Discriminating individual faces from neural activation December 29, 2007Posted by Johan in Face Perception, Neuroscience.
How do we recognise faces? The vast majority of research into face perception has attempted to answer this question by restricting their investigations to a small section of the fusiform gyrus, which Kanwisher and colleagues named the Fusiform Face Area (FFA) in 1997. It is commonly proposed that the FFA handles not only the detection but also the recognition of individual faces. A recent paper by Kriegeskorte et al (2007) suggests that instead, a region in the right anterior inferotemporal cortex (aIT – ahead of and above the FFA) encodes information about different faces, while the FFA does not. In order to understand the finer points of this finding, it is necessary to explain the basic assumptions of univariate neuroimaging analysis, and how it is used to identify the FFA. Skip ahead a paragraph if this is familiar territory.
The classic fMRI or PET analysis consists of taking an experimental condition and a control condition, and asking “which areas respond significantly more to the experimental condition than the control?” The resulting activations can be said to constitute areas that are specifically implicated in the experimental condition. For example, the FFA is usually defined as the part of the fusiform gyrus that responds more to faces than to houses. Note that there is an element of inference or assumption involved in then concluding that this bit of brain is the bit that does faces, since other areas might also respond to faces without being detected in a relatively insensitive univariate whole-brain analysis. The common acceptance of this type of contrast analysis stems in part from its practical utility. For example, the FFA corresponds closely to the critical lesion site that causes prosopagnosia (an inability to recognise faces), and activation in this area can be correlated with behavioural performance at various face recognition tasks.
In this study, contrasts were used to identify the FFA in each participant, in addition to a region in the aIT that also responded more to faces than to objects. To do this, Kriegeskorte et al (2007) used only four stimuli, as shown below.
Although contrasting faces and houses revealed the previously mentioned activations in the FFA and aIT, contrasting the two different faces produced no activations.
Kriegeskorte et al (2007) next used a type of pattern analysis, where the FFA and aIT voxels were used as input. The specifics of this type of analysis are too complex to discuss in detail here (see this review by Norman et al, 2006 which concerns a related technique, and a previous post), but essentially, this analysis uses multivariate statistics to assess whether the overall pattern of activation in an area differs significantly between conditions. If it does, it can be inferred that the area processes information about the categories. Pattern analyses are far more sensitive than traditional contrasts when it comes to differences within a region, but they achieve this sensitivity by sacrificing spatial localisation. Kriegeskorte et al (2007) used a range of pattern analyses, but their results are nicely summarised by the analysis depicted in this figure.
In this analysis, Kriegeskorte et al (2007) attempted to discriminate between the two faces based on an increasing number of voxels, expanding from the FFA and aIT regions that were revealed by the contrast. The lines on the y-axis show whether the patterns evoked by the two faces are significantly different in the voxels. Only the voxels in the right aIT respond significantly differently to the two faces, and this difference becomes significant early, when around 200 voxels are included. By contrast, even when 4000 voxels around the FFA are included, encompassing much of the temporal and occipital lobes, the activation here cannot discriminate between the two faces.
So to summarise, both the FFA and the aIT (among other areas) respond more to faces than to houses, but only the aITS responds differentially to specific faces. Although these results lend themselves to the conclusion that the FFA does some type of face detection while the aITS is involved in encoding the identity of faces, Kriegeskorte et al (2007) suggest that it probably isn’t that simple. Previous studies have found identity-specific activations in the FFA using other paradigms (e.g., Rotshtein et al, 2005), so Kriegeskorte et al (2007) go for the classic neuroimaging cop-out of suggesting that identity information nevertheless exists in the FFA, but at a resolution beyond that of current scanners. However, note that the fact that the identity effects in the aIT were detectable suggests that this area might play a larger role in this task than the FFA does, at least. Kriegeskorte et al (2007) note that prosopagnosia may be caused by lesions to the FFA region, but also by aIT lesions, and suggest that face recognition depends on interactions between (among others) these two areas.
From a more methodological standpoint, it is interesting to note that although a contrast between the two faces yielded no significant effects, differences appeared in a pattern analysis. This is a nice example of how pattern analysis may be a more sensitive measure.
The aIT has not received a great deal of attention previously as a face recognition region, so Kriegeskorte et al (2007) are probably going to face close scrutiny, as they have essentially posited that the region plays a leading role in the holy grail of face perception – the recognition of individual faces. It is interesting to note, however, that these findings do offer a means of reconciling fMRI results from humans with data from single-cell recording studies in monkeys, which have revealed identity-specific face responses primarily in anterior temporal regions. Such monkey regions correspond far better to the aIT than the FFA, which has been somewhat of a problem for the conventional account of the FFA as a Swiss army knife of face perception (but see Tsao et al, 2006 for evidence of a better monkey homologue of the FFA).
Really though, the most striking thing about this study is that current neuroimaging technique enables us to discriminate between the neural representation of these two faces. When you look at the faces above, it is clear that physically, they are quite similar. It is quite inspiring to think that it is nevertheless possible to pick out these undoubtedly subtle differences in the evoked neural response pattern.
Kriegeskorte, N., Formisano, E., Sorger, B., and Goebel, R. (2007). Individual faces elicit distinct response patterns in human anterior temporal cortex. Proceedings of the National Academy of Sciences (USA), 104, 20600-20605.