A New Minimum Spanning Tree-Based Analysis of EEG and MEG With Application to Classification of Alzheimer’s Disease

Each brain cognitive deficit is likely to be associated with alteration in brain connectivity. Given the vast number of connections, studying and identifying these changes is neither practical nor efficient. Therefore, we used minimum spanning tree (MST) to encounter these challenges. We hypothesize that there is a global subgraph for each cognitive disease which consists of most repeated connections for each group and because of the slow changes of the disease, we call this as “disease-specific subgraph”. To validate this model, two datasets are used, a MEG dataset with 80 subjects with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy control (HC), and an EEG dataset recorded from 88 subjects: 36 AD, 23 frontotemporal dementia (FTD), and 29 HC. The weighted phase lag index (wPLI), phase lag index (PLI), and phase locking value (PLV) were employed to assess the functional connectivity and brain graph construction. However, due to less sensitivity to volume conduction and noise, wPLI was utilized to construct the graph and MST matrices. The results and the location of hub nodes showed higher integrated graphs of AD, MCI and FTD which is related to poorer cognitive performance and compatible with physiological observations. Moreover, the characteristics of MST matrices of subjects were used as cognition features and showed high classification rates in comparison with state-of-the-art methods.