  Brain Functional Networks (BFNs), graph theoretical models of brain activity
data, provide a systems perspective of complex functional connectivity within
the brain. Neurological disorders are known to have basis in abnormal
functional activities, that could be captured in terms of network markers.
Schizophrenia is a pathological condition characterized with altered brain
functional state. We created weighted and binary BFN models of schizophrenia
patients as well as healthy subjects starting from fMRI data in an effort to
search for network biomarkers of the disease. We investigated 45 topological
features of BFNs and their higher order combinations (2 to 8). We find that
network features embodying modularity, betweenness, assortativity and edge
density emerge as key markers of schizophrenia. Also, features derived from
weighted BFNs were observed to be more effective in disease classification as
compared to those from binary BFNs. These topological markers may provide
insights into mechanisms of functional activity underlying disease phenotype
and could further be used for designing algorithms for clinical diagnosis of
schizophrenia as well as its early detection.
