Network-Assisted Mediation Analysis with High-Dimensional Neuroimaging Mediators

Baoyi Shi, Ying Liu, Shanghong Xie, Xi Zhu, Yuanjia Wang
Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 2024.

Abstract

Mediation analysis is a widely used statistical approach to estimate the causal pathways through which an exposure affects an outcome via intermediate variables, i.e., mediators. In many applications, high-dimensional correlated biomarkers are potential mediators, posing challenges to standard mediation analysis approaches. However, some of these biomarkers, such as neuroimaging measures across brain regions, often exhibit hierarchical network structures that can be leveraged to advance mediation analysis. In this paper, we aim to study how brain cortical thickness, characterized by a star-shaped hierarchical network structure, mediates the effect of maternal smoking on children’s cognitive abilities within the adolescent brain cognitive development (ABCD) study. We propose a network-assisted mediation analysis approach based on a conditional Gaussian graphical model to account for the star-shaped network structure of neuroimaging mediators. Within our framework, the joint indirect effect of these mediators is decomposed into the indirect effect through hub mediators and the indirect effects solely through each leaf mediator. This decomposition provides mediator-specific insights and informs efficient intervention designs. Additionally, after accounting for hub mediators, the indirect effects solely through each leaf mediator can be identified and evaluated individually, thereby addressing the challenges of high-dimensional correlated mediators. In our study, our proposed approach identifies a brain region as a significant leaf mediator, a finding that existing approaches cannot discover.

Cite this Paper

BibTeX
@InProceedings{pmlr-v252-shi24a, title = {Network-Assisted Mediation Analysis with High-Dimensional Neuroimaging Mediators}, author = {Shi, Baoyi and Liu, Ying and Xie, Shanghong and Zhu, Xi and Wang, Yuanjia}, booktitle = {Proceedings of the 9th Machine Learning for Healthcare Conference}, year = {2024}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo}, volume = {252}, series = {Proceedings of Machine Learning Research}, month = {16--17 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v252/main/assets/shi24a/shi24a.pdf}, url = {https://proceedings.mlr.press/v252/shi24a.html}, abstract = {Mediation analysis is a widely used statistical approach to estimate the causal pathways through which an exposure affects an outcome via intermediate variables, i.e., mediators. In many applications, high-dimensional correlated biomarkers are potential mediators, posing challenges to standard mediation analysis approaches. However, some of these biomarkers, such as neuroimaging measures across brain regions, often exhibit hierarchical network structures that can be leveraged to advance mediation analysis. In this paper, we aim to study how brain cortical thickness, characterized by a star-shaped hierarchical network structure, mediates the effect of maternal smoking on children’s cognitive abilities within the adolescent brain cognitive development (ABCD) study. We propose a network-assisted mediation analysis approach based on a conditional Gaussian graphical model to account for the star-shaped network structure of neuroimaging mediators. Within our framework, the joint indirect effect of these mediators is decomposed into the indirect effect through hub mediators and the indirect effects solely through each leaf mediator. This decomposition provides mediator-specific insights and informs efficient intervention designs. Additionally, after accounting for hub mediators, the indirect effects solely through each leaf mediator can be identified and evaluated individually, thereby addressing the challenges of high-dimensional correlated mediators. In our study, our proposed approach identifies a brain region as a significant leaf mediator, a finding that existing approaches cannot discover.} }
Endnote
%0 Conference Paper %T Network-Assisted Mediation Analysis with High-Dimensional Neuroimaging Mediators %A Baoyi Shi %A Ying Liu %A Shanghong Xie %A Xi Zhu %A Yuanjia Wang %B Proceedings of the 9th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2024 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %F pmlr-v252-shi24a %I PMLR %U https://proceedings.mlr.press/v252/shi24a.html %V 252 %X Mediation analysis is a widely used statistical approach to estimate the causal pathways through which an exposure affects an outcome via intermediate variables, i.e., mediators. In many applications, high-dimensional correlated biomarkers are potential mediators, posing challenges to standard mediation analysis approaches. However, some of these biomarkers, such as neuroimaging measures across brain regions, often exhibit hierarchical network structures that can be leveraged to advance mediation analysis. In this paper, we aim to study how brain cortical thickness, characterized by a star-shaped hierarchical network structure, mediates the effect of maternal smoking on children’s cognitive abilities within the adolescent brain cognitive development (ABCD) study. We propose a network-assisted mediation analysis approach based on a conditional Gaussian graphical model to account for the star-shaped network structure of neuroimaging mediators. Within our framework, the joint indirect effect of these mediators is decomposed into the indirect effect through hub mediators and the indirect effects solely through each leaf mediator. This decomposition provides mediator-specific insights and informs efficient intervention designs. Additionally, after accounting for hub mediators, the indirect effects solely through each leaf mediator can be identified and evaluated individually, thereby addressing the challenges of high-dimensional correlated mediators. In our study, our proposed approach identifies a brain region as a significant leaf mediator, a finding that existing approaches cannot discover.
APA
Shi, B., Liu, Y., Xie, S., Zhu, X. & Wang, Y.. (2024). Network-Assisted Mediation Analysis with High-Dimensional Neuroimaging Mediators. Proceedings of the 9th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 252 Available from https://proceedings.mlr.press/v252/shi24a.html.

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