How to break the cortical face perception network

Time/Room: Friday, May 15, 2015, 2:30 – 4:30 pm, Pavilion
Organizer(s): David Pitcher; NIMH
Presenters: Marlene Behrmann, Arash Afraz, Kevin Weiner, David Pitcher

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Symposium Description

Faces are a rich source of social information that simultaneously convey an individual’s identity, attentional focus, and emotional state. Primate visual systems are so efficient that processing this wealth of information seems to happen effortlessly. Yet the simplest functions, like recognizing your mother or judging her mood, require the interaction of multiple specialized brain regions distributed across cortex. Despite many years of study our understanding of the unique functions performed by each region and how these regions interact to facilitate face perception remains limited. The speakers in this symposium use novel combinations of experimental techniques to study the behavioral effects of damage and disruption in the cortical face perception network in both human and non-human primates. Our aims are to update the fundamental understanding of how faces are cortically represented and to establish common theoretical ground amongst researchers who use different experimental techniques. To achieve this we will present studies using a range of subject populations (healthy-humans, brain-damaged patients, pre-operative epileptic patients and macaques) and experimental methods (optogenetics, fMRI, microstimulation, physiology, TMS, diffusion weighted imaging and neuropsychology). We believe this symposium will be of great interest to VSS attendees for two reasons. Firstly, understanding the neural processes underlying face perception has proven to be a testing ground in which key disputes concerning anatomical specificity and computational modularity take place and which therefore generates great interest amongst all cognitive neuroscientists. Secondly, studying the face network serves as an excellent proxy for studying the whole brain as a network and we believe attendees will be eager to apply the experimental techniques discussed to address their own questions. The symposium will conclude with an open discussion between the speakers and the audience to establish common ground between those who use different experimental methods and who hold different theoretical positions.

Presentations

Reverse engineering the face perception system: insights from congenital prosopagnosia

Speaker: Marlene Behrmann; Department of Psychology, Carnegie Mellon University, USA

Reverse engineering involves disassembling a complex device and analyzing its components and workings in detail with the goal of understanding how the device works in its intact state. To elucidate the neural components implicated in normal face perception, we investigate the disrupted components in individuals with congenital prosopagnosia, an apparently lifelong impairment in face processing, despite normal vision and other cognitive skills. Structural and functional MRI data reveal compromised connectivity between more posterior face-selective cortical patches and more anterior regions that respond to face stimuli. Computational descriptions of the topology of this connectivity, using measures from graph theory that permit the construction of the network at the level of the whole brain, uncover atypical organization of the face network in CP. Moreover, this network disorganization is increasingly pronounced as a function of severity of the face recognition disorder. Last, we reconstruct the face images viewed by normal and prosopagnosic observers from the neural data and demonstrate the altered underlying representations in key cortical regions in the prosopagnosic individuals. This multipronged approach uncovers in fine-grained detail the alteration in information discrimination in the prosopagnosic individuals as well as the pertubations in the neural network that gives rise to normal face perception.

The causal role of face-selective neurons in face perception.

Speaker: Arash Afraz; Massachusetts Institute of Technology

Many neurons in the inferior temporal cortex (IT) of primates respond more strongly to images of faces than to images of non-face objects. Such so-called ‘face neurons’ are thought to be involved in face recognition behaviors such as face detection and face discrimination. While this view implies a causal role for face neurons in such behaviors, the main body of neurophysiological evidence to support it is only correlational. Here, I bring together evidence from electrical microstimulation, optogenetic and pharmacological intervention to bridge the gap between the neural spiking of IT face selective neurons and face perception.

The human face processing network is resilient after resection of specialized cortical inputs

Speaker: Kevin Weiner; Department of Psychology, Stanford University

Functional hierarchies are a prevalent feature of brain organization. In high-level visual cortex, the ‘occipital face area’ (OFA/IOG-faces) is thought to be the input to a specialized processing hierarchy subserving human face perception. However, evidence supporting or refuting the causal role of IOG-faces as a necessary input to the face network evades researchers because it necessitates a patient with a focal lesion of the right inferior occipital cortex, as well as functional measurements both before and after surgical removal of this region. Here, in a rare patient fulfilling both of these requirements, we show that the face network is surprisingly resilient in two ways following surgical removal of IOG-faces. First, the large-scale cortical layout and selectivity of the face network are stable after removal of IOG-faces. Second, following resection, face-selective responses in ventral temporal cortex surprisingly become more reliable in the resected hemisphere, but not in the intact hemisphere. Further investigations of the anatomical underpinnings of this resiliency using diffusion tensor imaging suggest the existence of additional white matter pathways connecting early visual cortex to downstream face-selective regions independent of IOG-faces. Thus, after resection, neural signals can still reach downstream regions via these pathways that are largely unconsidered by present neurofunctional models of face processing. Altogether, these measurements indicate that IOG-faces is not the key input to the face network. Furthermore, our results pose important constraints on hierarchical models in high-level sensory cortices and provide powerful insight into the resiliency of such networks after damage or cortical trauma.

Transient disruption in the face perception network: combining TMS and fMRI

Speaker: David Pitcher; NIMH

Faces contain structural information, for identifying individuals, as well as changeable information, that can convey emotion and direct attention. Neuroimaging studies reveal brain regions that exhibit preferential responses to invariant or changeable facial aspects but the functional connections between these regions are unknown. This issue was addressed by causally disrupting two face-selective regions with thetaburst transcranial magnetic stimulation (TBS) and measuring the effects of this disruption in local and remote face-selective regions with functional magnetic resonance imaging (fMRI). Participants were scanned, over two sessions, while viewing dynamic or static faces and objects. During these sessions, TBS was delivered over the right occipital face area (rOFA) or right posterior superior temporal sulcus (rpSTS). Disruption of the rOFA reduced the neural response to both static and dynamic faces in the downstream face-selective region in the fusiform gyrus. In contrast, the response to dynamic and static faces was doubly dissociated in the rpSTS. Namely, disruption of the rOFA reduced the response to static but not dynamic faces, while disruption of the rpSTS itself, reduced the response to dynamic but not static faces. These results suggest that dynamic and static facial aspects are processed via dissociable cortical pathways that begin in early visual cortex, a conclusion inconsistent with current models of face perception.

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