SOCR News AmStats SIM 2024

From SOCR
Revision as of 09:31, 29 May 2024 by Dinov (talk | contribs) (→‎Best Student Paper Award)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

SOCR News & Events: 2024 American Statistical Association, Statistical Methods in Imaging Annual Meeting, Indianapolis

The 2024 American Statistical Association, Statistical Methods in Imaging (SMI) Annual Meeting will feature three SOCR activities:

Spacekime Analytics Tutorial

Tutorial Logistics

Abstract

This time-comlexity and inferential uncertainty (TCIU) Spacekime Analytics 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: Longitudinal Imaging and Biostatistical Methods

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

Session Presenters

Title: Supervised Modeling of Heterogeneous Networks: Investigating Functional Connectivity Across Various Cognitive Control Tasks
Abstract: We present a novel Bayesian approach to address limitations in current methods for studying the relationship between functional connectivity across cognitive control domains and cognitive phenotypes. Our integrated framework jointly learns heterogeneous networks with vector-valued predictors, overcoming the constraints of treating each network independently in regression analysis. By assuming shared nodes across networks with varying interconnections, our method captures complex relationships while offering uncertainty quantification. Theoretical analysis demonstrates convergence to the true data-generating density, supported by empirical studies showcasing superior performance over existing approaches.
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.
Title: Title: Reduced-Rank Tensor-on-Tensor Regression and Tensor-Variate Analysis of Variance
Abstract: Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure. We extend the classical multivariate regression model to exploit such structure in two ways: first, we impose four types of low-rank tensor formats on the regression coefficients. Second, we model the errors using the tensor-variate normal distribution that imposes a Kronecker separable format on the covariance matrix. We obtain maximum likelihood estimators via block-relaxation algorithms and derive their computational complexity and asymptotic distributions. Our regression framework enables us to formulate tensor-variate analysis of variance (TANOVA) methodology. This methodology, when applied in a one-way TANOVA layout, enables us to identify cerebral regions significantly associated with the interaction of suicide attempters or non-attemptor ideators and positive-, negative- or death-connoting words in a functional Magnetic Resonance Imaging study. Another application uses three-way TANOVA on the Labeled Faces in the Wild image dataset to distinguish facial characteristics related to ethnic origin, age group and gender. A R package totr implements the methodology.
Title: Bayesian k-Space Estimation for fMRI
Abstract: In fMRI, as voxel sizes decrease, there is less tissue to produce a signal, resulting in a decrease in the signal-to-noise ratio and contrast-to-noise ratio. In fMRI, there have been many attempts to decrease the noise in an image in order to increase activation, but most lead to blurrier images. An alternative is to develop methods in spatial frequency space, which have unique benefits. This work proposes a Bayesian approach that quantifies available a priori information about measured complex-valued frequency coefficients. This prior information is incorporated with observed spatial frequency coefficients, and the spatial frequency coefficients estimated a posteriori. The posterior estimated spatial frequency coefficient are inverse Fourier transform reconstructed into images with reduced noise and increased detection power.


Best Student Paper Award

Congratulations to Yueyang Shen and Yupeng Zhang on Co-Winning (along with another student, James Buenfil/Washington) the annual "Theory and Methods Track" Best Paper Award of the 2024 American Statistical Association (AmStat) - Statistical Methods in Imaging (SMI) Conference.

This paper Statistical foundations of invariance and equivariance in deep artificial neural network learning was recognized as being of remarkable quality by the AmStat/SMI review committee.

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
Yueyang Shen & Yupeng Zhang Co-Winning (along with another student, James Buenfil/Washington) the annual "Theory and Methods Track" Best Paper Award for their work
Statistical foundations of invariance and equivariance in deep artificial neural network learning
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


Translate this page:

(default)
Uk flag.gif

Deutsch
De flag.gif

Español
Es flag.gif

Français
Fr flag.gif

Italiano
It flag.gif

Português
Pt flag.gif

日本語
Jp flag.gif

България
Bg flag.gif

الامارات العربية المتحدة
Ae flag.gif

Suomi
Fi flag.gif

इस भाषा में
In flag.gif

Norge
No flag.png

한국어
Kr flag.gif

中文
Cn flag.gif

繁体中文
Cn flag.gif

Русский
Ru flag.gif

Nederlands
Nl flag.gif

Ελληνικά
Gr flag.gif

Hrvatska
Hr flag.gif

Česká republika
Cz flag.gif

Danmark
Dk flag.gif

Polska
Pl flag.png

România
Ro flag.png

Sverige
Se flag.gif

Retrieved from "https://wiki.socr.umich.edu/index.php?title=SOCR_News_AmStats_SIM_2024&oldid=17940"