Normalized Cut Group Clustering of Resting-State fMRI Data

• Published in: PloS one Vol. 3; no. 4; p. e2001
• Main Authors: van den Heuvel, Martijn, Mandl, Rene, Hulshoff Pol, Hilleke
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.04.2008; Public Library of Science (PLoS)
• Subjects: Adult; Attention; Brain; Brain mapping; Brain research; Cluster Analysis; Clustering; Data processing; Female; Functional magnetic resonance imaging; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Methods; Networks; Neural networks; Neuroimaging; Neurons; Neuroscience; Neuroscience/Cognitive Neuroscience; Neurosciences; NMR; Nuclear magnetic resonance; Principal components analysis; Psychiatry; Radiology and Medical Imaging/Magnetic Resonance Imaging; Rest; Rest - physiology; Schizophrenia; Sensorimotor integration; Sensory integration; Visual perception
• DOI: 10.1371/journal.pone.0002001
• ISSN: 1932-6203

Functional brain imaging studies have indicated that distinct anatomical brain regions can show coherent spontaneous neuronal activity during rest. Regions that show such correlated behavior are said to form resting-state networks (RSNs). RSNs have been investigated using seed-dependent functional connectivity maps and by using a number of model-free methods. However, examining RSNs across a group of subjects is still a complex task and often involves human input in selecting meaningful networks. We report on a voxel based model-free normalized cut graph clustering approach with whole brain coverage for group analysis of resting-state data, in which the number of RSNs is computed as an optimal clustering fit of the data. Inter-voxel correlations of time-series are grouped at the individual level and the consistency of the resulting networks across subjects is clustered at the group level, defining the group RSNs. We scanned a group of 26 subjects at rest with a fast BOLD sensitive fMRI scanning protocol on a 3 Tesla MR scanner. An optimal group clustering fit revealed 7 RSNs. The 7 RSNs included motor/visual, auditory and attention networks and the frequently reported default mode network. The found RSNs showed large overlap with recently reported resting-state results and support the idea of the formation of spatially distinct RSNs during rest in the human brain.