ArticleIEEE transactions on medical imaging2018
Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- FPLS-DC: functional partial least squares through distance covariance for imaging genetics.Bioinformatics (Oxford, England) · 2024Article
- A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence.Network neuroscience (Cambridge, Mass.) · 2024Article
- Latent Similarity Identifies Important Functional Connections for Phenotype Prediction.IEEE transactions on bio-medical engineering · 2023Article
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis.IEEE transactions on bio-medical engineering · 2021Article
- Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis.IEEE transactions on medical imaging · 2020Article
- A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.IEEE transactions on bio-medical engineering · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Distance canonical correlation analysis with application to an imaging-genetic study.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Article
- A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Recent advances in imaging genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data. In this paper we present a novel method to detect conditional associations between imaging genetics data. We use projected distance correlation to build a conditional dependency graph among high-dimensional mixed data, then use multiple testing to detect significant group level associations (e.g., ROI-gene). In addition, we introduce a scalable algorithm based on orthogonal greedy algorithm, yielding the greedy projected distance correlation (G-PDC). This can reduce the computational cost, which is critical for analyzing large-volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with GPDC than distance correlation, Pearson's correlation and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies. The Matlab code is available at https://sites.google.com/site/jianfang86/gPDC.
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