Modeling face recognition in the predictive coding framework: A combined computational modeling and functional imaging study.

The learning of new facial identities and the recognition of familiar faces are crucial processes for social interactions. Recently, a combined computational modeling and functional magnetic resonance imaging (fMRI) study used predictive coding as a biologically plausible framework to model face identity learning and to relate specific model parameters with brain activity (Apps and Tsakiris, Nat Commun 4, 2698, 2013). On the one hand, it was shown that behavioral responses on a two-option face recognition task could be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles.

Let's face it: The lateralization of the face perception network as measured with fMRI is not clearly right dominant.

The neural face perception network is distributed across both hemispheres. However, the dominant role in humans is virtually unanimously attributed to the right hemisphere. Interestingly, there are, to our knowledge, no imaging studies that systematically describe the distribution of hemispheric lateralization in the core system of face perception across subjects in large cohorts so far.

Developmental changes within the extended face processing network: A cross-sectional functional magnetic resonance imaging study.

In the field of face processing, the so-called "core network" has been intensively researched. Its neural activity can be reliably detected in children and adults using functional magnetic resonance imaging (fMRI). However, the core network's counterpart, the so-called "extended network," has been less researched.

"I Spy with my Little Eye, Something that is a Face…": A Brain Network for Illusory Face Detection.

The most basic aspect of face perception is simply detecting the presence of a face, which requires the extraction of features that it has in common with other faces. Putatively, it is caused by matching high-dimensional sensory input with internal face templates, achieved through a top-down mediated coupling between prefrontal regions and brain areas in the occipito-temporal cortex ("core system of face perception"). Illusory face detection tasks can be used to study these top-down influences.

The Trajectory of Hemispheric Lateralization in the Core System of Face Processing: A Cross-Sectional Functional Magnetic Resonance Imaging Pilot Study.

Face processing is mediated by a distributed neural network commonly divided into a "core system" and an "extended system." The core system consists of several, typically right-lateralized brain regions in the occipito-temporal cortex, including the occipital face area (OFA), the fusiform face area (FFA) and the posterior superior temporal sulcus (pSTS). It was recently proposed that the face processing network is initially bilateral and becomes right-specialized in the course of the development of reading abilities due to the competition between language-related regions in the left occipito-temporal cortex (e.

Neoplastic Cells are the Major Source of MT-MMPs in -Mutant Glioma, Thus Enhancing Tumor-Cell Intrinsic Brain Infiltration.

Tumor-cell infiltration is a major obstacle to successful therapy for brain tumors. Membrane-type matrix metalloproteinases (MT-MMPs), a metzincin subfamily of six proteases, are important mediators of infiltration. The cellular source of MT-MMPs and their role in glioma biology, however, remain controversial.

Illusory face detection in pure noise images: The role of interindividual variability in fMRI activation patterns.

Illusory face detection tasks can be used to study the neural correlates of top-down influences on face perception. In a typical functional magnetic resonance imaging (fMRI) study design, subjects are presented with pure noise images, but are told that half of the stimuli contain a face. The illusory face perception network is assessed by comparing blood oxygenation level dependent (BOLD) responses to images in which a face has been detected against BOLD activity related to images in which no face has been detected.