Effective connectivities of cortical regions for top-down face processing: A Dynamic Causal Modeling study

Abstract

To study top-down face processing, the present study used an experimental paradigm in which participants detected non-existent faces in pure noise images. Conventional BOLD signal analysis identified three regions involved in this illusory face detection. These regions included the left orbitofrontal cortex (OFC) in addition to the right fusiform face area (FFA) and right occipital face area (OFA), both of which were previously known to be involved in both top-down and bottom-up processing of faces. We used Dynamic Causal Modeling (DCM) and Bayesian model selection to further analyze the data, revealing both intrinsic and modulatory effective connectivities among these three cortical regions. Specifically, our results support the claim that the orbitofrontal cortex plays a crucial role in the top-down processing of faces by regulating the activities of the occipital face area, and the occipital face area in turn detects the illusory face features in the visual stimuli and then provides this information to the fusiform face area for further analysis.

Introduction

Humans are exceptionally skilled at face perception. Over a wide range of viewing conditions we effortlessly detect and recognize faces accurately and quickly. Traditionally, face processing is viewed as a feed-forward, bottom-up process in which facial identity is processed first in the ventral visual stream and then passed along to more anterior regions of the brain such as the frontal cortex. This face processing model has been supported by many existing studies. However, in these experiments the stimuli are high quality images of actual faces, maximizing bottom-up information. This may swamp any top-down influences on face perception, thus biasing interpretation of experimental results in favor of bottom-up driven processing models.

Providing evidence that supports the bidirectional interactive face processing model proposed by Haxby et al. (2000), recent studies using novel stimuli and paradigms have questioned the traditional view about the neural systems of face processing. These studies focused on top-down face processing by requiring participants to imagine faces (Ishai et al., 2000, Mechelli et al., 2004), or to interpret ambiguous face stimuli such as Mooney faces and vase/face illusion (Andrews & Schluppeck, 2004, Hasson et al., 2001), or to detect impoverished face stimuli (Gosselin & Schyns, 2001, Summerfield et al., 2006, Wild & Busey, 2004), or to even make illusory face detection of pure visual noise stimuli (Li et al., 2009, Liu et al., 2010, Zhang et al., 2008). It was found that top-down feed-backward mechanisms play an important role in face processing, which is perhaps engendered by the neural system's use of face-relevant knowledge and learned expectations that regulate the bottom-up processing of visual stimuli (Mechelli et al., 2004, Summerfield et al., 2006). Further, these studies revealed a distributed cortical network for top-down face processing (Li et al., 2009), which overlaps to a large extent with the face processing network reported in traditional bottom-up face processing studies (for reviews, see Haxby et al., 2000, Ishai et al., 2005, Ishai, 2008). Taken together, the findings from bottom-up and top-down paradigms suggest that the neural system for processing faces involves a network of neural regions distributed from occipital to frontal cortices that has both feed-forward and feed-backward connections (Fairhall & Ishai, 2007, Haxby et al., 2000, Ishai, 2008, Li et al., 2009, Mechelli et al., 2004, Summerfield et al., 2006).

Previous studies of top-down illusory face processing (Li et al., 2009, Zhang et al., 2008) used conventional analyses that cannot determine in which direction signals flow and whether connections between brain regions are modulated during the task. The term “effective connectivity” is used in referring to the connection strength between different brain regions and how these strengths vary with experimental manipulations. To understand the top-down face processing network in terms of effective connectivity, the analyses reported here used Dynamic Causal Modeling (DCM: Friston et al., 2003). This analysis not only determines the active neural connections between brain regions during the experiment, but it also determines the direction of the intrinsic and modulatory cortical pathways specifically involved in top-down face perception.

The present study focused on three cortical regions identified by traditional analyses: the fusiform face area (FFA), occipital face areas (OFA), and orbitofrontal cortex (OFC). It is now well established that the FFA and OFA play an important role in face processing and are part of the bottom-up and top-down face processing networks (Fairhall & Ishai, 2007, Liu et al., 2009, Mechelli et al., 2004, Summerfield et al., 2006, Zhang et al., 2009). Thus, these are “core regions” of face processing. However, the function of the OFC and its relation to the core face processing regions is less clear.

A number of functional neuroimaging studies have identified OFC activation both during face processing and during processing of non-face objects. It has been proposed that the OFC is involved in encoding novel information (Frey & Petrides, 2000, Frey et al., 2004) as well as in mediating the perception of attractive and sexually relevant faces (Ishai, 2007, Kranz & Ishai, 2006, O'Doherty et al., 2001). More importantly, recent studies of object recognition revealed that the OFC plays a key role in top-down object processing (Bar et al., 2006, Bar, 2009, Johnson, 2005, Kveraga et al., 2007a, Kveraga et al., 2007b). Specifically, it has been proposed that the OFC uses low spatial frequency visual information to form a coarse prediction of the most likely candidate object, which is used to prime the corresponding object processing areas in the ventral occipital-temporal cortex in a top-down manner. This hypothesis predicts that during top-down face detection, the OFC should have functional connections to the OFA or the FFA. The present study tested this prediction.

Based on our recent investigation on top-down face processing (Li et al., 2009, Zhang et al., 2008), we adopted a novel paradigm that promotes illusory face detection in response to images that only contain noise. Participants were told that half of the images in the experiment contained faces and the other half did not. Their task was to detect which images contained faces. An initial training stage of the experiment did indeed contain faces on 50% of the trials. During this training, the faces became more and more difficult to detect by mixing higher degrees of noise with the faces. Eventually, participants were only shown pure noise images, although they were instructed that there were still faces on 50% of the trials and that face detection would be very difficult. The noise images were a mixture of Gaussian blobs of different spatial frequencies placed randomly throughout the image. These complex noise images lend themselves to a large number of interpretations and participants readily continued to detect faces. Thus, we were able to study top-down influences using false detections of faces to images containing only noise, which avoids contamination from strong bottom-up face information. Furthermore, an independent localizer task was performed to validate the ventral occipito-temporal face-sensitive areas identified by the illusory face detection task. Our recent study using this method revealed a complex distributed cortical network for top-down face processing (Li et al., 2009). However, this finding was obtained by using simple correlational analyses (Psychophysiological Interaction, or PPI) with the right FFA as the seed region. Not only is this method unable to measure the directional effective connectivity between different brain regions involved in top-down face processing, but this method also has potential methodological problems such as “double dipping” in which the same data is used for more than one analysis (see Kriegeskorte et al., 2009).

To determine the directional top-down effective connectivities involved in the top-down face processing as well as to avoid the methodological problems associated with PPI, we used DCM (Friston et al., 2003) in combination with Bayesian model selection (Penny et al., 2004) to analyze the data obtained in Li et al. (2009). The use of DCM has several advantages in addition to avoiding double dipping (Stephan et al., 2010). First, this analytic method provides information not only about the intrinsic effective connectivities among various brain regions (i.e., connections strengths that are constant throughout the experiment), but it also provides information about enhanced connectivities due to a specific processing demand (i.e., illusory face detection). Rather than revealing simple correlational relationships, DCM extracts directional relationships, providing information about how different brain regions are functionally connected during object processing (e.g. in a feed-forward or feed-backward manner). This information is particularly important for the present study considering that we were interested in the interplay between the FFA, OFA and OFC in top-down face processing.