Access the full text.
Sign up today, get DeepDyve free for 14 days.
V. Chernozhukov, D. Chetverikov, Kengo Kato (2014)
Central limit theorems and bootstrap in high dimensionsAnnals of Probability, 45
M. Glasser, S. Sotiropoulos, J. Wilson, Timothy Coalson, B. Fischl, J. Andersson, Junqian Xu, S. Jbabdi, Matthew Webster, J. Polimeni, D. Essen, M. Jenkinson (2013)
The minimal preprocessing pipelines for the Human Connectome ProjectNeuroImage, 80
V. Chernozhukov, D. Chetverikov, Kengo Kato (2012)
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectorsAnnals of Statistics, 41
A. Lenartowicz, A. Mcintosh (2005)
The Role of Anterior Cingulate Cortex in Working Memory is Shaped by Functional ConnectivityJournal of Cognitive Neuroscience, 17
J. Binder, William Gross, J. Allendorfer, L. Bonilha, J. Chapin, J. Edwards, T. Grabowski, J. Langfitt, D. Loring, M. Lowe, K. Koenig, P. Morgan, J. Ojemann, C. Rorden, J. Szaflarski, M. Tivarus, K. Weaver (2011)
Mapping anterior temporal lobe language areas with fMRI: A multicenter normative studyNeuroImage, 54
H. Hotelling (1936)
Relations Between Two Sets of VariatesBiometrika, 28
Changbo Zhu, Shu Yao, Xianyang Zhang, X. Shao (2019)
Distance-based and RKHS-based dependence metrics in high dimensionarXiv: Statistics Theory
A. Drobyshevsky, S. Baumann, W. Schneider (2006)
A rapid fMRI task battery for mapping of visual, motor, cognitive, and emotional functionNeuroImage, 31
M. Naylor, V. Cardenas, D. Tosun, N. Schuff, M. Weiner, A. Schwartzman (2013)
Voxelwise multivariate analysis of multimodality magnetic resonance imagingHuman Brain Mapping, 35
Shubhadeep Chakraborty, Xianyang Zhang (2017)
Distance Metrics for Measuring Joint Dependence with Application to Causal InferenceJournal of the American Statistical Association, 114
L. Saulis, V. Statulevičius (1991)
Limit theorems for large deviations
Jonathan Power, A. Cohen, S. Nelson, G. Wig, K. Barnes, J. Church, Alecia Vogel, Timothy Laumann, F. Miezin, B. Schlaggar, S. Petersen (2011)
Functional Network Organization of the Human BrainNeuron, 72
L. Tyler, W. Marslen-Wilson, B. Randall, P. Wright, B. Devereux, J. Zhuang, M. Papoutsi, E. Stamatakis (2011)
Left inferior frontal cortex and syntax: function, structure and behaviour in patients with left hemisphere damageBrain, 134
Hang Deng, Cun-Hui Zhang (2017)
Beyond Gaussian approximation: Bootstrap for maxima of sums of independent random vectorsThe Annals of Statistics
V. Calhoun, T. Adalı, K. Kiehl, R. Astur, J. Pekar, G. Pearlson (2006)
A method for multitask fMRI data fusion applied to schizophreniaHuman Brain Mapping, 27
Shubhadeep Chakraborty, Xianyang Zhang (2019)
A new framework for distance and kernel-based metrics in high dimensionsElectronic Journal of Statistics
Fang Han, Shizhe Chen, Han Liu (2014)
Distribution-free tests of independence in high dimensionsBiometrika, 104
D. Essen, Stephen Smith, D. Barch, Timothy Behrens, E. Yacoub, K. Uğurbil (2013)
The WU-Minn Human Connectome Project: An overviewNeuroImage, 80
G. Sz'ekely, Maria Rizzo, N. Bakirov (2007)
Measuring and testing dependence by correlation of distancesAnnals of Statistics, 35
Liping Zhu, Kai Xu, Runze Li, Wei Zhong (2017)
Projection correlation between two random vectorsBiometrika, 104
Xianyang Zhang (2015)
Testing High Dimensional Mean Under SparsityarXiv: Methodology
M. Raichle (2015)
The brain's default mode network.Annual review of neuroscience, 38
Shu Yao, Xianyang Zhang, X. Shao (2016)
Testing mutual independence in high dimension via distance covarianceJournal of the Royal Statistical Society: Series B (Statistical Methodology), 80
Ze Jin, D. Matteson (2017)
Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statisticsJ. Multivar. Anal., 168
Jianqing Fan, Q. Shao, Wen-Xin Zhou (2015)
ARE DISCOVERIES SPURIOUS? DISTRIBUTIONS OF MAXIMUM SPURIOUS CORRELATIONS AND THEIR APPLICATIONS.Annals of statistics, 46 3
Weidong Liu (2013)
Gaussian graphical model estimation with false discovery rate controlAnnals of Statistics, 41
R. Heller, Yair Heller, M. Gorfine (2012)
A consistent multivariate test of association based on ranks of distancesBiometrika, 100
S. Taskinen, H. Oja, R. Randles (2005)
Multivariate Nonparametric Tests of IndependenceJournal of the American Statistical Association, 100
M. Jovanović, S. Gerhold (2017)
Probability Inequalities
Tommaso Cai, Weidong Liu, Yin Xia (2014)
Two‐sample test of high dimensional means under dependenceJournal of the Royal Statistical Society: Series B (Statistical Methodology), 76
J. Blum, J. Kiefer, M. Rosenblatt (1961)
DISTRIBUTION FREE TESTS OF INDEPENDENCE BASED ON THE SAMPLE DISTRIBUTION FUNCTIONAnnals of Mathematical Statistics, 32
Jinyuan Chang, Xiaohui Chen, Mingcong Wu (2021)
Central limit theorems for high dimensional dependent dataBernoulli
Sidong Liu, Weidong Cai, Siqi Liu, Fan Zhang, M. Fulham, D. Feng, Sonia Pujol, R. Kikinis (2015)
Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disordersBrain Informatics, 2
D. Essen, K. Uğurbil, E. Auerbach, D. Barch, Timothy Behrens, R. Bucholz, A. Chang, Liyong Chen, M. Corbetta, Sandra Curtiss, S. Penna, D. Feinberg, M. Glasser, N. Harel, A. Heath, L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. Petersen, F. Prior, B. Schlaggar, Stephen Smith, A. Snyder, Junqian Xu, E. Yacoub (2012)
The Human Connectome Project: A data acquisition perspectiveNeuroImage, 62
Hongjian Shi, M. Drton, Fang Han (2019)
Distribution-Free Consistent Independence Tests via Center-Outward Ranks and SignsJournal of the American Statistical Association, 117
Nabarun Deb, B. Sen (2019)
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure TransportationJournal of the American Statistical Association, 118
Mahdi Ramezani, Kris Marble, Heather Trang, I. Johnsrude, P. Abolmaesumi (2015)
Joint Sparse Representation of Brain Activity Patterns in Multi-Task fMRI DataIEEE Transactions on Medical Imaging, 34
Olivier Ledoit, Michael Wolf (2002)
Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample sizeQuality Engineering, 48
A. Routier, Arnaud Marcoux, Mauricio Melo, J. Guillon, Jorge Samper-Gonzlez, Junhao Wen, Simona Bottani, A. Guyot, Elina Thibeau-Sutre, M. Teichmann, M. Habert, S. Durrleman, N. Burgos, O. Colliot (2019)
New advances in the Clinica software platform for clinical neuroimaging studies
Dennis Leung, M. Drton (2015)
Testing independence in high dimensions with sums of rank correlationsarXiv: Statistics Theory
Jinyuan Chang, Chaowen Zheng, Wen-Xin Zhou, Wen Zhou (2014)
Simulation‐based hypothesis testing of high dimensional means under covariance heterogeneityBiometrics, 73
P. Gieser, R. Randles (1997)
A Nonparametric Test of Independence between Two VectorsJournal of the American Statistical Association, 92
James Schott (2005)
Testing for complete independence in high dimensionsBiometrika, 92
Thomas Berrett, Ioannis Kontoyiannis, R. Samworth (2020)
Optimal rates for independence testing via U-statistic permutation testsThe Annals of Statistics
Fulvia Castelli, F. Happé, U. Frith, C. Frith (2000)
Movement and Mind: A Functional Imaging Study of Perception and Interpretation of Complex Intentional Movement PatternsNeuroImage, 12
C. Spearman (1987)
The proof and measurement of association between two things. By C. Spearman, 1904.The American journal of psychology, 100 3-4
H. Nagao (1973)
On Some Test Criteria for Covariance MatrixAnnals of Statistics, 1
N. Nissim, A. O'Shea, Vaughn Bryant, E. Porges, R. Cohen, A. Woods (2017)
Frontal Structural Neural Correlates of Working Memory Performance in Older AdultsFrontiers in Aging Neuroscience, 8
Wicher Bergsma, A. Dassios (2010)
A consistent test of independence based on a sign covariance related to Kendall's tauarXiv: Statistics Theory
R. Spreng (2012)
The Fallacy of a “Task-Negative” NetworkFrontiers in Psychology, 3
Miles Lopes (2020)
Central limit theorem and bootstrap approximation in high dimensions: Near 1/n rates via implicit smoothingThe Annals of Statistics
Lan Gao, Yingying Fan, Jinchi Lv, Q. Shao (2019)
ASYMPTOTIC DISTRIBUTIONS OF HIGH-DIMENSIONAL DISTANCE CORRELATION INFERENCE.Annals of statistics, 49 4
Yin Xia, Lexin Li, S. Lockhart, W. Jagust (2019)
Simultaneous Covariance Inference for Multimodal Integrative AnalysisJournal of the American Statistical Association, 115
M. Hallin, E. Barrio, J. Cuesta-Albertos, C. Matrán (2021)
Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approachThe Annals of Statistics
C. Borell (1975)
The Brunn-Minkowski inequality in Gauss spaceInventiones mathematicae, 30
A. Gretton, K. Fukumizu, C. Teo, Le Song, B. Scholkopf, Alex Smola (2007)
A Kernel Statistical Test of Independence
Jinyuan Chang, Q. Shao, Wen-Xin Zhou (2014)
Cramér-type moderate deviations for Studentized two-sample $U$-statistics with applicationsAnnals of Statistics, 44
Z. Bai, Dandan Jiang, J. Yao, Shu-rong Zheng (2009)
Corrections to LRT on large-dimensional covariance matrix by RMTAnnals of Statistics, 37
N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, M. Joliot (2002)
Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject BrainNeuroImage, 15
S. Berman (1962)
A Law of Large Numbers for the Maximum in a Stationary Gaussian SequenceAnnals of Mathematical Statistics, 33
David Reshef, Yakir Reshef, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher, Pardis Sabeti (2011)
Detecting Novel Associations in Large Data SetsScience, 334
M. Kendall (1938)
A NEW MEASURE OF RANK CORRELATIONBiometrika, 30
Niklas Pfister, Peter Buhlmann, B. Scholkopf, J. Peters (2016)
Kernel‐based tests for joint independenceJournal of the Royal Statistical Society: Series B (Statistical Methodology), 80
B. Becker, Lucas Androsch, Ralph Jahn, Therese Alich, N. Striepens, S. Markett, W. Maier, R. Hurlemann (2013)
Inferior frontal gyrus preserves working memory and emotional learning under conditions of impaired noradrenergic signalingFrontiers in Behavioral Neuroscience, 7
D. Barch, G. Burgess, M. Harms, S. Petersen, B. Schlaggar, M. Corbetta, M. Glasser, Sandra Curtiss, S. Dixit, Cindy Feldt, D. Nolan, Edward Bryant, T. Hartley, Owen Footer, J. Bjork, R. Poldrack, Steve Smith, H. Johansen-Berg, A. Snyder, D. Essen (2013)
Function in the human connectome: Task-fMRI and individual differences in behaviorNeuroImage, 80
S. Wilks (1935)
On the Independence of k Sets of Normally Distributed Statistical VariablesEconometrica, 3
V. Chernozhukov, D. Chetverikov, Kengo Kato, Yuta Koike (2019)
Improved central limit theorem and bootstrap approximations in high dimensionsThe Annals of Statistics
K. Pearson (1920)
NOTES ON THE HISTORY OF CORRELATIONBiometrika, 13
Abstract Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this article, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data. Motivated by the analysis of multi-task fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence among imaging modalities over brain regions, (b) testing independence between brain regions within imaging modalities, and (c) testing independence between brain regions across different modalities. Considering a general form for all the three tests, we develop a global testing procedure and a multiple testing procedure controlling the false discovery rate. We study theoretical properties of the proposed tests and develop a computationally efficient distributed algorithm. The proposed methods and theory are general and relevant for many statistical problems of testing independence structure among the components of high-dimensional random vectors with arbitrary dependence structures. We also illustrate our proposed methods via extensive simulations and analysis of five task fMRI contrast maps in the HCP study. Supplementary materials for this article are available online.
Journal of the American Statistical Association – Taylor & Francis
Published: May 25, 2023
Keywords: FDR control; High-dimensional inference; Independence test; Multimodal neuroimaging
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.