ArticleInternational journal of methods in psychiatric research2018
Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains.
Article in International journal of methods in psychiatric research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Using hierarchical unsupervised learning to integrate and reduce multi-level and multi-paraspinal muscle MRI data in relation to low back pain.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Aging-Dependent Genetic Effects Associated to ADHD Predict Longitudinal Changes of Ventricular Volumes in Adulthood.Frontiers in psychiatry · 2020Article
- Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains.International journal of methods in psychiatric research · 2018Article
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Authors and funding
8 authors.
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Abstract
objectivesWe proposed the application of a multivariate cross-sectional framework based on a combination of a variable selection method and a multiple factor analysis (MFA) in order to identify complex meaningful biological signals related to attention-deficit/hyperactivity disorder (ADHD) symptoms and hyperactivity/inattention domains.
methodsThe study included 135 children from the general population with genomic and neuroimaging data. ADHD symptoms were assessed using a questionnaire based on ADHD-DSM-IV criteria. In all analyses, the raw sum scores of the hyperactivity and inattention domains and total ADHD were used. The analytical framework comprised two steps. First, zero-inflated negative binomial linear model via penalized maximum likelihood (LASSO-ZINB) was performed. Second, the most predictive features obtained with LASSO-ZINB were used as input for the MFA.
resultsWe observed significant relationships between ADHD symptoms and hyperactivity and inattention domains with white matter, gray matter regions, and cerebellum, as well as with loci within chromosome 1.
conclusionsMultivariate methods can be used to advance the neurobiological characterization of complex diseases, improving the statistical power with respect to univariate methods, allowing the identification of meaningful biological signals in Imaging Genetic studies.
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