Comparing Healthy Subjects and Alzheimer’s Disease Patients using Brain Network Similarity: a Preliminary Study

Brain network analysis is an interdisciplinary field linking computational neuroscience with biomedical data analytics, aiming for instance to map the brain into interconnected regions at different conditions, resting versus inactivity, and normal versus pathological. In our study, brain connectivity modeling and analysis are performed via graph theory. Several studies have revealed alterations in structural/functional brain networks of people diagnosed with several brain disorders. Most of the studies in the literature used graph theoretical approaches to characterize these disorders, however less attention was given for distance-based approaches (or network similarity). Our objective here is to compare the brain networks of normal versus Alzheimer’s disease (AD) patients by performing distance-based graph similarity analysis between their electrophysiological brain networks. The brain networks of a group of 10 healthy control subjects and 10 AD patients were constructed from Electroencephalography (EEG) signals recorded at rest, followed by the computation of intra- and inter-group network similarity via Siminet and DeltaCon algorithms at the EEG alpha and beta frequency bands. Results showed that AD networks have significantly lower similarity scores and tend to be more heterogenous with respect to the healthy networks. This work provides a preliminary foundation for the effective use of graph similarity in the computational assessment of pathological brain networks compared to healthy subjects.