The new edition of the highly popular, The Fractal Geometry of the Brain, reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages. It brings an understanding of fractals to clinicians and researchers even if they do not have a mathematical background, and it serves as a valuable tool for teaching the translational applications of computational fractal-based models to both students and scholars. As a consequence of the novel research developed at Professor Di Ieva's laboratory and other centers around the world, the second edition will explore the use of computational fractal-based analysis in many clinical disciplines and different fields of research, including neurology and neurosurgery, neuroanatomy and psychology, magnetoencephalography (MEG), eye-tracking devices (for the fractal computational characterization of “scanpaths”),deep learning in image analysis, radiomics for the characterization of brain MRIs, characterization of neuropsychological and psychiatric diseases and traits, signal complexity analysis in time series, and functional MRI, amongst others.
The new edition of the highly popular, The Fractal Geometry of the Brain, reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages. It brings an understanding of fractals to clinicians and researchers even if they do not have a mathematical background, and it serves as a valuable tool for teaching the translational applications of computational fractal-based models to both students and scholars. As a consequence of the novel research developed at Professor Di Ieva's laboratory and other centers around the world, the second edition will explore the use of computational fractal-based analysis in many clinical disciplines and different fields of research, including neurology and neurosurgery, neuroanatomy and psychology, magnetoencephalography (MEG), eye-tracking devices (for the fractal computational characterization of “scanpaths”),deep learning in image analysis, radiomics for the characterization of brain MRIs, characterization of neuropsychological and psychiatric diseases and traits, signal complexity analysis in time series, and functional MRI, amongst others.
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