Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies. - Alan Agresti; Brian C. Caffo; Pamela Ohman-Strickland (Comput. Stat. Data Anal., 2004)

Flexible random intercept models for binary outcomes using mixtures of normals. - Brian C. Caffo; Ming-Wen An; Charles Rohde (Comput. Stat. Data Anal., 2007)

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 M. Yousem; Christos Davatzikos (NeuroImage, 2007)

Optimal sampling times in bioequivalence studies using a simulated annealing algorithm. - Leena Choi; Brian C. Caffo; Charles Rohde (Stat. Comput., 2007)

A Bayesian hierarchical framework for spatial modeling of fMRI data. - F. DuBois Bowman; Brian Caffo; Susan Spear Bassett; Clint Kilts (NeuroImage, 2008)

Toward Realistic and Practical Ideal Observer (IO) Estimation for the Optimization of Medical Imaging Systems. - Xin He; Brian S. Caffo; Eric C. Frey (IEEE Trans. Medical Imaging, 2008)

Quantifying auditory event-related responses in multichannel human intracranial recordings. - Dana Frances Boatman-Reich; Piotr J. Franaszczuk; Anna Korzeniewska; Brian Caffo; Eva K. Ritzl; Sarah Colwell; Nathan E. Crone (Frontiers Comput. Neurosci., 2010)

Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk. - Brian S. Caffo; Ciprian M. Crainiceanu; Guillermo Verduzco; Suresh Joel; Stewart H. Mostofsky; Susan Spear Bassett; James J. Pekar (NeuroImage, 2010)

Assessment of Bias for MRI Diffusion Tensor Imaging Using SIMEX. - Carolyn B. Lauzon; Andrew J. Asman; Ciprian M. Crainiceanu; Brian C. Caffo; Bennett A. Landman (MICCAI (2), 2011)

Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging. - Xue Yang; Carolyn B. Lauzon; Ciprian M. Crainiceanu; Brian C. Caffo; Susan M. Resnick; Bennett A. Landman (MBIA, 2011)

Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis. - Jeff Goldsmith; Ciprian M. Crainiceanu; Brian S. Caffo; Daniel S. Reich (NeuroImage, 2011)

Population-wide principal component-based quantification of blood-brain-barrier dynamics in multiple sclerosis. - Russell T. Shinohara; Ciprian M. Crainiceanu; Brian S. Caffo; María Inés Gaitán; Daniel S. Reich (NeuroImage, 2011)

Functional principal component model for high-dimensional brain imaging. - Vadim Zipunnikov; Brian C. Caffo; David M. Yousem; Christos Davatzikos; Brian S. Schwartz; Ciprian M. Crainiceanu (NeuroImage, 2011)

Towards automatic quantitative quality control for MRI. - Carolyn B. Lauzon; Brian C. Caffo; Bennett A. Landman (Image Processing, 2012)

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; Brian Caffo; Jerry L. Prince (NeuroImage, 2012)

Biological parametric mapping accounting for random regressors with regression calibration and model II regression. - Xue Yang; Carolyn B. Lauzon; Ciprian M. Crainiceanu; Brian C. Caffo; Susan M. Resnick; Bennett A. Landman (NeuroImage, 2012)

Clustering of High Dimensional Longitudinal Imaging Data. - Seonjoo Lee; Vadim Zipunnikov; Navid Shiee; Ciprian M. Crainiceanu; Brian S. Caffo; Dzung L. Pham (PRNI, 2013)

Information Criteria for Dynamic Contrast-Enhanced Magnetic Resonance Imaging. - Russell T. Shinohara; Ciprian S. Crainiceanu; Brian S. Caffo; Daniel S. Reich (PRNI, 2013)

Effects of Working Memory Demand on Neural Mechanisms of Motor Response Selection and Control. - Anita D. Barber; Brian S. Caffo; James J. Pekar; Stewart H. Mostofsky (J. Cogn. Neurosci., 2013)

Ironing out the statistical wrinkles in "ten ironic rules". - Martin A. Lindquist; Brian C. Caffo; Ciprian M. Crainiceanu (NeuroImage, 2013)

MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning. - Gagan Sidhu; Brian Caffo (CoRR, 2014)

Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach. - Martin A. Lindquist; Yuting Xu; Mary Beth Nebel; Brian S. Caffo (NeuroImage, 2014)

Reduction of motion-related artifacts in resting state fMRI using aCompCor. - John Muschelli; Mary Beth Nebel; Brian S. Caffo; Anita D. Barber; James J. Pekar; Stewart H. Mostofsky (NeuroImage, 2014)

Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI. - Haochang Shou; Ani Eloyan; Mary Beth Nebel; Amanda F. Mejia; James J. Pekar; Stewart H. Mostofsky; Brian S. Caffo; Martin A. Lindquist; Ciprian M. Crainiceanu (NeuroImage, 2014)

Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes. - Huitong Qiu; Sheng Xu; Fang Han; Han Liu 001; Brian Caffo (ICML, 2015)

Robust Portfolio Optimization. - Huitong Qiu; Fang Han; Han Liu 001; Brian Caffo (NIPS, 2015)

Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models. - Chen Yue; Shaojie Chen; Haris I. Sair; Raag D. Airan; Brian S. Caffo (Comput. Stat. Data Anal., 2015)

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 H. Mostofsky; Brian Caffo; Martin A. Lindquist (NeuroImage, 2015)

Ten Simple Rules for Effective Statistical Practice. - Robert E. Kass; Brian S. Caffo; Marie Davidian; Xiao-Li Meng; Bin Yu 001; Nancy Reid (PLoS Comput. Biol., 2016)

Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging. - Maleeha F. Syed; Martin A. Lindquist; Jay J. Pillai; Shruti Agarwal; Sachin K. Gujar; Ann S. Choe; Brian Caffo; Haris I. Sair (Brain Connect., 2017)

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 (NeuroImage, 2017)

An M-estimator for reduced-rank system identification. - Shaojie Chen; Kai Liu; Yuguang Yang 002; Yuting Xu; Seonjoo Lee; Martin A. Lindquist; Brian S. Caffo; Joshua T. Vogelstein (Pattern Recognit. Lett., 2017)

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage. - Amanda F. Mejia; Mary Beth Nebel; Anita D. Barber; Ann S. Choe; James J. Pekar; Brian S. Caffo; Martin A. Lindquist (NeuroImage, 2018)

Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c. - Che Ngufor; Holly Van Houten; Brian S. Caffo; Nilay D. Shah; Rozalina G. McCoy (J. Biomed. Informatics, 2019)

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models. - Heather Shappell; Brian S. Caffo; James J. Pekar; Martin A. Lindquist (NeuroImage, 2019)

SpineCloud: image analytics for predictive modeling of spine surgery outcomes. - Tharindu De Silva; S. Swaroop Vedula; Alexander Perdomo-Pantoja; Rohan Vijayan; Sophia A. Doerr; Ali Uneri; Runze Han; Michael D. Ketcha; R. L. Skolasky Jr.; Brian Caffo; Greg Hager; Timothy Witham; et al. (Image-Guided Procedures, 2020)

Bayesian inference of a directional brain network model for intracranial EEG data. - Tingting Zhang; Yinge Sun; Huazhang Li; Guofen Yan; Seiji Tanabe; Ruizhong Miao; Yaotian Wang; Brian S. Caffo; Mark S. Quigg (Comput. Stat. Data Anal., 2020)

Sparse principal component based high-dimensional mediation analysis. - Yi Zhao; Martin A. Lindquist; Brian S. Caffo (Comput. Stat. Data Anal., 2020)

Rxnat: An Open-Source R Package for XNAT-Based Repositories. - Adrian Gherman; John Muschelli; Brian Caffo; Ciprian M. Crainiceanu (Frontiers Neuroinformatics, 2020)

Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics. - Eric W. Bridgeford; Shangsi Wang; Zeyi Wang; Ting Xu 001; R. Cameron Craddock; Jayanta Dey; Gregory Kiar; William R. Gray Roncal; Carlo Colantuoni; Christopher Douville; Stephanie Noble; Carey E. Priebe; et al. (PLoS Comput. Biol., 2021)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

The Multiple Subnetwork Hypothesis: Enabling Multidomain Learning by Isolating Task-Specific Subnetworks in Feedforward Neural Networks. - Jacob Renn; Ian Sotnek; Benjamin Harvey; Brian Caffo (CoRR, 2022)

Editorial: Explainable artificial intelligence models and methods in finance and healthcare. - Brian S. Caffo; Fabio Aurelio D'Asaro; Artur Garcez; Emanuela Raffinetti (Frontiers Artif. Intell., 2022)

A Unified Framework on Generalizability of Clinical Prediction Models. - Bohua Wan; Brian Caffo; S. Swaroop Vedula (Frontiers Artif. Intell., 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

Learning sources of variability from high-dimensional observational studies. - Eric W. Bridgeford; Jaewon Chung; Brian Gilbert; Sambit Panda; Adam Li; Cencheng Shen; Alexandra Badea; Brian Caffo; Joshua T. Vogelstein (CoRR, 2023)

Applications of Sequential Learning for Medical Image Classification. - Sohaib Naim; Brian Caffo; Haris I. Sair; Craig K. Jones (CoRR, 2023)

Empowering Learning: Standalone, Browser-Only Courses for Seamless Education. - Babak Moghadas; Brian S. Caffo (CoRR, 2023)

Corrigendum: Editorial: Explainable artificial intelligence models and methods in finance and healthcare. - Brian S. Caffo; Fabio Aurelio D'Asaro; Artur Garcez; Emanuela Raffinetti (Frontiers Artif. Intell., 2023)

First Organoid Intelligence (OI) workshop to form an OI community. - Itzy E. Morales Pantoja; Lena Smirnova; Alysson R. Muotri; Karl J. Wahlin; Jeffrey Kahn; J. Lomax Boyd; David H. Gracias; Timothy D. Harris; Tzahi Cohen-Karni; Brian S. Caffo; Alexander S. Szalay; Fang Han; et al. (Frontiers Artif. Intell., 2023)

A machine learning based approach towards high-dimensional mediation analysis. - Tanmay Nath; Brian Caffo; Tor D. Wager; Martin A. Lindquist (NeuroImage, 2023)

Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference. - Amanda F. Mejia; David Bolin; Yu Ryan Yue; Jiongran Wang; Brian S. Caffo; Mary Beth Nebel (J. Comput. Graph. Stat., 2023)

GAMing the Brain: Investigating the Cross-Modal Relationships Between Functional Connectivity and Structural Features Using Generalized Additive Models. - Arunkumar Kannan; Brian Caffo; Archana Venkataraman (MLCN@MICCAI, 2024)

Evidential Uncertainty Quantification: A Variance-Based Perspective. - Ruxiao Duan; Brian Caffo; Harrison X. Bai; Haris I. Sair; Craig K. Jones (WACV, 2024)

Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis. - Shijia Zhang; Xiyu Ding; Brian Caffo; Junyu Chen; Cindy Zhang; Hadi Kharrazi; Zheyu Wang (CoRR, 2025)

BrainMT: A Hybrid Mamba-Transformer Architecture for Modeling Long-Range Dependencies in Functional MRI Data. - Arunkumar Kannan; Martin A. Lindquist; Brian Caffo (CoRR, 2025)