MULTIFRACTAL ANALYSIS OF RESTING STATE NETWORKS IN FUNCTIONAL MRI

Abstract : 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 properties 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.
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Philippe Ciuciu, Gaël Varoquaux, Patrice Abry, Moty Almog. MULTIFRACTAL ANALYSIS OF RESTING STATE NETWORKS IN FUNCTIONAL MRI. IEEE International Symposium on Biomedical Imaging, Mar 2011, Chicago, United States. paper ID 1544. ⟨cea-00558870⟩

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