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Liu Z, Peach RL, Laumann F, Vallejo Mengod S, Barahona M. Kernel-based joint independence tests for multivariate stationary and non-stationary time series. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230857. [PMID: 38034126 PMCID: PMC10685129 DOI: 10.1098/rsos.230857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
Multivariate time-series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the d-variable Hilbert-Schmidt independence criterion to encompass both stationary and non-stationary processes, thus allowing broader real-world applications. By leveraging resampling techniques tailored for both single- and multiple-realization time series, we show how the method robustly uncovers significant higher-order dependencies in synthetic examples, including frequency mixing data and logic gates, as well as real-world climate, neuroscience and socio-economic data. Our method adds to the mathematical toolbox for the analysis of multivariate time series and can aid in uncovering high-order interactions in data.
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Affiliation(s)
- Zhaolu Liu
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Robert L. Peach
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK
- Department of Neurology, University Hospital Würzburg, Würzburg 97070, Germany
| | - Felix Laumann
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
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2
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Motiwala Z, Sandholu AS, Sengupta D, Kulkarni K. Wavelet coherence phase analysis decodes the universal switching mechanism of Ras GTPase superfamily. iScience 2023; 26:107031. [PMID: 37448564 PMCID: PMC10336170 DOI: 10.1016/j.isci.2023.107031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 03/06/2023] [Accepted: 05/31/2023] [Indexed: 07/15/2023] Open
Abstract
The Ras superfamily of GTPases regulate critical cellular processes by shuttling between GTP-bound ON and GDP-bound OFF states. This switching mechanism is attributed to the conformational changes in two loops, SWI and SWII, upon GTP binding and hydrolysis. Since these conformational changes vary across the Ras superfamily, there is no generic parameter to define their functional states. A unique wavelet coherence (WC) analysis-based approach developed here shows that the structural changes in switch regions could be mapped onto the wavelet coherence phase couplings (WPCs). Thus, WPCs could serve as unique parameters to define their functional states. Disentanglement of WPCs in oncogenic GTPases shows how breakdown of structural allostery leads to their aberrant function. These observations stand out even for simulated ensemble of switch region conformers. Overall, for the first time, we show that WPCs could unravel the latent structural deviations in Ras proteins to decode their universal switching mechanism.
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Affiliation(s)
- Zenia Motiwala
- Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune 411008, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Anand S. Sandholu
- Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune 411008, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Durba Sengupta
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Division of Physical and Material Chemistry, CSIR-National Chemical Laboratory, Pune 411008, India
| | - Kiran Kulkarni
- Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune 411008, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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3
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Chung J, Varjavand B, Arroyo‐Relión J, Alyakin A, Agterberg J, Tang M, Priebe CE, Vogelstein JT. Valid two‐sample graph testing via optimal transport Procrustes and multiscale graph correlation with applications in connectomics. Stat (Int Stat Inst) 2022. [DOI: 10.1002/sta4.429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jaewon Chung
- Department of Biomedical Engineering Johns Hopkins University MD 21218 USA
| | - Bijan Varjavand
- Department of Biomedical Engineering Johns Hopkins University MD 21218 USA
| | | | - Anton Alyakin
- Department of Applied Mathematics and Statistics Johns Hopkins University MD 21218 USA
| | - Joshua Agterberg
- Department of Applied Mathematics and Statistics Johns Hopkins University MD 21218 USA
| | - Minh Tang
- Department of Statistics North Carolina State University NC 27695 USA
| | - Carey E. Priebe
- Department of Applied Mathematics and Statistics Johns Hopkins University MD 21218 USA
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4
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Abstract
Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data. To overcome the difficulty, in this paper we propose a chi-square test for distance correlation. Method-wise, the chi-square test is non-parametric, extremely fast, and applicable to bias-corrected distance correlation using any strong negative type metric or characteristic kernel. The test exhibits a similar testing power as the standard permutation test, and can be utilized for K-sample and partial testing. Theory-wise, we show that the underlying chi-square distribution well approximates and dominates the limiting null distribution in upper tail, prove the chi-square test can be valid and universally consistent for testing independence, and establish a testing power inequality with respect to the permutation test.
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Affiliation(s)
- Cencheng Shen
- Department of Applied Economics and Statistics, University of Delaware,
| | - Sambit Panda
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University
| | - Joshua T. Vogelstein
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University,Center for Imaging Science, Kavli Neuroscience Discovery Institute, Johns Hopkins University
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5
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Cai Z, Uji M, Aydin Ü, Pellegrino G, Spilkin A, Delaire É, Abdallah C, Lina J, Grova C. Evaluation of a personalized functional near infra-red optical tomography workflow using maximum entropy on the mean. Hum Brain Mapp 2021; 42:4823-4843. [PMID: 34342073 PMCID: PMC8449120 DOI: 10.1002/hbm.25566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 11/20/2022] Open
Abstract
In the present study, we proposed and evaluated a workflow of personalized near infra-red optical tomography (NIROT) using functional near-infrared spectroscopy (fNIRS) for spatiotemporal imaging of cortical hemodynamic fluctuations. The proposed workflow from fNIRS data acquisition to local 3D reconstruction consists of: (a) the personalized optimal montage maximizing fNIRS channel sensitivity to a predefined targeted brain region; (b) the optimized fNIRS data acquisition involving installation of optodes and digitalization of their positions using a neuronavigation system; and (c) the 3D local reconstruction using maximum entropy on the mean (MEM) to accurately estimate the location and spatial extent of fNIRS hemodynamic fluctuations along the cortical surface. The workflow was evaluated on finger-tapping fNIRS data acquired from 10 healthy subjects for whom we estimated the reconstructed NIROT spatiotemporal images and compared with functional magnetic resonance imaging (fMRI) results from the same individuals. Using the fMRI activation maps as our reference, we quantitatively compared the performance of two NIROT approaches, the MEM framework and the conventional minimum norm estimation (MNE) method. Quantitative comparisons were performed at both single subject and group-level. Overall, our results suggested that MEM provided better spatial accuracy than MNE, while both methods offered similar temporal accuracy when reconstructing oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentration changes evoked by finger-tapping. Our proposed complete workflow was made available in the brainstorm fNIRS processing plugin-NIRSTORM, thus providing the opportunity for other researchers to further apply it to other tasks and on larger populations.
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Affiliation(s)
- Zhengchen Cai
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Makoto Uji
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Giovanni Pellegrino
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Amanda Spilkin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Édouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Chifaou Abdallah
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Jean‐Marc Lina
- Département de Génie ElectriqueÉcole de Technologie SupérieureMontréalQuébecCanada
- Centre De Recherches En MathématiquesMontréalQuébecCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
- Centre De Recherches En MathématiquesMontréalQuébecCanada
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6
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Bridgeford EW, Wang S, Wang Z, Xu T, Craddock C, Dey J, Kiar G, Gray-Roncal W, Colantuoni C, Douville C, Noble S, Priebe CE, Caffo B, Milham M, Zuo XN, Vogelstein JT. Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics. PLoS Comput Biol 2021; 17:e1009279. [PMID: 34529652 PMCID: PMC8500408 DOI: 10.1371/journal.pcbi.1009279] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 10/08/2021] [Accepted: 07/14/2021] [Indexed: 11/25/2022] Open
Abstract
Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations-such as measurement error-as compared to systematic deviations-such as individual differences-are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.
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Affiliation(s)
| | - Shangsi Wang
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Zeyi Wang
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Ting Xu
- Child Mind Institute, New York, New York, United States of America
| | - Cameron Craddock
- Child Mind Institute, New York, New York, United States of America
| | - Jayanta Dey
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | | | - Carlo Colantuoni
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Stephanie Noble
- Yale University, New Haven, Connecticut, United States of America
| | - Carey E. Priebe
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Brian Caffo
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael Milham
- Child Mind Institute, New York, New York, United States of America
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Joshua T. Vogelstein
- Johns Hopkins University, Baltimore, Maryland, United States of America
- Progressive Learning, Baltimore, Maryland, United States of America
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7
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Nentwich M, Ai L, Madsen J, Telesford QK, Haufe S, Milham MP, Parra LC. Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI. Neuroimage 2020; 218:117001. [PMID: 32492509 PMCID: PMC7457369 DOI: 10.1016/j.neuroimage.2020.117001] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/21/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023] Open
Abstract
A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity'' -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.
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Affiliation(s)
- Maximilian Nentwich
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, New York, NY, USA
| | - Jens Madsen
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Michael P Milham
- Center for the Developing Brain, The Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Lucas C Parra
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA.
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8
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Charles AS, Falk B, Turner N, Pereira TD, Tward D, Pedigo BD, Chung J, Burns R, Ghosh SS, Kebschull JM, Silversmith W, Vogelstein JT. Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics. Annu Rev Neurosci 2020; 43:441-464. [PMID: 32283996 DOI: 10.1146/annurev-neuro-100119-110036] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.
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Affiliation(s)
- Adam S Charles
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; .,Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Benjamin Falk
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Nicholas Turner
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Talmo D Pereira
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA
| | - Daniel Tward
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Benjamin D Pedigo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Jaewon Chung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Randal Burns
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Justus M Kebschull
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; .,Stanford University, Palo Alto, California 94305, USA
| | - William Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; .,Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
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9
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Bahrami M, Laurienti PJ, Simpson SL. Analysis of brain subnetworks within the context of their whole-brain networks. Hum Brain Mapp 2019; 40:5123-5141. [PMID: 31441167 DOI: 10.1002/hbm.24762] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 07/24/2019] [Accepted: 08/05/2019] [Indexed: 12/17/2022] Open
Abstract
Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole-brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed-effects modeling framework that allows analyzing subnetworks within the context of their whole-brain networks, and show that it can better disentangle global (whole-brain) and local (subnetwork) differences when compared to standard t-test analyses. T-test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system-level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model-quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks-but for local, subnetwork-level hypotheses.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Biomedical Engineering, Virginia Tech - Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sean L Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, North Carolina
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10
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Lee Y, Shen C, Priebe CE, Vogelstein JT. Network dependence testing via diffusion maps and distance-based correlations. Biometrika 2019. [DOI: 10.1093/biomet/asz045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Summary
Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.
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Affiliation(s)
- Youjin Lee
- Center for Causal Inference, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| | - Cencheng Shen
- Department of Applied Economics and Statistics, University of Delaware, Newark, Delaware, U.S.A
| | - Carey E Priebe
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland, U.S.A
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland, U.S.A
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11
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Shen C, Priebe CE, Vogelstein JT. From Distance Correlation to Multiscale Graph Correlation. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2018.1543125] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Cencheng Shen
- Department of Applied Economics and Statistics, University of Delaware, Newark, DE
| | - Carey E. Priebe
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering and Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD
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