Computational and Network Modeling of Neuroimaging Data: Neuroimaging Methods and Applications
Editat de Kendrick Kayen Limba Engleză Paperback – 18 iun 2024
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.
- Provides an authoritative and comprehensive overview of major modeling approaches to neuroimaging data
- Written by experts, the book's chapters use a common structure to introduce, motivate, and describe a specific modeling approach used in neuroimaging
- Gives insights into the similarities and differences across different modeling approaches
- Analyses details of outstanding research challenges in the field
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Specificații
ISBN-13: 9780443134807
ISBN-10: 0443134804
Pagini: 354
Dimensiuni: 191 x 235 x 21 mm
Greutate: 0.7 kg
Editura: ELSEVIER SCIENCE
Seria Neuroimaging Methods and Applications
ISBN-10: 0443134804
Pagini: 354
Dimensiuni: 191 x 235 x 21 mm
Greutate: 0.7 kg
Editura: ELSEVIER SCIENCE
Seria Neuroimaging Methods and Applications
Cuprins
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
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