Multiple brain networks for visual self-recognition with different sensitivity for motion and body part.

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Brain Imaging

Background:

• Visual self-recognition is a complex cognitive function potentially supported by multiple, specialized brain networks.
• Previous hypotheses suggest distinct roles for the left ventral occipito-temporal cortex (self-face as symbol) and the right parieto-frontal network (self-image with motion-action).

Purpose of the Study:

• To test the hypotheses regarding the functional specialization of brain networks in visual self-recognition.
• To investigate the differential activation patterns associated with static self-face and dynamic whole-body self-representation.
• To segregate self-specific brain networks using principal component analysis (PCA).

Main Methods:

• Functional magnetic resonance imaging (fMRI) was employed to measure brain activity.
• A two-factorial design contrasted static pictures versus dynamic movies (Motion) and faces versus whole bodies (Body).
• Principal component analysis (PCA) was used to identify independent patterns of inter-subject variability in brain activation.

Main Results:

• Self-specific activation was observed in the bilateral ventral occipito-temporal cortex and the right parietal and frontal cortices.
• The left ventral occipito-temporal cortex showed greater self-specific activation for static self-faces than for whole bodies.
• The right parietal and frontal cortices did not exhibit the predicted preference for dynamic whole-body stimuli.

Conclusions:

• The findings provide evidence for multiple, distinct brain networks supporting visual self-recognition.
• The left ventral occipito-temporal cortex appears crucial for processing symbolic self-face representations.
• The right posterior and frontal cortical networks may play roles in visuo-spatial and conceptual self-representations, respectively.