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Fusiform Gyrus Face Selectivity Relates to Individual Differences in Facial Recognition Ability

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Fusiform Gyrus Face Selectivity Relates to Individual Differences in Facial Recognition Ability Fusiform Gyrus Face Selectivity Relates to Individual Differences in Facial Recognition Ability Nicholas Furl, Lúcia Garrido, Raymond J. Dolan, Jon Driver, and Bradley Duchaine Abstract ■ Regions of the occipital and temporal lobes, including a re- robust relationships between face selectivity and face identifi- gion in the fusiform gyrus (FG), have been proposed to con- cation ability in FG across our sample for several convergent stitute a “core” visual representation system for faces, in part measures, including voxel-wise statistical parametric mapping, because they show face selectivity and face repetition suppres- peak face selectivity in individually defined “fusiform face areas” sion. But recent fMRI studies of developmental prosopagnosics (FFAs), and anatomical extents (cluster sizes) of those FFAs. (DPs) raise questions about whether these measures relate to None of these measures showed associations with behavioral face processing skills. Although DPs manifest deficient face expression or object recognition ability. As a group, DPs had processing, most studies to date have not shown unequivocal reduced face-selective responses in bilateral FFA when com- reductions of functional responses in the proposed core re- pared with non-DPs. Individual DPs were also more likely than gions. We scanned 15 DPs and 15 non-DP control participants non-DPs to lack expected face-selective activity in core regions. with fMRI while employing factor analysis to derive behavioral These findings associate individual differences in face process- components related to face identification or other processes. ing ability with selectivity in core face processing regions. Repetition suppression specific to facial identities in FG or to This confirms that face selectivity can provide a valid marker expression in FG and STS did not show compelling relation- for neural mechanisms that contribute to face identification ships with face identification ability. However, we identified ability. ■ INTRODUCTION more strongly to faces than to nonface objects (cf. Yovel & Developmental prosopagnosia (DP) is a condition in which Kanwisher, 2005). Neural responses in core regions can individuals (DPs) with otherwise normal vision, intelli- also attenuate in response to repeated facial information gence, and social functioning show face recognition im- (i.e., repetition suppression). In normal observers, iden- pairments in the absence of evident brain injury. Although tity repetition typically suppresses regions in the fusiform numerous behavioral studies have enhanced understanding gyrus (FG) while repeating more changeable attributes, of the cognitive bases of DP (Bentin, Degutis, DʼEsposito, & such as expression, typically leads to BOLD suppression Robertson, 2007; Humphreys, Avidan, & Behrmann, 2007; in other regions such as STS (e.g., see Fox, Moon, Iaria, Duchaine & Nakayama, 2006b; Behrmann, Avidan, Marotta, &Barton,2009;Rotshtein,Henson,Treves,Driver,& & Kimchi, 2005), progress on the neural basis of DP is more Dolan, 2005; Andrews & Ewbank, 2004; Winston, Henson, limited to date, with the few existing studies of this reviewed Fine-Goulden, & Dolan, 2004). below. Although these core face-related brain regions suggest An influential account of the functional neuroanatomy possible loci for neural deficits in DPs, there is scant evi- of face perception (Haxby, Hoffman, & Gobbini, 2000) dence to date showing functional deficits in these re- describes a so-called core system in the occipital and tem- gions. DP case studies using fMRI have mainly reported poral lobes. These core regions are considered responsible relatively normal face-selective responses in FG (e.g., see for visual recognition of facial attributes (Haxby et al., Minnebusch, Suchan, Köster, & Daum, 2009; Thomas 2000) and are distinguished from a more “extended sys- et al., 2009; Behrmann, Avidan, Gao, & Black, 2007; Williams, tem,” which further processes information resulting from Berberovic, & Mattingley, 2007; Avidan, Hasson, Malach, visual recognition. The proposed “core” regions have & Behrmann, 2005; Hasson, Avidan, Deouell, Bentin, & been associated with face selectivity: they typically respond Malach, 2003). Despite these numerous “null” (i.e., ap- parently normal) findings, some atypical findings in in- dividual cases have been identified, including some DPs with no face selectivity (Minnebusch et al., 2009; Bentin University College London et al., 2007; Hadjikhani & de Gelder, 2002), weakened ©2011MassachusettsInstituteofTechnology JournalofCognitiveNeuroscience23:7,pp.1723–1740 face selectivity (Avidan & Behrmann, 2009), or expression- METHODS dependent FG face selectivity (van de Stock, van de Riet, Participants Righart, & de Gelder, 2009). One study (using an unusually large sample of DPs) found that voice recognition deficits Our sample initially included 20 right-handed DPs who in DPs were associated with reduced face selectivity in contacted the Web site www.faceblind.org and reported core regions (von Kriegstein et al., 2008). Studies examining severe face recognition difficulties in everyday life. See repetition suppression have reported apparently normal Garrido et al. (2009) for more information about this partici- suppression to repeated (familiar) faces in DPs (Avidan & pant sample. We confirmed that these participants merited Behrmann, 2009; Williams et al., 2007; Avidan et al., 2005). classification as DPs by using the Cambridge Face Memory Thus, functional imaging of DP has provided inconsis- Test (CFMT in its original form; Duchaine & Nakayama, tent or incomplete evidence for substantially altered neural 2006a) and the Famous Faces Test (FFT; Duchaine & function in the “core” regions that have been associated Nakayama, 2005). These tests have been validated on DPs with face processing in studies of people with typical face in previous studies that also provided the mean CFMT value skills. But such evidence might still be reconciled with (mean ± SD =57.90±7.91)from50non-DPcontrols a role for these core regions in DP if, for example, face- (Duchaine & Nakayama, 2006a) and the mean FFT value related effects were reduced or variable in DPs rather than (0.89 ± 0.09) from 22 non-DP controls (Garrido, Duchaine, being completely abolished. It remains possible that the &Nakayama,2008).WeobservedthatalloftheDPsinthe magnitude of functional abnormalities might relate sys- present study scored more than two standard deviations tematically to the extent of reduced face skills. Indeed below those non-DP means (on both tests). All non-DP one open possibility, which has not been rigorously inves- participants (see below) in the present study scored better tigated to date, is that DPs might constitute the lower tail than this criterion for the CFMT and all but two for the FFT. of a fairly continuous distribution of face identification All the DPs showed impaired performance compared with ability. If so, one would not predict discrete, all-or-nothing the non-DP averages using a modified t test (Crawford & functional deficits that should be easily observed in every Howell, 1998) for single cases. Three DPs were excluded individual DP. Instead, one would predict functional neuro- from the study because of neurological or visual abnormality anatomic correlates of performance might be best de- (epilepsy, brain injury, strabismus). The remaining 17 DPs tected by testing for continuous relationships between (11 women) were compared with a sample of 18 non-DP fMRI measures and a wide range of behavioral face iden- participants (11 women), who were matched for age and tification ability across both DPs and individuals exhibiting IQ. Scores on the CFMT and FFT tests in the present study normal variation in face skills. are shown in Figure 1 for the DPs and the non-DP group. Here, we aimed to implement such an “individual differ- The ages of DP and non-DP groups did not significantly dif- ences” approach by testing not only a sample large enough fer (mean ± SD:DPs=30.9±7.5,non-DP=28.9±5.7), to allow conventional group contrasts (DPs vs. non-DPs) t(33) = 0.89, p =.38,nordidIQ(DPs=123.9±7.8, but also a more systematic analysis of neuroimaging data non-DP = 118.9 ± 8.8), t(33) = 1.7, p = .1 (note that IQ in relation to the subject-by-subject scores on a battery of tests. We recently applied this approach successfully in a purely structural MRI analysis of 17 DPs and 18 non-DP comparison participants (Garrido et al., 2009). We tested these participants with a battery of behavioral tasks assess- ing judgments of facial identity, facial expression, and non- face object recognition. Using factor analysis, we showed that we could derive summary scores for orthogonal com- ponents of behavioral performance from this battery. Em- pirically, this yielded separate face identity, face expression, and nonface object behavioral components. In our MRI morphometric analysis, these factor scores showed linear relationships with gray matter volume in temporal cortex across the entire sample (Garrido et al., 2009). Using the same factor analytic approach to individual dif- ferences in behavioral performance, we now extend our analysis of this sample by exploring the relationship of face identification performance deficits to the aforementioned fMRI measures of functional face selectivity and repetition Figure 1. Face identification ability in non-DPs and DPs. Performance suppression. We hypothesized that the use of these mea- of all study participants on the original Cambridge Face Memory Test (CFMT) is
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