Description
Back to topNeuroimaging involves generating images of the central nervous system to understand its structure, function, or pharmacology. The field is rapidly evolving, with new techniques emerging for data acquisition and advanced statistical learning methods being developed for data analysis. Recently, there’s been a surge in collecting neuroimaging data across healthcare, research, and clinical trials. Such imaging aids in diagnosing and prognosing brain diseases, like multiple sclerosis, dementia, and schizophrenia. It helps identify issues such as strokes, tumors, and brain swelling. Current applications, like MRI for multiple sclerosis monitoring, still present opportunities for enhanced statistical modeling.
Large biomedical studies gather extensive neuroimaging data, including sMRI, DWI, and fMRI. These studies target the human brain’s connectivity, understanding brain disorders, monitoring neuropsychiatric progression, and diagnosing brain cancer. The influx of data can significantly enhance our comprehension of the brain and help in creating effective treatments for neurological and psychiatric conditions. However, analyzing this data necessitates the progression of statistical learning techniques, encompassing image processing and population-based statistical evaluations. While topics like image enhancement and predictive models are of interest, the growth in statistical analysis lags behind neuroimaging advancements, challenging the application of research in clinical settings.
This workshop aims is to provide a comprehensive discussion of mathematical and statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. This research topic is important and timely to ensure that researchers are equipped with the tools and methods needed to handle the large and complex datasets and to produce reliable and reproducible research findings.
This workshop will include a poster session for early career researchers (including graduate students). In order to propose a poster, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. The registration form should not be used to propose a poster.
The deadline for proposing is May 20, 2024. If your proposal is accepted, you should plan to attend the event in-person.
Organizers
Back to topSpeakers
Back to topSchedule
Back to topPlenary Speaker: Robert Kass (Carnegie Mellon University, Statistics & Data Sciences)
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)
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: A Study Using HCP-D, HCP-YA, and HCP-A Datasets”, Tingting Zhang (University of Pittsburgh)
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 Analyis, Yi Zhao (Indiana University)
Panelists:
- Robert Kass (Carnegie Mellon University)
- Tianwen Ma (Emory University)
- Jun Young Park (University of Toronto)
Plenary Speaker: Markus Axer (Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich)
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)
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)
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)
Panelists:
- Tom Nichols (University of Oxford)
- Li Shen (University of Pennsylvania)
- Dana Tudorascu (University of Pittsburgh)
- Wesley Thompson (Laureate Institute for Brain Research)
Plenary Speaker: Jianfeng Feng (University of Warwick)
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)
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)
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)
Panelists:
- Tom Nichols (University of Oxford)
- Hernando Ombao (KAUST)
- Anuj Srivastava (Florida State University)
- Michele Guindani (UCLA)
Plenary Speaker: Thomas Nichols (University of Oxford)
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)
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)
Plenary Speaker: Hanchuan Peng (Institute for Brain and Intelligence)
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)
Poster Session
Back to topThe posters that have been submitted in advance for the poster session are available on the poster session page.