Sign in to rate or review. Barcode Alex's research uses cognitive neuroscience, network science, and graph theory to understand brain network organization in health and disease. He has published over scientific articles, much of which are focused on the development and application of new methods to understand how brain networks dynamically adapt to changing task demands, how they are disrupted by disease, and how they are shaped by genetic influences. He works with neuroscientists, utilizing his engineering expertise in networks to understand human brain organization in health and disease.
He has developed widely used methods for modeling and performing statistical inference on brain imaging data. His methods are utilized to investigate brain connectivity abnormalities in disease. He identified some of the first evidence of connectome pathology in schizophrenia.
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He is based at the University of Melbourne and holds a joint appointment between the Melbourne Neuropsychiatry Centre and the Melbourne School of Engineering. He leads the Systems Neuropsychiatry Group. Since , he has worked half-time for GlaxoSmithKline, currently focusing on immuno-psychiatry. He has published about scientific papers, and his work has been highly cited.
He has played an internationally-leading role in understanding brain connectivity and networks by graph theoretical analysis of neuroimaging and other neuroscientific datasets.
Fundamentals Brain Network Analysis by Alex Fornito Andrew Zalesky Edward
What patterns of connections allow us to think the way we do, or distinguish our ways of thinking from those of other animals? These and related questions are the bread and butter of an excellent new textbook. Fundamentals of Brain Network Analysis by Fornito, Zalesky and Bullmore, is a thorough and didactic presentation of the tools available to research scientists wishing to engage in the emerging field of network neuroscience Bullmore and Sporns, Blending computational tools and mathematical frameworks from physics, engineering, statistics, and computer science with the reams of data now being collected from diverse neural systems, network neuroscience is a truly interdisciplinary and ground-breaking field poised to transform our understanding of the brain.
Rather than focusing solely on the function of single neurons or brain regions, these efforts expand the purview of our interests to the pattern of interactions between neural elements, suggesting that these patterns can offer significant insight into the workings of the mind Fig. Indeed, a network or graph representation—in which component parts are represented as network nodes, and in which the interaction between two nodes is represented by an edge—offers a mathematical construct for studying, predicting, and controlling exactly these patterns Bollobas, Because of its transdisciplinary nature as well as its relative infancy, network neuroscience has remained a challenging topic for professors to teach and students to learn.
The difficulty largely lies in the lack of a quintessential text. This beautiful tome therefore fills a much-needed gap, offering the first and only textbook-style presentation of the network-based tools that have proven useful in addressing specific questions and hypotheses in the neurosciences.
The book will undoubtedly serve as a critical resource for both students and teachers alike as network neuroscience becomes a ubiquitous topic in graduate education in the neurosciences. The book is co-written by three seminal scientists in the field, who each lend their expertise and knowledge to make this piece a particularly rich read. Indeed, their collective background in psychology, psychiatry, and engineering perfectly complements the interdisciplinary nature of the topics that they cover.
Fundamentals of Brain Network Analysis — Monash University
While remaining true to the mathematical complexity and theoretical depth of the ideas Estrada and Knight, , the authors are careful to consistently point out the relevance of these tools for understanding cognition and behaviour Medaglia et al. To organize this expansive content, the book walks step-by-step through the building blocks that compose a network, and how these building blocks can be extracted from different types of neural data, across species, spatial scales, and imaging modalities Oh et al.
The bulk of the remaining exposition delineates how networks can be described, characterizing their structure at local, meso-scale, and global levels. From hubs and rich-clubs to small worlds and modules, the text provides a surprisingly accessible and yet thorough tutorial on the methods underpinning these notions, and how to accurately use them on neural data. These topics are complemented by a particularly useful presentation of the parametric and non-parametric approaches that have been developed for statistical inference in networks, as well as a discussion of the considerations that are particularly relevant for brain networks derived from imaging data.
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The authors also touch on the applicability of powerful machine learning techniques to problems in brain network analysis. Arguably the greatest boon that the book offers to the budding network neuroscientist is the unbridled use of full-colour images taken directly from recently published journal articles in the field. While their aesthetic beauty draws the reader in, the content of the figures directly demonstrates the use of network-based tools to address critical and long-standing questions in the neurosciences, as well as to test explicitly network-based hypotheses regarding the workings of the mind.
The reader is struck with just how vast the field already is, and moreover is motivated to pick up the tools offered and use them to better tackle their own personal research questions. Looking forward, this text promises to be an essential title on the bookshelf of the intellectually curious neuroscientist.
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Indeed, as with any good book, one turns the final page wishing there was more. Yet in that remaining void, the student is invited to pioneer the next cutting edge advances to help us better understand the networks that support scientific inquiry. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.
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Graph theory methods: applications in brain networks
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