Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex | Zendy

Baptiste CouvyDuchesne | Zendy; Futao Zhang | Zendy; Kathryn E. Kemper | Zendy; Julia Sidorenko | Zendy; Naomi R. Wray | Zendy; Peter M. Visscher | Zendy; Jian Yang | Zendy; Olivier Colliot | Zendy

Open Access

Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex

Author(s) -

Baptiste CouvyDuchesne,

Futao Zhang,

Kathryn E. Kemper,

Julia Sidorenko,

Naomi R. Wray,

Peter M. Visscher,

Jian Yang,

Olivier Colliot

Publication year - 2021

Publication title -

medical imaging 2022: image processing

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1117/12.2581022

Subject(s) - univariate , neuroimaging , biobank , trait , false positive paradox , association (psychology) , statistical power , multivariate statistics , cluster (spacecraft) , principal component analysis , false discovery rate , sample size determination , independent component analysis , computer science , correlation , artificial intelligence , biology , statistics , psychology , mathematics , bioinformatics , genetics , machine learning , neuroscience , gene , geometry , psychotherapist , programming language

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