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Machine learning for brain disorders
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.
Autistic community and the neurodiversity movement : Stories from the frontline
This book marks the first historical overview of the autism rights branch of the neurodiversity movement, describing the activities and rationales of key leaders in their own words since it organized into a unique community in 1992. Sandwiched by editorial chapters that include critical analysis, the book contains 19 chapters by 21 authors about the forming of the autistic community and neurodiversity movement, progress in their influence on the broader autism community and field, and their possible threshold of the advocacy establishment
