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Activity Number: 562 - Regression Methods for Neuroimaging Data
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #323667
Title: Mediation Analysis for Multimodal Neuroimaging Data with Application to Alzheimer’s Disease
Author(s): Zhe Sun* and Yize Zhao
Companies: Yale University and Yale University
Keywords: Mediation Analysis; Alzheimer’s Disease; Neuroimaging measures; Imaging genetics; Multimodal data integration; Regularization

Mediation modeling has become an important tool in the psychological research for studying the intermediate effect of neuroimaging biomarkers on the causal pathway from genetic variations to diagnostic outcomes. One the other side, researchers begin combining of multiple types of measurements, which are scientifically complementary, to strengthen our understanding of brain dynamics and their associations with neurological disorders. However, little work has been done when the mediator is high-dimensional multimodal neuroimaging data. We propose a mediation model framework with functional connectivity data and multiple region-level imaging data as mediators, and we conduct model estimation by imposing group sparsity penalty and graph based Laplacian penalty. We illustrate our method with a multimodal brain pathway analysis having both positron emission tomography (PET) and magnetic resonance imaging (MRI) measurements as mediators in the association between Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) and single nucleotide polymorphisms (SNPs) in the APOE gene, identifying which brain locations in each modality mediate the relationship.

Authors who are presenting talks have a * after their name.

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