This paper reviews a probabilistic independent component analysis (PICA) approach optimized for the analysis of functional magnetic resonance imaging (fMRI) data. The authors discuss the role of this exploratory technique in understanding the structure of resting-state connectivity patterns. They apply PICA to fMRI data acquired at rest to characterize the spatio-temporal structure of these data, demonstrating that it is an effective and robust tool for identifying low-frequency resting-state patterns. The networks identified by PICA exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex. The paper also explores the ability of PICA to extract resting fluctuations and apply it to fMRI resting data to test hypotheses about the structure of resting-state connectivity. Key findings include the identification of overlapping spatial maps, the distinction between neural and physiological effects, and the spatial characteristics of resting-state fluctuations within grey matter. The consistency of resting-state patterns across subjects is also investigated, showing that these patterns are localized within discrete areas of functional significance.This paper reviews a probabilistic independent component analysis (PICA) approach optimized for the analysis of functional magnetic resonance imaging (fMRI) data. The authors discuss the role of this exploratory technique in understanding the structure of resting-state connectivity patterns. They apply PICA to fMRI data acquired at rest to characterize the spatio-temporal structure of these data, demonstrating that it is an effective and robust tool for identifying low-frequency resting-state patterns. The networks identified by PICA exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex. The paper also explores the ability of PICA to extract resting fluctuations and apply it to fMRI resting data to test hypotheses about the structure of resting-state connectivity. Key findings include the identification of overlapping spatial maps, the distinction between neural and physiological effects, and the spatial characteristics of resting-state fluctuations within grey matter. The consistency of resting-state patterns across subjects is also investigated, showing that these patterns are localized within discrete areas of functional significance.