  Recent advances in neuroimaging have enabled studies in functional
connectivity (FC) of human brain, alongside investigation of the neuronal basis
of cognition. One important FC study is the representation of vision in human
brain. The release of publicly available dataset BOLD5000 has made it possible
to study the brain dynamics during visual tasks in greater detail. In this
paper, a comprehensive analysis of fMRI time series (TS) has been performed to
explore different types of visual brain networks (VBN). The novelty of this
work lies in (1) constructing VBN with consistently significant direct
connectivity using both marginal and partial correlation, which is further
analyzed using graph theoretic measures, (2) classification of VBNs as formed
by image complexity-specific TS, using graphical features. In image
complexity-specific VBN classification, XGBoost yields average accuracy in the
range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than
that using negative correlation. This result not only reflects the
distinguishing graphical characteristics of each image complexity-specific VBN,
but also highlights the importance of studying both positively correlated and
negatively correlated VBN to understand the how differently brain functions
while viewing different complexities of real-world images.
