Multifractal analysis of Resting State Networks in functional MRI

Philippe Ciuciu, Gael Varoquaux, Patrice Abry, Moty Almog
2011 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
It has been know for at least one decade [1] that functional MRI time series display long-memory properties, such as power-law scaling in the frequency spectrum. Concomitantly, multivariate modelfree analysis of spatial patterns , such as spatial Independent Component Analysis (sICA) [2], has been successfully used to segment from spontaneous activity Resting-State Networks (RSN) that correspond to known brain function. As recent neuroscientific studies suggest a link between spectral
more » ... of brain activity and cognitive processes [3], a burning question emerges: can temporal scaling properties offer new markers of brain states encoded in these large scale networks? In this paper, we combine two recent methodologies: group-level canonical ICA for multi-subject segmentation of brain network, and wavelet leader-based multifractal formalism for the analysis of RSN scaling properties. We identify the brain networks that elicit self-similarity or multifractality and explore which spectral properties correspond specifically to known functionallyrelevant processes in spontaneous activity.
doi:10.1109/isbi.2011.5872448 dblp:conf/isbi/CiuciuVAA11 fatcat:ojhyjzsqejau7bxyxrgtj43pla