Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data.

Vo, Tien; Mishra, Akshay; Ithapu, Vamsi; Singh, Vikas; Newton, Michael A

Vo, Tien; Mishra, Akshay; Ithapu, Vamsi; Singh, Vikas; Newton, Michael A.

Afiliação

Vo T; Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Mishra A; Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Ithapu V; Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Singh V; Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Newton MA; Department of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.

Biostatistics ; 23(3): 860-874, 2022 07 18.

Article em En | MEDLINE | ID: mdl-33616173

Assuntos

Encéfalo; Imageamento por Ressonância Magnética; Teorema de Bayes; Encéfalo/diagnóstico por imagem; Humanos; Projetos de Pesquisa

Palavras-chave

Empirical Bayes; Graph-respecting partition; GraphMM; Image analysis; Local false-discovery rate; Mixture model

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Imageamento por Ressonância Magnética Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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