| 書目名稱 | Pattern Analysis of the Human Connectome | | 編輯 | Dewen Hu,Ling-Li Zeng | | 視頻video | http://file.papertrans.cn/743/742508/742508.mp4 | | 概述 | Presenting recent advances in pattern analysis of the human connectome.Focusing mainly on advances made over the past decade in the field of manifold learning, sparse coding, multi-task learning, and | | 圖書封面 |  | | 描述 | .This book presents recent advances in pattern analysis of the human connectome. The human connectome, measured by magnetic resonance imaging at the macroscale, provides a comprehensive description of how brain regions are connected. Based on machine learning methods, multiviarate pattern analysis can directly decode psychological or cognitive states from brain connectivity patterns. Although there are a number of works with chapters on conventional human connectome encoding (brain-mapping), there are few resources on human connectome decoding (brain-reading). Focusing mainly on advances made over the past decade in the field of manifold learning, sparse coding, multi-task learning, and deep learning of the human connectome and applications, this book helps students and researchers gain an overall picture of pattern analysis of the human connectome. It also offers valuable insights for clinicians involved in the clinical diagnosis and treatment evaluation of neuropsychiatric disorders.. | | 出版日期 | Book 2019 | | 關鍵詞 | Human connectome; Magnetic resonance imaging; Manifold learning; Sparse coding; Deep learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-32-9523-0 | | isbn_softcover | 978-981-32-9525-4 | | isbn_ebook | 978-981-32-9523-0 | | copyright | Springer Nature Singapore Pte Ltd. 2019 |
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