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Statistical approaches for resting state fMRI data analysis

Guorong Wu (UGent)
(2015)
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(UGent)
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Abstract
This doctoral dissertation investigates the methodology to explore brain dynamics from resting state fMRI data. A standard resting state fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. There are two main objectives in the analysis of these noisy data: establishing the link between neural activity and the measured signal, and determining distributed brain networks that correspond to brain function. These measures can then be used as indicators of psychological, cognitive or pathological states. Two main issues will be addressed: retrieving and interpreting the hemodynamic response function (HRF) at rest, and dealing with the redundancy inherent to fMRI data. Novel approaches are introduced, discussed and validated on simulated data and on real datasets, in health and disease, in order to track modulation of brain dynamics and HRF across different pathophysiological conditions.
Keywords
resting state, point process, fMRI, hemodynamic response function, dynamical systems

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Citation

Please use this url to cite or link to this publication:

MLA
Wu, Guorong. “Statistical Approaches for Resting State fMRI Data Analysis.” 2015 : n. pag. Print.
APA
Wu, Guorong. (2015). Statistical approaches for resting state fMRI data analysis. Ghent University. Faculty of Psychology and Educational Sciences, Ghent, Belgium.
Chicago author-date
Wu, Guorong. 2015. “Statistical Approaches for Resting State fMRI Data Analysis”. Ghent, Belgium: Ghent University. Faculty of Psychology and Educational Sciences.
Chicago author-date (all authors)
Wu, Guorong. 2015. “Statistical Approaches for Resting State fMRI Data Analysis”. Ghent, Belgium: Ghent University. Faculty of Psychology and Educational Sciences.
Vancouver
1.
Wu G. Statistical approaches for resting state fMRI data analysis. [Ghent, Belgium]: Ghent University. Faculty of Psychology and Educational Sciences; 2015.
IEEE
[1]
G. Wu, “Statistical approaches for resting state fMRI data analysis,” Ghent University. Faculty of Psychology and Educational Sciences, Ghent, Belgium, 2015.
@phdthesis{6961907,
  abstract     = {This doctoral dissertation investigates the methodology to explore brain dynamics from resting state fMRI data. A standard resting state fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. There are two main objectives in the analysis of these noisy data: establishing the link between neural activity and the measured signal, and determining distributed brain networks that correspond to brain function. These measures can then be used as indicators of psychological, cognitive or pathological states. Two main issues will be addressed: retrieving and interpreting the hemodynamic response function (HRF) at rest, and dealing with the redundancy inherent to fMRI data. Novel approaches are introduced, discussed and validated on simulated data and on real datasets, in health and disease, in order to track modulation of brain dynamics and HRF across different pathophysiological conditions.},
  author       = {Wu, Guorong},
  isbn         = {9789461973207},
  keywords     = {resting state,point process,fMRI,hemodynamic response function,dynamical systems},
  language     = {eng},
  pages        = {XI, 116},
  publisher    = {Ghent University. Faculty of Psychology and Educational Sciences},
  school       = {Ghent University},
  title        = {Statistical approaches for resting state fMRI data analysis},
  year         = {2015},
}