Resting-state functional connectivity within the face processing network of normal and congenitally prosopagnosic individuals Academic Article uri icon


  • A recent development in human neuroscience is the discovery of resting state networks (RSN) whose coordinated activity can be uncovered using the spontaneous, relatively slow fluctuations (<0.1 Hz) of brain activity during rest, i.e, while participants are not engaged in a predefined cognitive task and when no stimulation is present. Here, using fMRI, we explore whether the well-documented distributed face processing network can be documented under these resting state conditions. Specifically, we characterize the synchronization pattern between key regions, which are part of the core and extended nodes of the face network, as evident by measures of functional connectivity. These regions, including the fusiform face area (FFA), occipital face area (OFA), superior temporal sulcus (STS), amygdala, anterior temporal lobe and other anterior regions, were first localized using a face localizer paradigm and were subsequently used as seed region/s for connectivity analyses of the resting state data. We conducted a whole brain analysis in order to identify any region whose time course correlated with those of the pre-selected seed/s. Using this approach, we have uncovered a set of cortical areas whose activity is significantly correlated during rest, reflecting the presence of a face-selective RSN. Importantly, we also compare and contrast this RSN with that obtained from individuals with congenital prosopagnosia, a deficit in face recognition that is apparently lifelong and occurs despite normal intelligence, sensory abilities and adequate opportunity to acquire normal face recognition. Together, these findings illustrate the inherent synchronization of a normal, distributed face network that involves regions of ventral cortex as well as more anterior regions, and they provide further support for the notion that this system is compromised in individuals with disordered face recognition.

publication date

  • January 1, 2010