Challenges in Neuroimaging Data Analysis

Chair: Jian Kang

Talks and Speakers:

• “Graph Neural Networks for Brain Connectome Analysis,” Carl Yang (Emory University)
• “Dynamic resting state functional connectivity: A time-varying dynamic network model,” Fei Jiang (University of California)
• “Doubly Adaptive Spatial Quantile Regression for Neuroimaging Data,” Linglong Kong (University of Alberta)
• “Deciphering Relationships between Massive High-Dimensional Imaging Responses and Scalar Predictors: A Distributed Learning Approach,” Lily Wang (George Mason University)

Chair: Tingting Zhang

Talks and Speakers:

• “Changepoint Analysis in a Mixed Model Framework, With Applications to fMRI Time Series,” Mark Fiecas (University of Minnesota)
• “The hidden cost of stringent motion scrubbing,” Amanda Mejia (Indiana University)
• “Statistical Brain Network Analysis: Recent Developments and Future Directions,” Sean Simpson (Wake Forest University)
• “Analysis of Functional Connectivity Changes from Childhood to Old Age,” Tingting Zhang (University of Pittsburgh)

Chair: Sean Simpson

Talks and Speakers:

• “Processing Induced Correlation in FMRI Data,” Daniel Rowe (Marquette University)
• “Sliding windows analysis can undo the effects of preprocessing when applied to fMRI,” Martin Lindquist (Johns Hopkins University)
• “A Hidden Semi-Markov Model Approach to State-Based Dynamic Brain Network Analyses: Recent Developments and Future Directions,” Heather Shappell (Wake Forest University School of Medicine)
• “Utilizing Invariance and Exchangeability in Neuroimaging Data Analysis,” Yi Zhao (Indiana University)

Chair: Eardi Lila

Panelists:

• Robert Kass (Carnegie Mellon University)
• Tianwen Ma (Emory University)
• Jun Young Park (University of Toronto)

Chair: Tingting Zhang

Talks and Speakers:

• “Longitudinal Manifold Learning for Modeling Shapes in Alzheimer’s Disease,” Ani Eloyan (Brown University)
• “The Missing Link: Establishing the Parallels Between Censored Covariate and Missing Data,” Tanya Garcia (University of North Carolina at Chapel Hill)
• “Some Recent and Ongoing Work on Intracranial Neurodata Analysis,” Lexin Li (University of California, Berkeley)
• “Orthogonal Common-Source and Distinctive-Source Decomposition Between High-Dimensional Data Views,” Hai Shu (New York University)

Chair: John Kornak

Talks and Speakers:

• “Statistical Regularization Used to Study Brain Structure, Function, and Connectivity: Current Work and Future Directions,” Jaroslaw Harezlak (Indiana University Bloomington)
• “Disease Progression Modeling for Frontotemporal Dementia,” John Kornak (University of California)
• “Statistical Challenges in Analysis of Tau PET Imaging Data in Alzheimer’s Disease,” Dana Tudorascu (University of Pittsburgh)
• “Multidimensional Biomarker Landscape in Alzheimer’s Disease: Insights for Improved Disease Modeling and Clinical Trial Design,” Duygu Tosun-Turgut (University of California)

Chair: Jian Kang

Talks and Speakers:

• “Bayes in Neuroscience: Addressing Key Research Challenges with Single and Multi-Object Data,” Sharmistha Guha (Texas A&M University)
• “Challenges in Functional Near-Infrared Spectroscopy,” Timothy Johnson (University of Michigan)
• “Bayesian Methods in EEG-Based Brain-Computer Interfaces,” Tianwen Ma (Emory University)
• “Opportunities and Challenges in the Analysis of Event-Related Potentials,” Marina Vannucci (Rice University)

Chair: Chao Huang

Panelists:

• Tom Nichols (University of Oxford)
• Li Shen (University of Pennsylvania)
• Dana Tudorascu (University of Pittsburgh)
• Wesley Thompson (Laureate Institute for Brain Research)

Chair: Anju Srivastava

Talks and Speakers:

• “Atlas-to-Data Alignment of Spatially-Resolved Transcriptomics Data,” Laurent Younes (Johns Hopkins University)
• “Cortical Convolutions: Morphogenesis and Morphometry,” L Mahadevan (Harvard University)
• “Which Riemannian Metric for Statistics in Connectomics?,” Xavier Pennec (INRIA)
• “Statistical Shape Analysis of Complex Natural Structures,” Anuj Srivastava (Florida State University)

Chair: Zhengwu Zhang

Talks and Speakers:

• “Challenges & Opportunities in Tractography: From Classical Techniques to Futuristic Machine Learning,” Maxime Descoteaux (Université de Sherbrooke)
• “Defining and Quantifying the Brain’s White Matter Bundles,” Lauren O’Donnell (Brigham and Women’s Hospital)
• “Connectome-Based Spatial Statistics Using a Function-Aware Structural Connectome Atlas,” Tengfei Li (University of North Carolina, Chapel Hill)
• “Continuous and Atlas-Free Analysis of Brain Structural Connectivity,” Zhengwu Zhang (University of North Carolina, Chapel Hill)

Chair: Hernando Ombao

Talks and Speakers:

• “Graphical Modeling and Spectral Analysis: New Directions and Challenges,” Sumanta Basu (Cornell University)
• “Challenges in Causal Inference from fMRI: Time Series, Networks, Interpretation, and Assumptions,” Xi (Rossi) Luo (UTHealth Houston)
• “Modeling Dependence and Testing for Causality Via Spectral Entropy,” Hernando Ombao (KAUST)
• “Non-Stationarity in Brain Imaging Data: Challenges and Opportunities,” Ali Shojaie (University of Washington)

Chair: Hongtu Zhu

Panelists:

• Tom Nichols (University of Oxford)
• Hernando Ombao (KAUST)
• Anuj Srivastava (Florida State University)
• Michele Guindani (UCLA)

Chair: Hongtu Zhu

Talks and Speakers:

• “Enhancing Dementia Studies with AI and Informatics: Strategies for Mining Brain Imaging Genomics Data,” Li Shen (University of Pennsylvania)
• “Integrative analysis of multi-modal MRI and genomics data: from linear to deep collaborative learning,” Yuping Wang (Tulane University)
• “Multi-organ imaging-derived polygenic indexes,” Bingxin Zhao (University of Pennsylvania)
• “Heritability and Genetic Contribution Analysis of Structural-Functional Coupling in Human Brain,” Yize Zhao (Yale University)

Chair: Tom Nichols

Talks and Speakers:

• “Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis,” Jian Kang (University of Michigan)
• “Network-Aware Connectome Analysis,” Liza Levina (University of Michigan)
• “Annotation-Informed Variance Components Model for Whole-Brain Associations,” Wesley Thompson (Laureate Institute for Brain Research)
• “Neuroimaging Data Analysis in the Era of Data Science and AI,” Hongtu Zhu (University of North Carolina)

Chair: Yi Zhao

Panelists:

• Markus Axer (Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich)
• Shuo Chen (University of Maryland)
• Moo Chung (University of Wisconsin, Madison)
• Ivor Cribben (University of Alberta)
• Maxime Descoteaux (Université de Sherbrooke)