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Brian Caffo

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14 papers

2 author rows

YNIMG Journal 2023 Journal Article

A machine learning based approach towards high-dimensional mediation analysis

Tanmay Nath
Brian Caffo
Tor Wager
Martin A. Lindquist

Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome.

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YNICL Journal 2019 Journal Article

“The effect of tDCS on functional connectivity in primary progressive aphasia” NeuroImage: Clinical, volume 19 (2018), pages 703–715

Bronte N. Ficek
Zeyi Wang
Yi Zhao
Kimberly T. Webster
John E. Desmond
Argye E. Hillis
Constantine Frangakis
Andreia Vasconcellos Faria

Transcranial direct current stimulation (tDCS) is an innovative technique recently shown to improve language outcomes even in neurodegenerative conditions such as primary progressive aphasia (PPA), but the underlying brain mechanisms are not known. The present study tested whether the additional language gains with repetitive tDCS (over sham) in PPA are caused by changes in functional connectivity between the stimulated area (the left inferior frontal gyrus (IFG)) and the rest of the language network. We scanned 24 PPA participants (11 female) before and after language intervention (written naming/spelling) with a resting-state fMRI sequence and compared changes before and after three weeks of tDCS or sham coupled with language therapy. We correlated changes in the language network as well as in the default mode network (DMN) with language therapy outcome measures (letter accuracy in written naming). Significant tDCS effects in functional connectivity were observed between the stimulated area and other language network areas and between the language network and the DMN. TDCS over the left IFG lowered the connectivity between the above pairs. Changes in functional connectivity correlated with improvement in language scores (letter accuracy as a proxy for written naming) evaluated before and after therapy. These results suggest that one mechanism for anodal tDCS over the left IFG in PPA is a decrease in functional connectivity (compared to sham) between the stimulated site and other posterior areas of the language network. These results are in line with similar decreases in connectivity observed after tDCS over the left IFG in aging and other neurodegenerative conditions.

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YNICL Journal 2018 Journal Article

The effect of tDCS on functional connectivity in primary progressive aphasia

Bronte N. Ficek
Zeyi Wang
Yi Zhao
Kimberly T. Webster
John E. Desmond
Argye E. Hillis
Constantine Frangakis
Andreia Vasconcellos Faria

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YNIMG Journal 2017 Journal Article

Comparing test-retest reliability of dynamic functional connectivity methods

Ann S. Choe
Mary Beth Nebel
Anita D. Barber
Jessica R. Cohen
Yuting Xu
James J. Pekar
Brian Caffo
Martin A. Lindquist

Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC (“brain states”). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i. e. , its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.

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YNIMG Journal 2015 Journal Article

Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators

Amanda F. Mejia
Mary Beth Nebel
Haochang Shou
Ciprian M. Crainiceanu
James J. Pekar
Stewart Mostofsky
Brian Caffo
Martin A. Lindquist

A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest. For example, brain parcellation is often a necessary step for defining the network nodes used in connectivity studies. While inference has traditionally been performed on group-level data, there is a growing interest in parcellating single subject data. However, this is difficult due to the inherent low signal-to-noise ratio of rsfMRI data, combined with typically short scan lengths. A large number of brain parcellation approaches employ clustering, which begins with a measure of similarity or distance between voxels. The goal of this work is to improve the reproducibility of single-subject parcellation using shrinkage-based estimators of such measures, allowing the noisy subject-specific estimator to “borrow strength” in a principled manner from a larger population of subjects. We present several empirical Bayes shrinkage estimators and outline methods for shrinkage when multiple scans are not available for each subject. We perform shrinkage on raw inter-voxel correlation estimates and use both raw and shrinkage estimates to produce parcellations by performing clustering on the voxels. While we employ a standard spectral clustering approach, our proposed method is agnostic to the choice of clustering method and can be used as a pre-processing step for any clustering algorithm. Using two datasets — a simulated dataset where the true parcellation is known and is subject-specific and a test–retest dataset consisting of two 7-minute resting-state fMRI scans from 20 subjects — we show that parcellations produced from shrinkage correlation estimates have higher reliability and validity than those produced from raw correlation estimates. Application to test–retest data shows that using shrinkage estimators increases the reproducibility of subject-specific parcellations of the motor cortex by up to 30%.

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ICML Conference 2015 Conference Paper

Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes

Huitong Qiu
Sheng Xu
Fang Han
Han Liu 0001
Brian Caffo

Gaussian vector autoregressive (VAR) processes have been extensively studied in the literature. However, Gaussian assumptions are stringent for heavy-tailed time series that frequently arises in finance and economics. In this paper, we develop a unified framework for modeling and estimating heavy-tailed VAR processes. In particular, we generalize the Gaussian VAR model by an elliptical VAR model that naturally accommodates heavy-tailed time series. Under this model, we develop a quantile-based robust estimator for the transition matrix of the VAR process. We show that the proposed estimator achieves parametric rates of convergence in high dimensions. This is the first work in analyzing heavy-tailed high dimensional VAR processes. As an application of the proposed framework, we investigate Granger causality in the elliptical VAR process, and show that the robust transition matrix estimator induces sign-consistent estimators of Granger causality. The empirical performance of the proposed methodology is demonstrated by both synthetic and real data. We show that the proposed estimator is robust to heavy tails, and exhibit superior performance in stock price prediction.

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NeurIPS Conference 2015 Conference Paper

Robust Portfolio Optimization

Huitong Qiu
Fang Han
Han Liu
Brian Caffo

We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle optimal risk with parametric rate under weakly dependent asset returns. The theory does not rely on higher order moment assumptions, thus allowing for heavy-tailed asset returns. Moreover, the rate of convergence quantifies that the size of the portfolio under management is allowed to scale exponentially with the sample size of the historical data. The empirical effectiveness of the proposed method is demonstrated under both synthetic and real stock data. Our work extends existing ones by achieving robustness in high dimensions, and by allowing serial dependence.

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YNIMG Journal 2014 Journal Article

Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI

Haochang Shou
Ani Eloyan
Mary Beth Nebel
Amanda Mejia
James J. Pekar
Stewart Mostofsky
Brian Caffo
Martin A. Lindquist

Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan–rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject's connectivity than the individual's own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis.

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YNIMG Journal 2013 Journal Article

Ironing out the statistical wrinkles in “ten ironic rules”

Martin A. Lindquist
Brian Caffo
Ciprian Crainiceanu

The article “Ten ironic rules for non-statistical reviewers” (Friston, 2012) shares some commonly heard frustrations about the peer-review process that all researchers can identify with. Though we found the article amusing, we have some concerns about its description of a number of statistical issues. In this commentary we address these issues, as well as the premise of the article.

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YNIMG Journal 2012 Journal Article

A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort

Bruno M. Jedynak
Andrew Lang
Bo Liu
Elyse Katz
Yanwei Zhang
Bradley T. Wyman
David Raunig
C. Pierre Jedynak

While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.

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YNIMG Journal 2012 Journal Article

Biological parametric mapping accounting for random regressors with regression calibration and model II regression

Xue Yang
Carolyn B. Lauzon
Ciprian Crainiceanu
Brian Caffo
Susan M. Resnick
Bennett A. Landman

Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard “design matrix”-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multi-modal three-dimensional assessments of the same individuals—e. g. , structural, functional and quantitative magnetic resonance imaging, alongside functional and ligand binding maps with positron emission tomography. Largely, current statistical methods in the imaging community assume that the regressors are non-random. For more realistic multi-parametric assessment (e. g. , voxel-wise modeling), distributional consideration of all observations is appropriate. Herein, we discuss two unified regression and inference approaches, model II regression and regression calibration, for use in massively univariate inference with imaging data. These methods use the design matrix paradigm and account for both random and non-random imaging regressors. We characterize these methods in simulation and illustrate their use on an empirical dataset. Both methods have been made readily available as a toolbox plug-in for the SPM software.

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YNIMG Journal 2011 Journal Article

Functional principal component model for high-dimensional brain imaging

Vadim Zipunnikov
Brian Caffo
David M. Yousem
Christos Davatzikos
Brian S. Schwartz
Ciprian Crainiceanu

We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a FPCA model is fit to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.

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YNIMG Journal 2008 Journal Article

A Bayesian hierarchical framework for spatial modeling of fMRI data

F. DuBois Bowman
Brian Caffo
Susan Spear Bassett
Clinton Kilts

Applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric, neurological, and substance abuse disorders and their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. Complementary approaches consider the ensemble of voxels constituting an anatomically defined region of interest (ROI) or summary statistics, such as means or quantiles, of the ROI. In this work, we present a Bayesian extension of voxel-level analyses that offers several notable benefits. Among these, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance matrix for regional mean parameters allows for the study of inter-regional (long-range) correlations, and the model employs an exchangeable correlation structure to capture intra-regional (short-range) correlations. Estimation is performed using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling. We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer’s disease.

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YNIMG Journal 2007 Journal Article

Relations of brain volumes with cognitive function in males 45 years and older with past lead exposure

Brian S. Schwartz
Sining Chen
Brian Caffo
Walter F. Stewart
Karen I. Bolla
David Yousem
Christos Davatzikos

We examined relations between brain volumes assessed by MRI and cognitive function in subjects in whom we have previously reported associations of cumulative lead dose with: (1) longitudinal declines in cognitive function; (2) smaller volumes of several regions of interest (ROIs) in the brain; and (3) increased prevalence and severity of white matter lesions. We used two complementary methods (ROI- [evaluating 20 ROIs] and voxel-wise) to examine associations between brain volumes and cognitive function using multiple linear regression. MRIs and cognitive testing were obtained from 532 former organolead workers with a mean (SD) age of 56. 1 (7. 7) years and a mean of 18. 0 (11. 0) years since the last occupational exposure to lead at the time of MRI acquisition. Cognitive testing was grouped into six domains of function (visuo-construction, verbal memory and learning, visual memory, executive functioning, eye–hand coordination, processing speed). Results indicated that larger ROI volumes were associated with better cognitive function in five of six cognitive domains, with significant associations observed for visuo-construction (15 of 20, p ≤0. 05), processing speed (12, p ≤0. 05), visual memory (11, p ≤0. 05), executive functioning (11, p ≤0. 05), and eye–hand coordination (11, p ≤0. 05). Significant structure–function relations were also identified in the voxel-wise analysis with low false discovery rates (all less than 2. 2%). Thus, larger volumes were associated with better cognitive function using both ROI- and voxel-based methods. In this cohort, an interesting group in which to examine structure–function relations, this finding provides a necessary condition to support the hypothesis that lead may influence cognitive function by its effect on brain volumes.

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