Machine Learning for Brain Disorders
Machine Learning for Brain Disorders by Olivier Colliot, published by Springer US on July 25, 2023, is an Open Access volume comprising 1,047 pages. This book provides a comprehensive guide to the methodological and applicative aspects of machine learning (ML) in the context of brain disorders. It is organized into five parts, covering the fundamentals of ML, types of data relevant to brain disorders, core methodologies, validation and datasets, and applications of ML across various neurological and psychiatric conditions.
Readers will find detailed chapters that include essential advice from specialists, aimed at achieving successful results in laboratory settings. The book addresses a range of topics, including clinical assessments, neuroimaging, and genetics, making it a valuable resource for both newcomers and experienced researchers in the fields of neuroscience and medical science. It serves as a practical reference for students and professionals such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists looking to deepen their understanding of machine learning applications in brain disorders.
Official synopsis Publisher
This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.
Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.
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