ArticleBioinformatics (Oxford, England)2019
Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's disease.
Article in Bioinformatics (Oxford, England), 2019. 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.
- Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics.Computational and structural biotechnology journal · 2024Article
- Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder.Frontiers in neuroscience · 2023Article
- Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder.Frontiers in psychiatry · 2023Article
- Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data.Frontiers in neuroscience · 2022Article
- Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data.IEEE transactions on medical imaging · 2020Article
- Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment.Bioinformatics (Oxford, England) · 2020Article
- A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.IEEE transactions on bio-medical engineering · 2020Article
- A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.Frontiers in neuroscience · 2020Review
- Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI DatasetFrontiers in neuroscience · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.Computational and mathematical methods in medicine · 2019Article
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Authors and funding
5 authors.
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Abstract
motivationNeuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimer's disease (AD)].
resultsA general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Second, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at http://ibrain.nuaa.edu.cn/2018/list.htm.
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