Computational and Network Modeling of Neuroimaging Data

Herausgeber: Kay, Kendrick

• Broschiertes Buch

Produktbeschreibung

Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data. As neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired, this book gives an accessible foundation to the field of computational neuroimaging, suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging. It is widely recognized that effective interpretation and extraction of information from complex data requires quantitative modeling. However, modeling the brain comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. This book takes a critical step towards synthesizing and integrating across different modeling approaches.

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Produktdetails

• Neuroimaging Methods and Applications
• Verlag: Elsevier Science Publishing Co Inc
• Seitenzahl: 354
• Erscheinungstermin: 18. Juni 2024
• Englisch
• Abmessung: 191mm x 234mm x 21mm
• Gewicht: 740g
• ISBN-13: 9780443134807
• ISBN-10: 0443134804
• Artikelnr.: 68747142

Inhaltsangabe

1. Statistical modeling: Harnessing uncertainty and variation in
neuroimaging data
2. Sensory modeling: Understanding computation in sensory systems through
image-computable models
3. Cognitive modeling: Joint models use cognitive theory to understand
brain activations
4. Network modeling: The explanatory power of activity flow models of brain
function
5. Biophysical modeling: An approach for understanding the physiological
fingerprint of the BOLD fMRI signal
6. Biophysical modeling: Multicompartment biophysical models for brain
tissue microstructure imaging
7. Dynamic brain network models: How interactions in the structural
connectome shape brain dynamics
8. Neural graph modelling
9. Machine learning and neuroimaging: Understanding the human brain in
health and disease
10. Decoding models: From brain representation to machine interfaces
11. Normative modeling for clinical neuroscience