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More About This Title Multimodal Methods of Neuroimaging Connectomes
English
A comprehensive understanding of the brain’s neural connections has the potential to revolutionize neuroscience. In recent years, great research efforts have been exerted in mapping and understanding these connections in various connectome-related projects. Multimodal Methods of Neuroimaging Connectomes is a timely practical guide to applying, developing, and understanding new multimodal methods being used in connectome research.
Multimodal Methods of Neuroimaging Connectomes provides the reader will provide the reader with an overview of existent methods and how they can be combined to provide a multimodal view of the connectome that will bring together anatomical and functional neural connections. Written in a practical and user-friendly manner, the book will be an essential book for anyone working in the field of connectomics.
English
I. Introduction
1. Nodes, edges and networks: Introduction to multimodal human connectome
II. Nodes: identification of interacting units
2. Unique nodes in the continuous (cytoarchitectonics and) structural brain
3. Similar structural connectivity, similar functionality and homogeneous nodes
4. Similar functional connectivity, similar functionality and homogeneous nodes
III. Edges: identification of interactions
5. Anatomical connectivity using DTI
6. fMRI-based functional connectivity
7. M/EEG-based functional connectivity
8. Population-based indirect interregional connectivity (PET, structural MRI)
IV. Network: identification of topology of interactions
9. Identification of hierarchical network organization: hubs, rich clubs
10. Quantification of network organization: node properties, edge properties and global network properties
11. Identification of network modules
V. Multimodal connectome and model-based realistic connectome
12. Multimodal integration of connectivity- DTI+fMRI, DTI+fMRI+MEEG, fMRI+PET, EEG+PET
13. Multimodal integration of networks
14. Model-based, context-dependent effective connectivity
15. Model-based effective networks
16. Visualization of multimodal brain connectome.
VI. Applications
17. Identification of individual variations, context-dependent dynamics and reorganization of the connectome
18. Machine learning for classifying brain states using multimodal connectome
19. Predicting brain states after perturbation using multimodal connectome
