Research On The Neural Mechanism Of Face Recognition Based On The Resting-state Brain Functional Connectivity

Face recognition is one of the essential activities in human’s daily life.As a special kind of visual stimulus,faces include multiple types of information such as identity,facial expression,actions,gender and so on.The complexity of face information and the similarity of face structures lead to the specificity of face recognition in communication and social life for human beings.Nowadays the neural mechanism of face recognition is one of the research hotspots in neuroscience.Researchers have used modern functional neuroimaging methodology to explore the brain activation patterns of face recognition tasks.A brain region located in the junction of the temporal and occipital lobe was found to be closely associated with face recognition processing.This pitch of cortical area was accordingly called the fusiform face area(FFA).Further research has found that the human face recognition relied on a distributed neural network within the brain,and there was a huge individual difference in the face recognition ability among people.Researches based on the functional brain network as well as individual differences in face recognition ability can characterize the neural mechanism underlying face recognition in a more comprehensive way,and therefore provide direct evidence supporting the functional brain network of face processing.The present study used the resting-state functional magnetic resonance imaging(fMRI)methodology to acquire healthy adults’ original information of brain activities,and whereby explored the intrinsic and specialized facial recognition function network.The present study included five steps:First,the resting state fMRI data of healthy adults were collected to establish the resting-state brain function network.Second,the facial recognition ability index of each participant is obtained through the face recognition test.Third,the relation between face recognition ability and functional brain network was examined using the univariate analysis method based on graph theory analysis.Fourth,the prediction mode of face recognition ability based on functional brain network was constructed using the multivariate analysis method based on the elastic network regression analysis.Fifth,the brain regions that played significant roles in resting-state brain function network were identified according to the number of brain regions that they connected to,respectively.The present study revealed a resting-state brain function network of face recognition,in which the temporal pole played an important role.This finding is consistent with that of studies using experimental paradigm based on cognitive task.Thus,the findings of the present study suggested that even in the resting state,there was a network of face processing in our brain.Additionally,the present study confirms the advantages of multivariate analysis to univariate analysis in the studies of functional brain networks.