ArticleMedical image analysis2014
Correspondence between fMRI and SNP data by group sparse canonical correlation analysis.
Article in Medical image analysis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it, 144 citations in OpenAlex.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Trustworthy causal biomarker discovery: a multiomics brain imaging genetics-based approach.Bioinformatics (Oxford, England) · 2025Article
- Mutual-assistance learning for trustworthy biomarker discovery and disease prediction.Briefings in bioinformatics · 2025Article
- Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.Briefings in bioinformatics · 2024Article
- Multi-Group Tensor Canonical Correlation Analysis.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Longitudinal Canonical Correlation Analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics.Genomics, proteomics & bioinformatics · 2023Article
- Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information.Frontiers in genetics · 2023Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage.Human brain mapping · 2022Article
- Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network.Scientific reports · 2022Article
- Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach.Journal of molecular neuroscience : MN · 2022Article
- Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints.Medical & biological engineering & computing · 2022Article
- Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.IEEE transactions on medical imaging · 2021Article
- Deep Generative Analysis for Task-Based Functional MRI Experiments.Proceedings of machine learning research · 2021Article
- An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer's Disease.Frontiers in aging neuroscience · 2021Article
- Genome-wide association studies of brain imaging data via weighted distance correlation.Bioinformatics (Oxford, England) · 2020Article
- Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.IEEE journal of biomedical and health informatics · 2020Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
Both genetic variants and brain region abnormalities are recognized as important factors for complex diseases (e.g., schizophrenia). In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI) to understand how genetic variation influences the brain activity. A group sparse canonical correlation analysis method (group sparse CCA) was developed to explore the correlation between these two datasets which are high dimensional-the number of SNPs/voxels is far greater than the number of samples. Different from the existing sparse CCA methods (sCCA), our approach can exploit structural information in the correlation analysis by introducing group constraints. A simulation study demonstrates that it outperforms the existing sCCA. We applied this method to the real data analysis and identified two pairs of significant canonical variates with average correlations of 0.4527 and 0.4292 respectively, which were used to identify genes and voxels associated with schizophrenia. The selected genes are mostly from 5 schizophrenia (SZ)-related signalling pathways. The brain mappings of the selected voxles also indicate the abnormal brain regions susceptible to schizophrenia. A gene and brain region of interest (ROI) correlation analysis was further performed to confirm the significant correlations between genes and ROIs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.