ReviewFrontiers in neuroinformatics2014
A review of multivariate analyses in imaging genetics.
Review in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Mapping Focal and Generalized Effects of Common Genetic Variants on Human Brain Structure.bioRxiv : the preprint server for biology · 2026Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Editorial: Multimodal brain data integration and computational modeling.Frontiers in neuroinformatics · 2026Article
- Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children.Nature communications · 2025Article
- Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis.Brain imaging and behavior · 2025Article
- Multivariate Analyses with Two-Step Dimension Reduction for an Association Study BetweenBioengineering (Basel, Switzerland) · 2025Article
- Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.Human brain mapping · 2024Article
- A novel classification framework for genome-wide association study of whole brain MRI images using deep learning.PLoS computational biology · 2024Article
- A multivariate to multivariate approach for voxel-wise genome-wide association analysis.Statistics in medicine · 2024Article
- FPLS-DC: functional partial least squares through distance covariance for imaging genetics.Bioinformatics (Oxford, England) · 2024Article
- Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.Human brain mapping · 2023Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.BMC bioinformatics · 2021Article
- Article
- Cognitive Development and Brain Gray Matter Susceptibility to Prenatal Adversities: Moderation by the Prefrontal Cortex Brain-Derived Neurotrophic Factor Gene Co-expression Network.Frontiers in neuroscience · 2021Article
- L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses.Journal of the American Statistical Association · 2020Article
- Distinct structural brain circuits indicate mood and apathy profiles in bipolar disorder.NeuroImage. Clinical · 2020Article
- Amygdala 5-HTT Gene Network Moderates the Effects of Postnatal Adversity on Attention Problems: Anatomo-Functional Correlation and Epigenetic Changes.Frontiers in neuroscience · 2020Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.
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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.