SOCR News AmStats SIM 2024

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:

Conference Program

Date	Start	End	Duration (minutes)	Session 1	Session 2
Wednesday 5/29/24	8:30	10:00	90	Tutorial: Complex-time representation of spatiotemporal processes and spacekime analyhtics; Instructor(s): Ivo Dinov; Location: Room 101	Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
	10:00	10:10	10	Break
	10:10	11:40	90	Tutorial: NiChart: a software tool for building machine learning oriented neuroimaging brain chart; Instructor(s): Haochang Shou Ren Zheng; Location: Room 101	Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
	11:40	13:10	90	Lunch break
	13:10	14:40	90	Student Paper Competition Winners; Location: Room 101/102
	14:40	14:50	10	Break
	14:50	15:50	60	Keynote - Bin Yu (Chair: Tingting Zhang); Location: Room 101/102
	15:50	18:00	130	Poster Presentations/Mixer (food and drinks offered); Location: Room 103/104
Thursday 5/30/24	8:00	8:30	30	Breakfast
	8:30	10:10	100	Longitudinal imaging and biostatistical methods; Session Organizer: Ivo Dinov; Speakers: Sharmistha Guha Hossein Moradi Ranjan Maitra Dan Rowe; Location: Room 103/104	Expanding neuroimaging research: integrating insights from biomedical sciences, Session Organizer: Jun Young Park; Speakers: Sarah M. Weinstein Andrew An Chen Bingxin Zhao Jun Young Park; Location: Room 102
	10:10	10:20	10	Break
	10:20	11:20	60	Keynote - Andrew J. Saykin (Chair: Sujuan Gao); Location: Room 103/104
	11:20	13:00	100	Lunch break
	13:00	14:15	75	Statistical learning methods for neuroscience; Session Organizer: Shuheng Zhou; Speakers: Jian Kang Chunming Zhang Shuheng Zhou; Location: Room 103/104	Recent developments in statistical methodology for neuroimaging data analysis; Session Organizer: Dayu Sun; Speakers: Xin Ma Shuo Chen Joshua Lukemire; Location: Room 102
	14:15	14:25	10	Break
	14:25	16:05	100	Statistical inference in neuroimaging; Session Organizer: Eardi Lila; Speakers: Benjamin Risk Raphiel Murden Daniel Kessler Simon Vandekar; Location: Room 103/104	New developments for harmonization processing and modeling for imaging data; Session Organizer: Yize Zhao; Speakers: Dana Tudorascu Selena Wang Zhengwu Zhang Tsung-Hung Yao; Location: Room 102
	16:05	16:15	10	Break
	16:15	17:30	75	Invariance and distribution/density objects in neuroimaging studies; Session Organizer: Yi Zhao; Speakers: Bonnie Smith Changbo Zhu Yi Zhao; Location: Room 103/104	Advances in statistical method for neuroimaging data; Session Organizer: Selena Wang; Speakers: Naomi Ding Dayu Sun Selena Wang ; Location: Room 102
Friday 5/31/24	8:00	8:30	30	Breakfast
	8:30	10:10	100	Graph-based network connectomes analysis; Session Organizer: Simon Vandekar; Speakers: Eardi Lila Sean L. Simpson Panpan Zhang Tingting Zhang; Location: Room 103/104	Statistical methods for dissecting tumor microenvironment based on spatial proteomics datasets; Session Organizer: Souvik Seal; Speakers: Thao Vu Jiangmei Xiong Julia Wrobel Junsouk Choi; Location: Room 102
	10:10	10:20	10	Break
	10:20	11:20	60	Keynote - Robert E. Kass (Chair: Daniel Rowe); Location: Room 103/104
	11:20	13:00	100	Lunch break
	13:00	14:40	100	When machine learning and generative models meet imaging network and point cloud data; Session Organizer: Zhengwu Zhang; Speakers: Mingxia Liu Maoran Xu Yuexuan Wu Xinyi Li; Location: Room 103/104	Novel statistical inference methods with applications; Session Organizer: Julia Fisher; Speakers: Fatma Parlak Daniel Adrian Yueyang Shen Jose Rodriguez-Acosta; Location: Room 102
	14:40	14:50	10	Break
	14:50	16:30	100	Frontiers in medical imaging: harnessing artificial intelligence and statistical analysis for breakthrough insights; Session Organizer: Lei Liu; Speakers: Yize Zhao Yifan Peng Lei Liu Haoda Fu; Location: Room 103/104
	16:30	16:40	10	Closing remarks
Location: Room 103/104

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