Computational and Network Modeling of Neuroimaging Data: Neuroimaging Methods and Applications
Editat de Kendrick Kayen Limba Engleză Paperback – 18 iun 2024
- 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: 0.74 kg
Editura: ELSEVIER SCIENCE
Seria Neuroimaging Methods and Applications
ISBN-10: 0443134804
Pagini: 354
Dimensiuni: 191 x 235 x 21 mm
Greutate: 0.74 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