Learning task-agnostic and interpretable subsequence-based representation of time series and its applications in fMRI analysis.

Bai, Wenjun; Yamashita, Okito; Yoshimoto, Junichiro

Bai, Wenjun; Yamashita, Okito; Yoshimoto, Junichiro.

Afiliação

Bai W; Department of Computational Brain Imaging, Advanced Telecommunication Research Institute International, Kyoto, Japan. Electronic address: wjbai@atr.jp.
Yamashita O; Department of Computational Brain Imaging, Advanced Telecommunication Research Institute International, Kyoto, Japan; Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan.
Yoshimoto J; Department of Computational Brain Imaging, Advanced Telecommunication Research Institute International, Kyoto, Japan; Department of Biomedical Data Science, School of Medicine, Fujita Health University, Aichi, Japan; International Center for Brain Science, Fujita Health University, Aichi, Japan.

Neural Netw ; 163: 327-340, 2023 Jun.

Article em En | MEDLINE | ID: mdl-37099896

Assuntos

Mapeamento Encefálico; Encéfalo; Humanos; Encéfalo/diagnóstico por imagem; Encéfalo/fisiologia; Mapeamento Encefálico/métodos; Imageamento por Ressonância Magnética/métodos; Fatores de Tempo; Aprendizagem

Palavras-chave

Multi-task learning paradigm; Subsequence-based time-series analysis; fMRI analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Neural Netw Assunto da revista: NEUROLOGIA Ano de publicação: 2023 Tipo de documento: Article

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