Neurofunctional Signature of Hyperfamiliarity for Unknown Faces

PLoS One. 2015 Jul 8;10(7):e0129970. doi: 10.1371/journal.pone.0129970. eCollection 2015.

Abstract

Hyperfamiliarity for unknown faces is a rare selective disorder that consists of the disturbing and abnormal feeling of familiarity for unknown faces, while recognition of known faces is normal. In one such patient we investigated with a multimodal neuroimaging design the hitherto undescribed neural signature associated with hyperfamiliarity feelings. Behaviorally, signal detection methods revealed that the patient's discrimination sensitivity between familiar and unfamiliar faces was significantly lower than that of matched controls, and her response criterion for familiarity decisions was significantly more liberal. At the neural level, while morphometric analysis and single-photon emission CT (SPECT) showed the atrophy and hypofunctioning of the left temporal regions, functional magnetic resonance imaging (fMRI) revealed that hyperfamiliarity feelings were selectively associated to enhanced activity in the right medial and inferior temporal cortices. We therefore characterize the neurofunctional signature of hyperfamiliarity for unknown faces as related to the loss of coordinated activity between the complementary face processing functions of the left and right temporal lobes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Behavior
  • Brain / pathology
  • Brain / physiopathology*
  • Brain Mapping
  • Face / physiopathology*
  • Female
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Neuropsychological Tests
  • Photic Stimulation
  • Recognition, Psychology*
  • Signal Processing, Computer-Assisted
  • Tomography, Emission-Computed, Single-Photon

Grants and funding

MT is supported by a Vidi grant from the Netherlands Organization for Scientific Research (NWO) (grant 452-11-015) and by a FIRB—Futuro in Ricerca 2012—grant from the Italian Ministry of Education University and Research (MIUR) (grant RBFR12F0BD). BdG is supported by an Advanced ERC grant from the EU-FP7.