivariance Version 2.4.0 Date 2020-12-10 Description Distance multivariance is a measure of dependence which can be used to detect and quantify dependence of arbitrarily many random vectors. The necessary functions are implemented in this packages and examples are given. It includes: distance multivariance, distance multicorrelation, dependence structure detection, tests of independence and copula versions of distance multivariance based on the Monte Carlo empirical transform. Detailed references are given in the package description, as starting point for the theoretic background we refer to: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), <doi:10.1515/stat-2020-0001>. Depends R (>= 3.3.0) License GPL-3 Encoding UTF-8 LazyData true Imports igraph, graphics, stats, Rcpp, microbenchmark RoxygenNote 7.1.1 Suggests testthat LinkingTo Rcpp NeedsCompilation yes Author Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb] Maintainer Björn Böttcher <[email protected]> Repository CRAN Date/Publication 2020-12-10 15:50:03 UTC 1 2 R topics documented: R topics documented: multivariance-package . .3 anscombe.extended . .5 cdm .............................................6 cdms.............................................7 clean.graph . .8 coins . .9 copula.multicorrelation . 10 copula.multicorrelation.test . 11 copula.multivariance . 11 dependence.structure . 12 dep_struct_iterated_13_100 . 15 dep_struct_ring_15_100 . 16 dep_struct_several_26_100 . 17 dep_struct_star_9_100 . 17 emp.transf . 18 fastdist . 18 fastEuclideanCdm . 19 find.cluster . 19 independence.test . 21 layout_on_circles . 22 m.multivariance . 23 multicorrelation . 24 multivariance . 27 multivariance.pvalue . 28 multivariance.test . 29 multivariance.timing . 31 multivariances.all .
    [Show full text]
  • Causal Inference on Discrete Data Via Estimating Distance Correlations
    1 Causal Inference on Discrete Data via Estimating Distance Correlations Furui Liu, Laiwan Chan Department of Computer Science and Engineering, The Chinese University of Hong Kong Abstract In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause P (X) and the condi- tional distribution mapping cause to effect P (Y jX) as independent random variables, we propose to infer the causal direction via comparing the distance correlation between P (X) and P (Y jX) with the distance correlation between P (Y ) and P (XjY ). We in- fer “X causes Y ” if the dependence coefficient between P (X) and P (Y jX) is smaller. Experiments are performed to show the performance of the proposed method. Editor: Aapo Hyvarinen¨ 1 Introduction Inferring the causal direction between two variables from observational data becomes arXiv:1803.07712v3 [stat.ML] 7 Aug 2018 a hot research topic. Additive noise models (ANMs) (Zhang and Hyvarinen,¨ 2009a; Shimizu et al., 2006; Hoyer et al., 2009; Peters et al., 2011; Hoyer and Hyttinen, 2009; Zhang and Hyvarinen,¨ 2009b; Shimizu et al., 2011; Hyvarinen¨ and Smith, 2013; Hyvarinen¨ et al., 2010; Hyttinen et al., 2012; Mooij et al., 2011) are preliminary trials to solve this problem. They assume that the effect is governed by the cause and an ad- ditive noise, and the causal inference is done by finding the direction that admits such a model. Recently, under another view of exploiting the asymmetry between cause and effect, the linear trace method (LTr) (Zscheischler et al., 2011; Janzing et al., 2010) and information geometric causal inference (IGCI) (Janzing et al., 2012) are proposed.
    [Show full text]
  • Lecture 2: Measures of Correlation and Dependence 4Mm Foundations of Data Science
    1 Lecture 2: Measures of Correlation and Dependence Foundations of Data Science: Algorithms and Mathematical Foundations Mihai Cucuringu [email protected] CDT in Mathematics of Random System University of Oxford 2 Measures of Correlation/Dependence I Pearson I Spearman I Kendall I Hoeffding’s I Maximal Correlation I Distance Correlation I Mutual Information I Maximal Information Coefficient (MIC) 3 Review of correlation measures Pearson correlation ρ is a measure of linear dependence between variables. I In the population: given random variables X; Y 2 R Cov(X; Y ) Cor(X; Y ) = pVar(X)pVar(Y ) I In the sample: given vectors x; y 2 R cov(x; y) (x − x¯1)T (y − y¯1) cor(x; y) = p p = var(x) var(y) jjx − x¯1jj2jjy − y¯1jj2 If x; y are have been centered xT y cor(x; y) = jjxjj2jjyjj2 4 Properties of the population correlation Cov(X; Y ) ρ =def Cor(X; Y ) = pVar(X)pVar(Y ) Properties of ρ I Cor(X; X) = 1 I Cor(X; Y ) = Cor(Y ; X) I Cor(aX + b; Y ) = sign(a)Cor(X; Y ) for any a; b 2 R I −1 ≤ Cor(X; Y ) ≤ 1 I jCor(X; Y )j = 1 if and only if Y = aX + b for some a; b 2 R, with a 6= 0 I If X; Y are independent then Cor(X; Y ) = 0 I If Cor(X; Y ) = 0 then X; Y need not be independent!!! I If (X; Y ) is bivariate normal and Cor(X; Y ) = 0,then X; Y are independent 5 Bivariate normal distribution Two-dimensional Gaussian distribution The random vector Z = (X; Y ) 2 R2 has a bivariate normal dist.
    [Show full text]