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Brain network analysis

By: Chung, Moo K.
Publisher: Cambridge Cambridge University Press 2019Description: xii, 329p.ISBN: 9781107184862.Subject(s): Brain-physiology | Nerve net-physiologyDDC classification: 612.82 | C472b Summary: This tutorial reference serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook for graduate students while simultaneously articulating important and technically challenging topics. Whereas most available books are graph theory-centric, this text introduces techniques arising from graph theory and expands to include other different models in its discussion on network science, regression, and algebraic topology. Links are included to the sample data and codes used in generating the book's results and figures, helping to empower methodological understanding in a manner immediately usable to both researchers and students.
List(s) this item appears in: New arrival Sep. 23 to 29, 2019 | New arrival 23 to 29 September 2019
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Item type Current location Collection Call number Status Date due Barcode Item holds
Books Books P K Kelkar Library, IIT Kanpur
General Stacks 612.82 C472b (Browse shelf) Available A184825
Total holds: 0

This tutorial reference serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook for graduate students while simultaneously articulating important and technically challenging topics. Whereas most available books are graph theory-centric, this text introduces techniques arising from graph theory and expands to include other different models in its discussion on network science, regression, and algebraic topology. Links are included to the sample data and codes used in generating the book's results and figures, helping to empower methodological understanding in a manner immediately usable to both researchers and students.

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