Introduction to Resting State fMRI Functional Connectivity

 
 
Oxford University Press
  • 1. Auflage
  • |
  • erschienen am 19. Mai 2017
  • |
  • 200 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-0-19-253574-0 (ISBN)
 
Spontaneous 'resting-state' fluctuations in neuronal activity offer insights into the inherent organisation of the human brain, and may provide markers for diagnosis and treatment of mental disorders. Resting state functional magnetic resonance imaging (fMRI) can be used to investigate intrinsic functional connectivity networks, which are identified based on similarities in the signal measured from different regions. From data acquisition to results interpretation, An Introduction to Resting State fMRI Functional Connectivity discusses a wide range of approaches without expecting previous knowledge of the reader, making it truly accessible to readers from a broad range of backgrounds. Supplemented with online examples to enable the reader to obtain hands-on experience working with data, the text also provides details to enhance learning for those already experienced in the field. The Oxford Neuroimaging Primers are written for new researchers or advanced undergraduates in neuroimaging to provide a thorough understanding of the ways in which neuroimaging data can be analysed and interpreted. Aimed at students without a background in mathematics or physics, this book is also important reading for those familiar with task fMRI but new to the field of resting state fMRI.
  • Englisch
  • Oxford
  • |
  • Großbritannien
  • 19,81 MB
978-0-19-253574-0 (9780192535740)
weitere Ausgaben werden ermittelt
Dr Janine D. Bijsterbosch is currently a postdoctoral researcher in the Analysis Group in the FMRIB Centre, University of Oxford, specialising in resting-state analysis. She has worked in brain imaging since 2007, with a background in psychology and experience working in psychology, psychiatry and neuroscience. Janine has considerable teaching experience, being the course organiser, lecturer and senior tutor on both the FMRIB Graduate Programme and the FSL Course. Professor Stephen M. Smith is a Professor of Biomedical Engineering in the FMRIB Centre, University of Oxford and founded the Analysis Group there in 1997. He is the co-founder of FSL (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl) and has written many tools for analysis of structural, diffusion and functional data, with a recent emphasis on resting-state imaging. In 2007 Stephen received the Wiley Young Investigator Award from the Organisation for Human Brain Mapping. Professor Christian F. Beckmann is a Professor of Statistics in Imaging Neuroscience at the Radboud University Medical Centre Nijmegen and leads the Statistical Imaging Neurosciences group at the Donders Institute for Brain, Cognition and Behaviour in Nijmegen. His research focuses on Independent Component Analysis in fMRI, utilising information theoretic principles for the development of imaging biomarkers, advanced diagnosis systems and understanding of the human brain. Christian has taught on the FSL Course since its inception in 2002 and in 2011 he received the Wiley Young Investigator Award from the Organisation for Human Brain Mapping.
  • Cover
  • Series
  • oxford neuroimaging primers Introduction to Resting State fMRI Functional Connectivity
  • Copyright
  • Contents
  • 1 Introduction
  • 1.1 From neural activity to functional connectivity
  • Box 1.1: Neuronal activity and local field potential
  • 1.2 What is a resting state network (RSN)?
  • 1.3 What can be gained from investigating the resting brain?
  • 1.4 Resting state fMRI signal properties
  • 1.5 Mapping the human connectome
  • Summary
  • Further reading
  • 2 Data Acquisition
  • 2.1 Repetition time, voxel size, and coverage
  • 2.2 Multiband EPI sequence
  • 2.3 Multi-?echo EPI sequence
  • 2.4 Distortion, shimming, and fieldmaps
  • 2.5 Scan duration
  • 2.6 Eyes open versus eyes closed
  • 2.7 Motion and physiological confounds
  • Summary
  • Further reading
  • 3 Data Preprocessing
  • 3.1 Sources of structured noise
  • 3.2 Conventional preprocessing steps
  • 3.3 Low-?pass temporal filtering
  • 3.4 Nuisance regression
  • 3.5 Global signal regression
  • 3.6 Physiological noise regression
  • 3.7 Volume censoring
  • 3.8 Independent component analysis
  • Example box: Single subject ICA
  • General statistics box: Multiple linear regression analysis (with the GLM)
  • Summary
  • Further reading
  • 4 Voxel-?based Connectivity Analyses
  • 4.1 Seed-?based correlation analysis
  • Example box: Seed-based correlation analysis
  • 4.2 Independent component analysis
  • Box 4.1: The ICA model
  • Box 4.2: How does the ICA "unmixing" work?
  • Example box: Group-ICA networks from different datasets
  • 4.3 Obtaining subject-?wise ICA estimates with dual regression
  • Example box: Visualizing dual regression group analysis results
  • 4.4 Amplitude of low-frequency fluctuations
  • 4.5 Regional homogeneity
  • 4.6 Group-level analysis for voxel-?based methods
  • General statistics box: Multiple comparisons correction
  • Summary
  • Further reading
  • 5 Node-?based Connectivity Analyses
  • 5.1 What is a node?
  • 5.2 Node definition
  • Example box: Examples of node parcellations
  • 5.3 Timecourse extraction
  • 5.4 Edge definition
  • 5.5 Network modeling analysis
  • Example box: Calculating subject and group network matrices
  • 5.6 Graph theory analysis
  • 5.7 Dynamic causal modeling
  • 5.8 Dynamic and non-?stationary methods
  • 5.9 When to use voxel-?based versus node-?based approaches
  • Summary
  • Further reading
  • 6 Interpretation
  • 6.1 The impact of psychology
  • 6.2 The effects of BOLD physiology
  • 6.3 The effects of methodological choices
  • 6.4 Complementary types of connectivity research
  • 6.5 Conclusions
  • Summary
  • Further reading
  • Index

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