Human parietofrontal networks related to action observation detected at rest

Elisa Molinari, Patrizia Baraldi, Martina Campanella, Davide Duzzi, Luca Nocetti, Giuseppe Pagnoni, Carlo A. Porro

Research output: Contribution to journalArticlepeer-review


Recent data show a broad correspondence between human resting-state and task-related brain networks. We performed a functional magnetic resonance imaging (fMRI) study to compare, in the same subjects, the spatial independent component analysis (ICA) maps obtained at rest and during the observation of either reaching/grasping hand actions or matching static pictures. Two parietofrontal networks were identified by ICA from action observation task data. One network, specific to reaching/grasping observation, included portions of the anterior intraparietal cortex and of the dorsal and ventral lateral premotor cortices. A second network included more posterior portions of the parietal lobe, the dorsomedial frontal cortex, and more anterior and ventral parts, respectively, of the dorsal and ventral premotor cortices, extending toward Broca's area; this network was more generally related to the observation of hand action and static pictures. A good spatial correspondence was found between the 2 observation-related ICA maps and 2 ICA maps identified from resting-state data. The anatomical connectivity among the identified clusters was tested in the same volunteers, using persistent angular structure-MRI and deterministic tractography. These findings extend available knowledge of human parietofrontal circuits and further support the hypothesis of a persistent coherence within functionally relevant networks during rest.

Original languageEnglish
Pages (from-to)178-186
Number of pages9
JournalCerebral Cortex
Issue number1
Publication statusPublished - Jan 2013


  • anatomical connectivity
  • functional connectivity
  • functional magnetic resonance imaging
  • independent component analysis
  • tractography

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience


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