SMI 2022 - Recent advances in neuroimaging statistics for investigating human brain function and disorders

Friday, May 27, 2022 • 4:50–6:05 pm (CT) Light Hall, Room 214

Organizer and chair: Joshua Lukemire, Emory University


Hierarchical tree data in regularized regression: A path analysis perspective

Yi Zhao, Indiana University

Bingkai Wang, University of Pennsylvania

Xi Luo, University of Texas Health Science Center

Chin-Fu Liu, Johns Hopkins University

Andreia V. Faria, Johns Hopkins University

Michael I. Miller, Johns Hopkins University

Brian S. Caffo, Johns Hopkins University

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an L1-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in L2-norm and the model selection is also consistent. When applied to a brain structural magnetic resonance imaging dataset acquired from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation.


A blind source separation method for investigating functional and structural networks in brain connectome

Ben Wu, Renmin University of China 

Jian Kang, University of Michigan

Ying Guo, Emory University

There is a strong interest in analyzing multimodal brain networks in recent years. Integrating information from multimodal connections can potentially help better understand the formation and alteration in brain connectors due to neurodevelopment and disease progression. Investigating the interplay among multimodal brain networks is challenging due to several reasons such as the high noise of the imaging data, the different measures of connectivity across modalities, etc. In this talk, we will introduce a new blind source separation method that can be applied to decompose discrete representations of brain networks and achieve joint analysis of multimodal connections. We demonstrate our method with comprehensive simulations and present our findings on functional and structural brain connectivity from a real data study. 


Mapping the genetic-imaging-clinical pathway with applications to Alzheimer's disease

Dehan Kong, University of Toronto

Dengdeng Yu, University of Texas at Arlington

Linbo Wang, University of Toronto

Hongtu Zhu, University of North Carolina at Chapel Hill

Alzheimer’s disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer's disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically regulated brain changes that drive disease progression based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimers Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions.


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