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.Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | PK Kelkar Library, IIT Kanpur | General Stacks | 612.82 C472b (Browse shelf) | Available | A184825 |
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
612.82 B989r Rhythms of the brain | 612.82 C139C CEREBRAL CODE | 612.82 C278 Casting light on the dark side of brain imaging | 612.82 C472b Brain network analysis | 612.82 C65G3 THE COGNITIVE NEUROSCIENCES | 612.82 Ec27f FACING REALITY | 612.82 Ed33n NEURAL DARWINISM |
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.
There are no comments for this item.