Functional Subdomains within Human FFA

Çukur, Tolga; Huth, Alexander G.; Nishimoto, Shinji; Gallant, Jack L.

doi:10.1523/jneurosci.1259-13.2013

Cited by 51 publications

(46 citation statements)

References 61 publications

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“…However, the ability of a region to support identity discrimination does not necessarily imply that it encodes visual face representations. Higher level semantic information (33) or even a variety of unrelated task/stimulus properties may account for pattern discrimination (34). The latter possibility is a source of concern, especially given certain limitations of the fMRI signal in decoding facial identity (35).…”

Section: Discussionmentioning

confidence: 99%

Feature-based face representations and image reconstruction from behavioral and neural data

Nestor

Plaut

Behrmann

2015

Proc. Natl. Acad. Sci. U.S.A.

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The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a method akin to reverse correlation to derive visual features from functional MRI patterns elicited by a large set of homogeneous face exemplars. Then, we combine these features to reconstruct novel face images from the corresponding neural patterns. This approach allows us to estimate collections of features associated with different cortical areas as well as to successfully match image reconstructions to corresponding face exemplars. Furthermore, we establish the robustness and the utility of this approach by reconstructing images from patterns of behavioral data. From a theoretical perspective, the current results provide key insights into the nature of high-level visual representations, and from a practical perspective, these findings make possible a broad range of image-reconstruction applications via a straightforward methodological approach.image reconstruction | face space | reverse correlation F ace recognition relies on visual representations sufficiently complex to distinguish even among highly similar individuals despite considerable variation due to expression, lighting, viewpoint, and so forth. A longstanding conceptual framework, termed "face space" (1-6), suggests that individual faces are represented in terms of their multidimensional deviation from an "average" face, but the precise nature of the dimensions or features that capture these deviations, and the degree to which they preserve visual detail, remain unclear. Thus, the featural basis of face space along with the neural system that instantiate it remain to be fully elucidated. The present investigation aims not only to uncover fundamental aspects of neural representations but also to establish their plausibility and utility through image reconstruction. Concretely, the current study addresses the issues above in the context of two distinct challenges, first, by determining the visual features used in face identification and, second, by validating these features through their use in facial image reconstruction.With respect to the first challenge, recent studies have demonstrated distinct sensitivity to local features (e.g., the size of the mouth) compared with configural features (e.g., the distance between the eyes and the mouth) in human face-selective cortex (7-10). Also, neurophysiological investigations (1, 11) of monkey cortex have found sensitivity to several facial features, particularly in the eye region of the face. However, most investigations consider only a few handpicked features. Thus, a comprehensive, unbiased assessment of face space stil...

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Section: Discussionmentioning

confidence: 99%

Feature-based face representations and image reconstruction from behavioral and neural data

Nestor

Plaut

Behrmann

2015

Proc. Natl. Acad. Sci. U.S.A.

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“…High-resolution fMRI experiments point to three distinct face-selective regions located on the midfusiform sulcus, the posterior fusiform gyrus, and the inferior occipital gyrus (Weiner and Grill-Spector 2010). Also face-specificity of the FFA has been questioned by recent experiments, suggesting selective enhancement or suppression of activity in three spatially segregated clusters of voxels by a wide variety of categories in addition to faces (Çukur et al 2013). In summary, rather than one single, face-specific FFA, functionally—and probably also anatomically (Caspers et al 2013)—distinct parts of the fusiform gyrus seem to be involved in face processing.…”

Section: Discussionmentioning

confidence: 99%

Neural networks related to dysfunctional face processing in autism spectrum disorder

et al. 2014

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One of the most consistent neuropsychological findings in autism spectrum disorders (ASD) is a reduced interest in and impaired processing of human faces. We conducted an activation likelihood estimation meta-analysis on 14 functional imaging studies on neural correlates of face processing enrolling a total of 164 ASD patients. Subsequently, normative whole-brain functional connectivity maps for the identified regions of significant convergence were computed for the task-independent (resting-state) and task-dependent (co-activations) state in healthy subjects. Quantitative functional decoding was performed by reference to the BrainMap database. Finally, we examined the overlap of the delineated network with the results of a previous meta-analysis on structural abnormalities in ASD as well as with brain regions involved in human action observation/imitation. We found a single cluster in the left fusiform gyrus showing significantly reduced activation during face processing in ASD across all studies. Both task-dependent and task-independent analyses indicated significant functional connectivity of this region with the temporo-occipital and lateral occipital cortex, the inferior frontal and parietal cortices, the thalamus and the amygdala. Quantitative reverse inference then indicated an association of these regions mainly with face processing, affective processing, and language-related tasks. Moreover, we found that the cortex in the region of right area V5 displaying structural changes in ASD patients showed consistent connectivity with the region showing aberrant responses in the context of face processing. Finally, this network was also implicated in the human action observation/imitation network. In summary, our findings thus suggest a functionally and structurally disturbed network of occipital regions related primarily to face (but potentially also language) processing, which interact with inferior frontal as well as limbic regions and may be the core of aberrant face processing and reduced interest in faces in ASD.

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“…It is important to note that FG activation in the context of face perception paradigms has generally been identified belonging to the ‘fusiform face area’ (FFA; Kanwisher et al, 1997). However, recent research has shown that there are up to three distinct clusters of face and object selective regions located within the traditionally defined FFA (Weiner & Grill-Spector, 2010; Cukur et al, 2013). Thus, we will use the more general term ‘F’ rather than the more specific ‘FFA’ terminology due to the ambiguity in precisely identifying the FFA within the brain.…”

Section: Fusiform Gyrus Superior Temporal Sulcus and The Amygdalmentioning

confidence: 99%

Face processing in autism spectrum disorders: From brain regions to brain networks

2015

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Autism spectrum disorder (ASD) is characterized by reduced attention to social stimuli including the human face. This hypo-responsiveness to stimuli that are engaging to typically developing individuals may result from dysfunctioning motivation, reward, and attention systems in the brain. Here we review an emerging neuroimaging literature that emphasizes a shift from focusing on hypo-activation of isolated brain regions such as the fusiform gyrus, amygdala, and superior temporal sulcus in ASD to a more holistic approach to understanding face perception as a process supported by distributed cortical and subcortical brain networks. We summarize evidence for atypical activation patterns within brain networks that may contribute to social deficits characteristic of the disorder. We conclude by pointing to gaps in the literature and future directions that will continue to shed light on aspects of face processing in autism that are still under-examined. In particular, we highlight the need for more developmental studies and studies examining ecologically valid and naturalistic social stimuli.

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Functional Subdomains within Human FFA

Cited by 51 publications

References 61 publications

Feature-based face representations and image reconstruction from behavioral and neural data

Feature-based face representations and image reconstruction from behavioral and neural data

Neural networks related to dysfunctional face processing in autism spectrum disorder

Face processing in autism spectrum disorders: From brain regions to brain networks

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