Difference between revisions of "SOCR News AmStats SIM 2024"

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== [[SOCR_News | SOCR News & Events]]: 2024 2024 American Statistical Association Special Session on Longitudinal Imaging and Biostatistical Methods, Statistics in Imaging Annual Meeting, Indianapolis ==
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== [[SOCR_News | SOCR News & Events]]: 2024 American Statistical Association, Statistical Methods in Imaging Annual Meeting, Indianapolis ==
 +
 
 +
The [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 American Statistical Association, Statistical Methods in Imaging (SMI) Annual Meeting] will feature three SOCR activities:
 +
 
 +
* [[SOCR_News_AmStats_SIM_2024#Spacekime_Analytics_Tutorial | A Spacekime Analytics Tutorial]],
 +
* [[SOCR_News_AmStats_SIM_2024#Special_Session| An Invited Special Session on ''Longitudinal Imaging and Biostatistical Methods'']], and
 +
* [[SOCR_News_AmStats_SIM_2024#Student_Paper_by_Yueyang_Shen_and_Yupeng_Zhang | A paper titled ''Statistical Foundations of Invariance and Equivariance in Deep Artificial Neural Network Learning'', by Yueyang Shen and Yupeng Zhang, received the Best Paper Award in the Methods and Theory Category]].
  
 
==Spacekime Analytics Tutorial==
 
==Spacekime Analytics Tutorial==
Line 7: Line 13:
 
* '''Organizer''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov (Michigan)]  
 
* '''Organizer''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov (Michigan)]  
 
* '''Date/Time''': Wednesday (May 29, 2024) 8:30am-10:00am [https://time.is/ET US ET]
 
* '''Date/Time''': Wednesday (May 29, 2024) 8:30am-10:00am [https://time.is/ET US ET]
−
* '''Venue''': [https://www.marriott.com/en-us/hotels/indjw-jw-marriott-indianapolis/overview/ JW Marriott Indianapolis], Singapore
+
* '''Venue''': [https://www.marriott.com/en-us/hotels/indjw-jw-marriott-indianapolis/overview/ JW Marriott Indianapolis], Indianapolis, IN
−
* '''Registration''': ... coming up ...
+
* '''Registration''': [https://indianauniv.ungerboeck.com/prod/emc00/PublicSignIn.aspx?aat=31684847734a734c626a6e71484e376d304a2b6c4436346d334e2b464462644271737a54647347546746773d Registration]
 
* '''Conference''': [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 Statistical Methods in Imaging Conference, Annual Meeting] of the [https://www.statsinimaging.org/ ASA Statistics in Imaging Section - ''Statistical Methods in Imaging (SMI)'']
 
* '''Conference''': [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 Statistical Methods in Imaging Conference, Annual Meeting] of the [https://www.statsinimaging.org/ ASA Statistics in Imaging Section - ''Statistical Methods in Imaging (SMI)'']
−
* '''Session Format''':  details coming up ...
+
* '''Format''':  A [https://wiki.socr.umich.edu/images/b/b2/Dinov_Spacekime_2024_Slidedeck_V3.pdf brief overview lecture] followed by [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html hands-on spacekime analytics tutorial and demonstrations], [https://wiki.socr.umich.edu/images/b/b2/Dinov_Spacekime_2024_Slidedeck_V3.pdf Slidedeck].
  
 
===Abstract===
 
===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.
+
: [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html 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==
+
== 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.
 
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 Logistics===
 
* '''Title''': ''Longitudinal Imaging and Biostatistical Methods''  
 
* '''Title''': ''Longitudinal Imaging and Biostatistical Methods''  
 
* '''Organizers''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov (Michigan)], Sharmistha Guha (TAMU), Hossein Moradi (SD State), Ranjan Maitra (Iowa), and Dan Rowe (Marquette)  
 
* '''Organizers''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov (Michigan)], Sharmistha Guha (TAMU), Hossein Moradi (SD State), Ranjan Maitra (Iowa), and Dan Rowe (Marquette)  
−
* '''Date/Time''': TBD
+
* '''Date/Time''': Thursday, 5/30/24, 8:30 AM - 10:20 AM [https://time.is/ET US ET]
−
* '''Venue''': [https://www.marriott.com/en-us/hotels/indjw-jw-marriott-indianapolis/overview/ JW Marriott Indianapolis], Singapore
+
* '''Venue''': [https://www.marriott.com/en-us/hotels/indjw-jw-marriott-indianapolis/overview/ JW Marriott Indianapolis], Indianapolis, IN
−
* '''Registration''': ... coming up ...
+
* '''Registration''': [https://indianauniv.ungerboeck.com/prod/emc00/PublicSignIn.aspx?aat=31684847734a734c626a6e71484e376d304a2b6c4436346d334e2b464462644271737a54647347546746773d Registration]
 
* '''Conference''': [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 Statistical Methods in Imaging Conference, Annual Meeting] of the [https://www.statsinimaging.org/ ASA Statistics in Imaging Section].
 
* '''Conference''': [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 Statistical Methods in Imaging Conference, Annual Meeting] of the [https://www.statsinimaging.org/ ASA Statistics in Imaging Section].
−
* '''Session Format''':  details coming up ...
+
* '''Session Format''':  20+5 minute presentations (staggered).
  
 
===Session Presenters===
 
===Session Presenters===
Line 35: Line 39:
 
: ''Title'': Supervised Modeling of Heterogeneous Networks: Investigating Functional Connectivity Across Various Cognitive Control Tasks
 
: ''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.
+
: ''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.
−
 
 
  
 
* [https://www.sdstate.edu/directory/hossein-moradi Hossein Moradi] (SDState)
 
* [https://www.sdstate.edu/directory/hossein-moradi Hossein Moradi] (SDState)
 
: ''Title'': Tensor Regression for Brain Imaging Data
 
: ''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.
  
 
* [https://www.stat.iastate.edu/people/ranjan-maitra Ranjan Maitra] (Iowa State)
 
* [https://www.stat.iastate.edu/people/ranjan-maitra Ranjan Maitra] (Iowa State)
−
: ''Title'': Tensor-on-Tensor Time Series Regression for Integrated fMRI Analysis (current work)
+
: ''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.
  
 
* [https://www.marquette.edu/mathematical-and-statistical-sciences/directory/daniel-rowe.php Dan Rowe] (Marquette)
 
* [https://www.marquette.edu/mathematical-and-statistical-sciences/directory/daniel-rowe.php Dan Rowe] (Marquette)
−
: ''Title'': (TBD)
+
: ''Title'': Bayesian k-Space Estimation for fMRI
 
   
 
   
−
* [https://www.socr.umich.edu/people/dinov/ Ivo Dinov] (Michigan)
+
: ''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.
 +
 
 +
<!-- * [https://www.socr.umich.edu/people/dinov/ Ivo Dinov] (Michigan)
 
: ''Title'': Spacekime analytics: from time-series to kime-surfaces and inference-functions
 
: ''Title'': Spacekime analytics: from time-series to kime-surfaces and inference-functions
 +
: ''Abstract'': -->
 +
 +
== Best Student Paper Award==
 +
 +
Congratulations to [https://www.socr.umich.edu/people/SOCR_Personnel_Yueyang.html Yueyang Shen] and [https://math.wisc.edu/graduate-students/ Yupeng Zhang]
 +
on Co-Winning (along with another student, [https://buenfilstats.github.io/ James Buenfil/Washington]) the [https://www.statsinimaging.org/smi_competitions_all/ annual '''"Theory and Methods Track" Best Paper Award''']
 +
of the [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 American Statistical Association (AmStat) - Statistical Methods in Imaging (SMI) Conference].
 +
 +
This paper [https://wiki.socr.umich.edu/images/b/bb/YueyangShen_BestPaper_AmStats_SMI_2024.pdf ''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==
 +
 +
{| class="wikitable" style="text-align: center;"
 +
! Date !! Start !! End !! Duration (minutes) !! Session 1 !! Session 2 
 +
|-
 +
| rowspan="7" | Wednesday 5/29/24 || 8:30 || 10:00 || 90 || Tutorial: Complex-time representation of spatiotemporal processes and spacekime analyhtics; Instructor(s): [https://www.socr.umich.edu/people/dinov/ 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 || colspan="2" | 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 || colspan="2" | Lunch break 
 +
|-
 +
|  13:10 || 14:40 || 90 || colspan="2" | Student Paper Competition Winners; Location: Room 101/102 <br/> 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 <br /> ''Statistical foundations of invariance and equivariance in deep artificial neural network learning''
 +
|-
 +
|  14:40 || 14:50 || 10 || colspan="2" | Break   
 +
|-
 +
|  14:50 || 15:50 || 60 || colspan="2" | Keynote - Bin Yu (Chair: Tingting Zhang); Location: Room 101/102
 +
|-
 +
|  15:50 || 18:00 || 130 || colspan="2" | Poster Presentations/Mixer (food and drinks offered); Location: Room 103/104
 +
|-
 +
| rowspan="10" | Thursday 5/30/24 || 8:00 || 8:30 || 30 || colspan="2" | Breakfast 
 +
|-
 +
|  8:30 || 10:10 || 100 || Longitudinal imaging and biostatistical methods; Session Organizer: [https://www.socr.umich.edu/people/dinov/ 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 || colspan="2" | Break   
 +
|-
 +
|  10:20 || 11:20 || 60 || colspan="2" | Keynote - Andrew J. Saykin (Chair: Sujuan Gao); Location: Room 103/104
 +
|-
 +
|  11:20 || 13:00 || 100 || colspan="2" | 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 || colspan="2" | 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 || colspan="2" | 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 
 +
|-
 +
| rowspan="9" | Friday 5/31/24 || 8:00 || 8:30 || 30 || colspan="2" | 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 || colspan="2" | Break 
 +
|-
 +
|  10:20 || 11:20 || 60 || colspan="2" | Keynote - Robert E. Kass (Chair: Daniel Rowe); Location: Room 103/104 
 +
|-
 +
|  11:20 || 13:00 || 100 || colspan="2" |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 [https://socr.umich.edu/docs/uploads/2024/Shen_StatisticalFoundationsOfInvarianceInDNN_SMI_2024.pdf Yueyang Shen] Jose Rodriguez-Acosta; Location: Room 102 
 +
|-
 +
|  14:40 || 14:50 || 10 || colspan="2" | Break
 +
|-
 +
|  14:50 || 16:30 || 100 || colspan="2" | 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 || colspan="2" | Closing remarks; Location: Room 103/104
 +
|}
  
  
 
<hr>
 
<hr>
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{{translate|pageName=https://wiki.socr.umich.edu/index.php?title=SOCR_News_HDDA_2024}}
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{{translate|pageName=https://wiki.socr.umich.edu/index.php?title=SOCR_News_AmStats_SIM_2024}}

Latest revision as of 10:31, 29 May 2024

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


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