SOCR News AmStats SIM 2024
Contents
SOCR News & Events: 2024 American Statistical Association Special Session on Longitudinal Imaging and Biostatistical Methods, Statistics in Imaging Annual Meeting, Indianapolis
Spacekime Analytics Tutorial
Tutorial Logistics
- Title: Complex-Time Representation of Spatiotemporal Processes and Spacekime Analytics
- Organizer: Ivo Dinov (Michigan)
- Date/Time: Wednesday (May 29, 2024) 8:30am-10:00am US ET
- Venue: JW Marriott Indianapolis, Indianapolis, IN
- Registration: ... coming up ...
- Conference: 2024 Statistical Methods in Imaging Conference, Annual Meeting of the ASA Statistics in Imaging Section - Statistical Methods in Imaging (SMI)
- Session Format: details coming up ...
Abstract
- This tutorial will describe the novel complex-time (kime) representation of repeated measurement longitudinal processes, which underlies advanced space-kime statistical inference and space-kime artificial intelligence (AI) applications. By translating fundamental quantum mechanics principles into statistical inference models of time-varying processes, we generalize the classical 4D spatiotemporal sampling to a 5D space-kime manifold, where the phase of complex-time encodes repeated random drawings at fixed spatiotemporal locations. Many AI applications and statistical inference techniques involving temporal data can be formulated in a Bayesian space-kime analytics framework. We explore alternative strategies for translating time-series observations into kime-surfaces, which are richer, computationally tractable, objects amenable to tensor-based linear modeling and model-free inference. Simulated and observed neuroimaging and macroeconomics data will be used to demonstrate applications of space-kime analytics. As time permits, we may discuss the space-kime analytic duality between theoretical model inference, based on generalized functions (distributions), and experimental data inference, based on replicated finite samples as proxy measures of the underlying probability distributions. Several theoretical, experimental, computational, and data-analytic open problems will be presented.
Special Session
This session will include a blend of mathematical, statistical, and computational experts presenting on recent methodological advances to represent, model, predict, synthesize, and analyze large and heterogeneous spatiotemporal imaging data.
Session Logistics
- Title: Longitudinal Imaging and Biostatistical Methods
- Organizers: Ivo Dinov (Michigan), Sharmistha Guha (TAMU), Hossein Moradi (SD State), Ranjan Maitra (Iowa), and Dan Rowe (Marquette)
- Date/Time: Thursday, 5/30/24, 8:30 AM - 10:20 AM US ET
- Venue: JW Marriott Indianapolis, Indianapolis, IN
- Registration: Registration
- Conference: 2024 Statistical Methods in Imaging Conference, Annual Meeting of the ASA Statistics in Imaging Section.
- Session Format: 20+5 minute presentations (staggered).
Session Presenters
- Sharmistha Guha (TAMU)
- Title: Supervised Modeling of Heterogeneous Networks: Investigating Functional Connectivity Across Various Cognitive Control Tasks
- Abstract: We present a neuroimaging-driven study examining the relationship between functional connectivity across cognitive control domains and cognitive phenotypes, aiming to identify specific brain regions significantly associated with these phenotypes. We propose a generalized linear modeling framework incorporating multiple network responses and predictors, allowing for diverse interconnections between edges. Leveraging hierarchical Bayesian modeling, our approach estimates regression coefficients and identifies predictor-linked nodes with precise uncertainty quantification. Theoretical analysis shows our model's posterior predictive density asymptotically approaches the true data generating density. Empirical investigations, including simulation studies and functional connectivity data analysis, demonstrate our framework's superior performance compared to competitors.
- Hossein Moradi (SDState)
- Title: Tensor Regression for Brain Imaging Data
- Abstract: Multidimensional array data, also called tensors, are used in neuroimaging and other big data applications. In this paper, we propose a parsimonious Bayesian Tensor linear model for neuroimaging study with brain image as a response and a vector of predictors. Our method provides estimates for the parameters of interest by using an Envelope method. The proposed method characterizes different sources of uncertainty and the inference is performed using Markov Chain Monte Carlo (MCMC). We demonstrate posterior consistency and develop a computationally efficient MCMC algorithm for posterior computation using Gibbs sampling. The effectiveness of our approach is illustrated through simulation studies and analysis of alcohol addiction's effect on brain connectivity.
- Ranjan Maitra (Iowa State)
- Title: Tensor-on-Tensor Time Series Regression for Integrated fMRI Analysis
- Abstract:
- Dan Rowe (Marquette)
- Title: (TBD)
- Abstract:
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