2012/06/01 by Wonsang You, Sophie Achard, Jörg Stadler +4
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Mathematics · Neuroscience · #Basis (linear algebra) #Complex Systems and Time Series Analysis #Estimator #Fractal #Fractal analysis #Fractal and DNA sequence analysis #Functional connectivity #Gaussian #Neural dynamics and brain function #Noise (video) #Population #Resting state fMRI #q-bio.NC #stat.AP
paper · pdf · doi:10.1109/ijcnn.2012.6252657
published as The 2012 International Joint Conference on Neural Networks, pp.1-8, 10-15 June 2012 · The 2012 International Joint Conference on Neural Networks
openalex publication_date 2012/06/01 · arxiv created 2012/08/04 · arxiv updated 2012/08/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
A variety of resting state neuroimaging data tend to exhibit fractal behavior where their power spectrums follow power-law scaling. Resting state functional connectivity is significantly influenced by fractal behavior which may not directly originate from neuronal population activities of the brain. To describe the fractal behavior, we adopted the fractionally integrated process (FIP) model instead of the fractional Gaussian noise (FGN) since the FIP model covers more general aspects of fractality than the FGN model. This model provides a theoretical basis for the dependence of resting state functional connectivity on fractal behavior. Inspired by this idea, we introduce a novel concept called the nonfractal connectivity which is defined as the correlation of short memory independent of fractal behavior, and compared it with the fractal connectivity which is an asymptotic wavelet correlation. We propose several wavelet-based estimators of fractal connectivity and nonfractal connectivity for a multivariate fractionally integrated noise (mFIN). These estimators were evaluated through simulation studies and applied to the analyses of resting state fMRI data of the rat brain.