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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.
Nearly one million people take their own lives each year world-wide - however, contrary to popular belief, suicide can be prevented. While suicide is commonly thought to be an understandable reaction to severe stress, it is actually an abnormal reaction to regular situations. Something more than unbearable stress is needed to explain suicide, and neuroscience shows what this is, how it is caused and how it can be treated. Professor Kees van Heeringen describes findings from neuroscientific research on suicide, using various approaches from population genetics to brain imaging. Compelling evidence is reviewed that shows how and why genetic characteristics or early traumatic experiences may lead to a specific predisposition that makes people vulnerable to triggering life events. Neuroscientific studies are yielding results that provide insight into how the risk of suicide may develop; ultimately demonstrating how suicide can be prevented.