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  • Title: Normal and abnormal face selectivity of the M170 response in developmental prosopagnosics.
    Author: Harris AM, Duchaine BC, Nakayama K.
    Journal: Neuropsychologia; 2005; 43(14):2125-36. PubMed ID: 16243056.
    Abstract:
    Developmental prosopagnosia is a lifelong impairment in face recognition despite normal low-level visual processing. Here we used magnetoencephalography (MEG) to examine the M170 response, a component occurring approximately 170 ms after stimulus onset, in a group of five developmental prosopagnosics. In normal subjects, the M170 is "face-selective", with a consistently higher amplitude to faces than to a wide variety of other visual stimulus categories; the N170, a component recorded using event-related potentials (ERP) and thought to be analogous to the M170, also shows this "face selectivity". Two previous ERP studies with developmental prosopagnosics have found attenuation or absence of face selectivity in the N170 response of these subjects [Bentin, S., Deouell, L. Y., and Soroker, N. (1999). Selective visual streaming in face recognition: Evidence from developmental prosopagnosia. Neuroreport, 10, 823-827; Kress, T., and Daum, I. (2003). Event-related potentials reflect impaired face recognition in patients with congenital prosopagnosia. Neuroscience Letters, 352, 133-136]. Three of our developmental prosopagnosic group showed this non-selective pattern at the M170 while the remaining two prosopagnosics were indistinguishable from normal controls. Thus, impaired face recognition is not necessarily correlated with an absence of the "face-selective" M170. Furthermore, ERP recordings collected simultaneously in the two developmental prosopagnosics with seemingly selective M170s also showed N170s within the same normal selective range, demonstrating that the face-selective signals found with MEG are not due to differences between MEG and ERP. While the presence of face selectivity at these neurophysiological markers is insufficient for predicting normal behavioral performance with faces, it could help to distinguish different classes of face recognition deficits.
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