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Tor D Wager - One of the best experts on this subject based on the ideXlab platform.

  • high dimensional multivariate mediation with application to Neuroimaging Data
    Biostatistics, 2018
    Co-Authors: Oliver Y Chen, Tor D Wager, Brian S Caffo, Ciprian M Crainiceanu, Elizabeth L Ogburn, Martin A Lindquist
    Abstract:

    Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.

  • high dimensional multivariate mediation with application to Neuroimaging Data
    arXiv: Methodology, 2015
    Co-Authors: Oliver Y Chen, Tor D Wager, Brian S Caffo, Ciprian M Crainiceanu, Elizabeth L Ogburn, Martin A Lindquist
    Abstract:

    Mediation analysis has become an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a randomized treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using structural equation models (SEMs), with model coefficients interpreted as possible effects. While there has been significant research on the topic in recent years, little work has been done on mediation analysis when the intermediate variable (mediator) is a high-dimensional vector. In this work we present a new method for exploratory mediation analysis in this setting called the directions of mediation (DMs). The first DM is defined as the linear combination of the elements of a high-dimensional vector of potential mediators that maximizes the likelihood of the SEM. The subsequent DMs are defined as linear combinations of the elements of the high-dimensional vector that are orthonormal to the previous DMs and maximize the likelihood of the SEM. We provide an estimation algorithm and establish the asymptotic properties of the obtained estimators. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of SNPs, or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging (fMRI) study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.

  • large scale automated synthesis of human functional Neuroimaging Data
    Nature Methods, 2011
    Co-Authors: Tal Yarkoni, Russell A. Poldrack, Thomas E Nichols, David C Van Essen, Tor D Wager
    Abstract:

    The rapid growth of the literature on Neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize Neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large Database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality Neuroimaging meta-analyses, address long-standing inferential problems in the Neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human Neuroimaging Data on an unprecedented scale.

  • large scale automated synthesis of human functional Neuroimaging Data
    Nature Methods, 2011
    Co-Authors: Tal Yarkoni, Russell A. Poldrack, Thomas E Nichols, David C Van Essen, Tor D Wager
    Abstract:

    A framework and web interface for the large-scale and automated synthesis of human Neuroimaging Data extracted from the literature is presented. It is used to generate a large Database of mappings between neural and cognitive states and to address long-standing inferential problems in the Neuroimaging literature.

  • meta analysis of functional Neuroimaging Data via bayesian spatial point processes
    Journal of the American Statistical Association, 2011
    Co-Authors: Jian Kang, Thomas E Nichols, Timothy D Johnson, Tor D Wager
    Abstract:

    As the discipline of functional Neuroimaging grows there is an increasing interest in meta analysis of brain imaging studies. A typical Neuroimaging meta analysis collects peak activation coordinates (foci) from several studies and identifies areas of consistent activation. Most imaging meta analysis methods only produce null hypothesis inferences and do not provide an interpretable fitted model. To overcome these limitations, we propose a Bayesian spatial hierarchical model using a marked independent cluster process. We model the foci as offspring of a latent study center process, and the study centers are in turn offspring of a latent population center process. The posterior intensity function of the population center process provides inference on the location of population centers, as well as the interstudy variability of foci about the population centers. We illustrate our model with a meta analysis consisting of 437 studies from 164 publications, show how two subpopulations of studies can be compared...

Hongtu Zhu - One of the best experts on this subject based on the ideXlab platform.

  • stgp spatio temporal gaussian process models for longitudinal Neuroimaging Data
    NeuroImage, 2016
    Co-Authors: Jung Won Hyun, Chao Huang, Martin Styner, Weili Lin, Hongtu Zhu
    Abstract:

    Longitudinal Neuroimaging Data plays an important role in mapping the neural developmental profile of major neuropsychiatric and neurodegenerative disorders and normal brain. The development of such developmental maps is critical for the prevention, diagnosis, and treatment of many brain-related diseases. The aim of this paper is to develop a spatio-temporal Gaussian process (STGP) framework to accurately delineate the developmental trajectories of brain structure and function, while achieving better prediction by explicitly incorporating the spatial and temporal features of longitudinal Neuroimaging Data. Our STGP integrates a functional principal component model (FPCA) and a partition parametric space-time covariance model to capture the medium-to-large and small-to-medium spatio-temporal dependence structures, respectively. We develop a three-stage efficient estimation procedure as well as a predictive method based on a kriging technique. Two key novelties of STGP are that it can efficiently use a small number of parameters to capture complex non-stationary and non-separable spatio-temporal dependence structures and that it can accurately predict spatio-temporal changes. We illustrate STGP using simulated Data sets and two real Data analyses including longitudinal positron emission tomography Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and longitudinal lateral ventricle surface Data from a longitudinal study of early brain development.

  • spatially varying coefficient model for Neuroimaging Data with jump discontinuities
    Journal of the American Statistical Association, 2014
    Co-Authors: Hongtu Zhu, Jianqing Fan, Linglong Kong
    Abstract:

    Motivated by recent work on studying massive imaging Data in various Neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional volume (or two-dimensional surface) with a set of covariates. Two stylized features of neuorimaging Data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account for these two features, SVCM includes a measurement model with multiple varying coefficient functions, a jumping surface model for each varying coefficient function, and a functional principal component model. We develop a three-stage estimation procedure to simultaneously estimate the varying coefficient functions and the spatial correlations. The estimation procedure includes a fast multiscale adaptive estimation and testing procedure to independently estimate each varying coefficient function, while preserving its edges among...

  • spatially varying coefficient model for Neuroimaging Data with jump discontinuities
    arXiv: Methodology, 2013
    Co-Authors: Hongtu Zhu, Jianqing Fan, Linglong Kong
    Abstract:

    Motivated by recent work on studying massive imaging Data in various Neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) with a set of covariates. Two key features of most neuorimaging Data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account for these two features, SVCM includes a measurement model with multiple varying coefficient functions, a jumping surface model for each varying coefficient function, and a functional principal component model. We develop a three-stage estimation procedure to simultaneously estimate the varying coefficient functions and the spatial correlations. The estimation procedure includes a fast multiscale adaptive estimation and testing procedure to independently estimate each varying coefficient function, while preserving its edges among different piecewise-smooth regions. We systematically investigate the asymptotic properties (e.g., consistency and asymptotic normality) of the multiscale adaptive parameter estimates. We also establish the uniform convergence rate of the estimated spatial covariance function and its associated eigenvalue and eigenfunctions. Our Monte Carlo simulation and real Data analysis have confirmed the excellent performance of SVCM.

  • tensor regression with applications in Neuroimaging Data analysis
    Journal of the American Statistical Association, 2013
    Co-Authors: Hua Zhou, Hongtu Zhu
    Abstract:

    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. Supplementary materials for this article are available online.

  • tensor regression with applications in Neuroimaging Data analysis
    arXiv: Methodology, 2012
    Co-Authors: Hua Zhou, Hongtu Zhu
    Abstract:

    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.

Klausrobert Muller - One of the best experts on this subject based on the ideXlab platform.

  • analyzing Neuroimaging Data through recurrent deep learning models
    Frontiers in Neuroscience, 2019
    Co-Authors: Armin W Thomas, Hauke R Heekeren, Klausrobert Muller, Wojciech Samek
    Abstract:

    The application of deep learning (DL) models to Neuroimaging Data poses several challenges, due to the high dimensionality, low sample size and complex temporo-spatial dependency structure of these Data. Even further, DL models often act as as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) Data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of Data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI Dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight’s ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.

  • analyzing Neuroimaging Data through recurrent deep learning models
    arXiv: Learning, 2018
    Co-Authors: Armin W Thomas, Hauke R Heekeren, Klausrobert Muller, Wojciech Samek
    Abstract:

    The application of deep learning (DL) models to Neuroimaging Data poses several challenges, due to the high dimensionality, low sample size and complex temporo-spatial dependency structure of these Datasets. Even further, DL models act as as black-box models, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) Data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates the fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of Data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI Dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.

  • analysis of multimodal Neuroimaging Data
    IEEE Reviews in Biomedical Engineering, 2011
    Co-Authors: Felix Biessmann, Sergey M Plis, Frank C Meinecke, Tom Eichele, Klausrobert Muller
    Abstract:

    Each method for imaging brain activity has technical or physiological limits. Thus, combinations of Neuroimaging modalities that can alleviate these limitations such as simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular. Multimodal imaging setups can take advantage of complementary views on neural activity and enhance our understanding about how neural information processing is reflected in each modality. However, dedicated analysis methods are needed to exploit the potential of multimodal methods. Many solutions to this Data integration problem have been proposed, which often renders both comparisons of results and the choice of the right method for the Data at hand difficult. In this review we will discuss different multimodal Neuroimaging setups, the advances achieved in basic research and clinical application and the methods used. We will provide a comprehensive overview of mathematical tools reoccurring in multimodal Neuroimaging studies for artifact removal, Data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.

Thomas E Nichols - One of the best experts on this subject based on the ideXlab platform.

  • fast and accurate modelling of longitudinal and repeated measures Neuroimaging Data
    NeuroImage, 2014
    Co-Authors: Bryan Guillaume, Xue Hua, Paul M Thompson, Lourens J Waldorp, Thomas E Nichols
    Abstract:

    Despite the growing importance of longitudinal Data in Neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry—the state of all equal variances and equal correlations—or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such Data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the “so-called” Sandwich Estimator (SwE) which accounts for the within-subject correlation existing in longitudinal Data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for Neuroimaging Data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal Dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE.

  • large scale automated synthesis of human functional Neuroimaging Data
    Nature Methods, 2011
    Co-Authors: Tal Yarkoni, Russell A. Poldrack, Thomas E Nichols, David C Van Essen, Tor D Wager
    Abstract:

    The rapid growth of the literature on Neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize Neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large Database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality Neuroimaging meta-analyses, address long-standing inferential problems in the Neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human Neuroimaging Data on an unprecedented scale.

  • large scale automated synthesis of human functional Neuroimaging Data
    Nature Methods, 2011
    Co-Authors: Tal Yarkoni, Russell A. Poldrack, Thomas E Nichols, David C Van Essen, Tor D Wager
    Abstract:

    A framework and web interface for the large-scale and automated synthesis of human Neuroimaging Data extracted from the literature is presented. It is used to generate a large Database of mappings between neural and cognitive states and to address long-standing inferential problems in the Neuroimaging literature.

  • meta analysis of functional Neuroimaging Data via bayesian spatial point processes
    Journal of the American Statistical Association, 2011
    Co-Authors: Jian Kang, Thomas E Nichols, Timothy D Johnson, Tor D Wager
    Abstract:

    As the discipline of functional Neuroimaging grows there is an increasing interest in meta analysis of brain imaging studies. A typical Neuroimaging meta analysis collects peak activation coordinates (foci) from several studies and identifies areas of consistent activation. Most imaging meta analysis methods only produce null hypothesis inferences and do not provide an interpretable fitted model. To overcome these limitations, we propose a Bayesian spatial hierarchical model using a marked independent cluster process. We model the foci as offspring of a latent study center process, and the study centers are in turn offspring of a latent population center process. The posterior intensity function of the population center process provides inference on the location of population centers, as well as the interstudy variability of foci about the population centers. We illustrate our model with a meta analysis consisting of 437 studies from 164 publications, show how two subpopulations of studies can be compared...

  • evaluating the consistency and specificity of Neuroimaging Data using meta analysis
    NeuroImage, 2009
    Co-Authors: Tor D Wager, Thomas E Nichols, Martin A Lindquist, Hedy Kober, Jared X Van Snellenberg
    Abstract:

    Making sense of a Neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of Neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.

Russell A. Poldrack - One of the best experts on this subject based on the ideXlab platform.

  • inferring mental states from Neuroimaging Data from reverse inference to large scale decoding
    Neuron, 2011
    Co-Authors: Russell A. Poldrack
    Abstract:

    A common goal of Neuroimaging research is to use imaging Data to identify the mental processes that are engaged when a subject performs a mental task. The use of reasoning from activation to mental functions, known as "reverse inference," has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question. In this Perspective, I outline the critique of informal reverse inference and describe a number of new developments that provide the ability to more formally test the predictive power of Neuroimaging Data.

  • large scale automated synthesis of human functional Neuroimaging Data
    Nature Methods, 2011
    Co-Authors: Tal Yarkoni, Russell A. Poldrack, Thomas E Nichols, David C Van Essen, Tor D Wager
    Abstract:

    The rapid growth of the literature on Neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize Neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large Database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality Neuroimaging meta-analyses, address long-standing inferential problems in the Neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human Neuroimaging Data on an unprecedented scale.

  • large scale automated synthesis of human functional Neuroimaging Data
    Nature Methods, 2011
    Co-Authors: Tal Yarkoni, Russell A. Poldrack, Thomas E Nichols, David C Van Essen, Tor D Wager
    Abstract:

    A framework and web interface for the large-scale and automated synthesis of human Neuroimaging Data extracted from the literature is presented. It is used to generate a large Database of mappings between neural and cognitive states and to address long-standing inferential problems in the Neuroimaging literature.

  • Can cognitive processes be inferred from Neuroimaging Data
    Trends in cognitive sciences, 2006
    Co-Authors: Russell A. Poldrack
    Abstract:

    There is much interest currently in using functional Neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap Neuroimaging Database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.