Brian S. Caffo

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A user-friendly tutorial on link-probit-normal models

Brian S. Caffo, Michael Griswold, B. Caffo, 2006 .

Brian S. Caffo, Ciprian M. Crainiceanu, Jeffrey S. Morris, 2013 .

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

Brian S. Caffo, Ying Guo, Amanda F. Mejia, 2019, Journal of the American Statistical Association.

Author response for "A whole‐brain modeling approach to identify individual and group variations in functional connectivity"

Brian S. Caffo, Yi Zhao, Xi Luo, 2020 .

Modular preprocessing pipelines can reintroduce artifacts into fMRI data

Martin A. Lindquist, Brian S. Caffo, Stephan Geuter, 2018 .

Velocity: A PET Study Motor Subcircuits Mediating the Control of Movement

Brian S. Caffo, Natalia Petridou, John W. Krakauer, 2015 .

Big Data Reproducibility: Applications in Brain Imaging and Genomics

Carey E. Priebe, Brian S. Caffo, Zhi Yang, 2020 .

Sparse principal component based high-dimensional mediation analysis

Martin A. Lindquist, Brian S. Caffo, Yi Zhao, 2018, Comput. Stat. Data Anal..

A Whole-Brain Regression Method to Identify Individual and Group Variations in Functional Connectivity

Brian S. Caffo, Yi Zhao, Xi Luo, 2020, bioRxiv.

LONGITUDINAL PENALIZED FUNCTIONAL REGRESSION

Brian S. Caffo, Ciprian M. Crainiceanu, Jeffrey D. Goldsmith, 2010 .

POPULATION FUNCTIONAL DATA ANALYSIS OF GROUP ICA-BASED CONNECTIVITY MEASURES FROM fMRI

Brian S. Caffo, Susan Spear Bassett, James J. Pekar, 2011 .

Parallel Bayesian MCMC Imputation for Multiple Distributed Lag Models

Thomas A. Louis, Brian S. Caffo, Roger D. Peng, 2011 .

A Length Penalized Probabilistic Principal Curve Algorithm With Applications To Handwritten Digits And Pharmacologic Colon Imaging

Brian S. Caffo, Huan Chen, Ethel Weld, 2020, bioRxiv.

Pulse pileup statistics for energy sensitive photon counting detectors with pulse height analysis

Katsuyuki Taguchi, Jochen Cammin, Somesh Srivastava, 2012, Medical Imaging.

Eliminating accidental deviations to minimize generalization error: applications in connectomics and genomics

Carey E. Priebe, Brian S. Caffo, Zhi Yang, 2019, bioRxiv.

Bayesian inference of a directional brain network model for intracranial EEG data

Brian S. Caffo, Tingting Zhang, Mark Quigg, 2020, Comput. Stat. Data Anal..

Multilevel functional principal component analysis

Brian S. Caffo, Ciprian M. Crainiceanu, Chongzhi Di, 2009 .

Solutions manual forWackerly, Mendenhall, and Scheaffer’s Mathematical Statistics with Applications

Brian S. Caffo, Galin L. Jones, 2001 .

An MCMC-based analysis of a multilevel model for functional MRI data

Brian S. Caffo, Lynn E. Eberly, Dubois Bowman, 2011 .

A spatial template independent component analysis model for subject-level brain network estimation and inference

David Bolin, Brian S. Caffo, Amanda F. Mejia, 2020 .

JOINTLY MODELING CONTINUOUS AND BINARY OUTCOMES FOR BOOLEAN OUTCOMES: AN APPLICATION TO MODELING HYPERTENSION

Brian S. Caffo, Elizabeth A. Stuart, Xianbin Li, 2008 .

Markov chain Monte Carlo (MCMC) based ideal observer estimation using a parameterized phantom and a pre-calculated dataset

Brian S. Caffo, Xin He, Eric C. Frey, 2007, SPIE Medical Imaging.

Multimodal neuroimaging data integration and pathway analysis

Brian S. Caffo, Lexin Li, Yi Zhao, 2019, Biometrics.

"Teaching Statistics for the Future, the MOOC Revolution and Beyond"

Brian S. Caffo, 2014 .

Editorial: Recent Advances and Challenges on Big Data Analysis in Neuroimaging

Han Liu, Brian S. Caffo, Jian Kang, 2016, Frontiers in Neuroscience.

Stimulus vectors for an event-related design (top) and a block design (bottom).

Shanshan Li, Brian S. Caffo, John Muschelli, 2014 .

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

Martin A. Lindquist, Brian S. Caffo, James J. Pekar, 2019 .

An M-Estimator for Reduced-Rank High-Dimensional Linear Dynamical System Identification

Brian S. Caffo, Seonjoo Lee, Joshua T. Vogelstein, 2015, 1509.03927.

ESTIMATING TEMPORAL ASSOCIATIONS IN ELECTROCORTICOGRAPHIC (ECoG) TIME SERIES WITH FIRST ORDER PRUNING

Brian S. Caffo, Dana Boatman, Haley Hedlin, 2010 .

Screening for Differentially Expressed Genes: Are Multilevel Models Helpful?

Brian S. Caffo, Dongmei Liu, Giovanni Parmigiani, 2013 .

Regularized regression on compositional trees with application to MRI analysis

Michael I. Miller, Brian S. Caffo, Xi Luo, 2021, Journal of the Royal Statistical Society. Series C, Applied statistics.

Principal regression for high dimensional covariance matrices.

Brian S. Caffo, Xi Luo, Yi Zhao, 2020, Electronic journal of statistics.

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Michael I. Miller, Brian S. Caffo, Xi Luo, 2021, Biometrics.

Compositional Data: An Application to Brain Volumetric Visualization

Brian S. Caffo, Vadim Zipunnikov, Andreia V. Faria, 2021 .

Covariate Assisted Principal Regression for Covariance Matrix Outcomes

Brian S. Caffo, Bingkai Wang, Stewart H. Mostofsky, 2018, bioRxiv.

Ascent-Based Monte Carlo EM

Brian S. Caffo, Galin L. Jones, B. Caffo, 2003 .

Bayesian inference of high-dimensional, cluster-structured ordinary differential equation models with applications to brain connectivity studies

Brian S. Caffo, Dana Boatman-Reich, Tingting Zhang, 2017 .

A unified approach to modeling multivariate binary data using copulas over partitions

Brian S. Caffo, Ciprian M. Crainiceanu, Bruce J. Swihart, 2010 .

Unequal sampling for Monte Carlo EM algorithms

Brian S. Caffo, James G. Booth, 2002 .

Connectivity supporting attention in children with attention deficit hyperactivity disorder

Brian S. Caffo, Mary Beth Nebel, James J. Pekar, 2014, NeuroImage: Clinical.

Power Conjugate Multilevel Models with Applications to Genomics

Brian S. Caffo, Liu Dongmei, Giovanni Parmigiani, 2004 .

Revisiting Brain Atrophy and Its Relationship to Disability in Multiple Sclerosis

Peter A. Calabresi, Brian S. Caffo, Daniel S. Reich, 2012, PloS one.

On statistical tests of functional connectome fingerprinting

Martin A. Lindquist, Brian S. Caffo, Susan M. Resnick, 2018, bioRxiv.

ON THE MERITS OF VOXEL-BASED MORPHOMETRIC PATH-ANALYSIS FOR INVESTIGATING VOLUMETRIC MEDIATION OF A TOXICANT'S INFLUENCE ON COGNITIVE FUNCTION

Christos Davatzikos, Brian S. Caffo, Walter F. Stewart, 2008 .

LONGITUDINAL ANALYSIS OF SPATIOTEMPORAL PROCESSES: A CASE STUDY OF DYNAMIC CONTRAST-ENHANCED MAGNETIC RESONANCE IMAGING IN MULTIPLE SCLEROSIS

Brian S. Caffo, Daniel S. Reich, Ciprian Crainiceanu, 2011 .

Exact Hypothesis Tests for Log-linear Models with exactLoglinTest

Brian S. Caffo, B. Caffo, 2006 .

Empirical supremum rejection sampling

Brian S. Caffo, Anthony C. Davison, James G. Booth, 2002 .

Developmental changes in within- and between-network connectivity between late childhood and adulthood

Brian S. Caffo, James J. Pekar, Anita D. Barber, 2013, Neuropsychologia.

A Markov chain Monte Carlo Algorithm for Approximating Exact Conditional Probabilities

Brian S. Caffo, James G Booth, J. Booth, 2001 .

For peer review only Computerized lung sound analysis to improve the specificity of pediatric pneumonia diagnosis in resource-poor settings:

Robert H. Gilman, Mounya Elhilali, Brian S. Caffo, 2011 .

LONGITUDINAL HIGH-DIMENSIONAL DATA ANALYSIS

Brian S. Caffo, Daniel S. Reich, Vadim Zipunnikov, 2011 .