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Magnetic resonance imaging studies of the resting brain have recently revealed the existence of low-frequency fluctuations of the cerebral hemodynamics. It has been suggested that these fluctuations are linked to baseline neural activity, organized in functional networks. This paper presents a novel method for the detection of these resting-state networks from blood-oxygen level dependent signals, through their wavelet representation in the appropriate frequency range. A valley-seeking clustering principle is employed, requiring no a priori knowledge of the number of functional networks. The technique is applied to a data set acquired at rest and is shown to retrieve a number of identifiable functional networks. The method is proposed as an alternative to e.g. independent component analysis and exhibits an enhanced network separation capability and stability against noise.
Date of Conference: 11-14 Sept. 2011