  A major challenge in cognitive neuroscience is to evaluate the ability of the
human brain to categorize or group visual stimuli based on common features.
This categorization process is very fast and occurs in few hundreds of
millisecond time scale. However, an accurate tracking of the spatiotemporal
dynamics of large-scale brain networks is still an unsolved issue. Here, we
show the combination of recently developed method called dense-EEG source
connectivity to identify functional brain networks with excellent temporal and
spatial resolutions and an algorithm, called SimNet, to compute brain networks
similarity. Two categories of visual stimuli were analysed in this study:
immobile and mobile. Networks similarity was assessed within each category
(intra-condition) and between categories (inter-condition). Results showed high
similarity within each category and low similarity between the two categories.
A significant difference between similarities computed in the intra and
inter-conditions was observed at the period of 120-190ms supposed to be related
to visual recognition and memory access. We speculate that these observations
will be very helpful toward understanding the object categorization in the
human brain from a network perspective.
