Handbook of Functional MRI Data Analysis

Handbook of Functional MRI Data Analysis
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ISBN-13:
9780521517669
Erscheinungsdatum:
01.09.2011
Seiten:
228
Autor:
Russell A. Poldrack
Gewicht:
720 g
Format:
261x177x17 mm
Sprache:
Englisch

Inhaltsverzeichnis
1. Introduction; 2. Image processing; 3. Preprocessing; 4. Normalization; 5. Statistical modeling; 6. Statistical modeling: group analysis; 7. Statistical inference; 8. Connectivity; 9. Visualization; 10. Machine learning; Appendix A. GLM intro/review; Appendix B. Data organization and management; Appendix C. Image formats.
Beschreibung
Using minimal jargon, this book provides a comprehensive and practical introduction to the methods used for fMRI data analysis.
Autor
Dr Russell A. Poldrack is the Director of the Imaging Research Center and Professor of Psychology and Neurobiology at the University of Texas, Austin. He has published more than 100 articles in the field of cognitive neuroscience, in journals including Science, Nature, Neuron, Nature Neuroscience and PNAS. He is well known for his writings on how neuroimaging can be used to make inferences about psychological function, as well as for his research using fMRI and other imaging techniques to understand the brain systems that support learning and memory, decision making and executive function. Dr Jeanette A. Mumford is a Research Assistant Professor in the Department of Psychology at the University of Texas, Austin. Trained in biostatistics, her research has focused on the development and characterisation of new methods for statistical modeling and analysis of fMRI data. Her work has examined the impact of different group modeling strategies and developed new tools for modeling network structure in resting-state fMRI data. She is the developer of the fmriPower software package, which provides power analysis tools for fMRI data. Dr Thomas E. Nichols is the Head of Neuroimaging Statistics at the University of Warwick, United Kingdom. He has been working in functional neuroimaging since 1992, when he joined the University of Pittsburgh's PET Facility as programmer and statistician. He is known for his work on inference in brain imaging, using both parametric and nonparametric methods, and he is an active contributor to the FSL and SPM software packages. In 2009 he received the Wiley Young Investigator Award from the Organization for Human Brain Mapping in recognition for his contributions to statistical modeling and inference of neuroimaging data.

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