The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier

Background: Amnestic mild cognitive impairment (aMCI) is an essential stage of early detection and potential intervention for Alzheimer's disease (AD). Patients with aMCI exhibit partially abnormal functional brain connectivity and it is suggested that these features may represent a new diagnostic marker of early AD. New method: In this paper, we constructed two brain network models, a phase synchronization index (PSI) undirected network and a directed transfer function (DTF) directed network, to evaluate the cognitive function in patients with aMCI. We then built SVM classification models using the network clustering coefficient, global efficiency and average node degree as features to distinguish between aMCI patients and controls. Results: Our results reveal a classification accuracy and AUC of 66.6 +/- 1.7% and 0.7475 and 80.0 +/- 2.2% and 0.7825, respectively, for the two network models (PSI and DTF). As the directed network model performed better than the undirected model, we introduced an improved graph theory feature, efficiency density, which resulted in an increased classification accuracy and AUC value 86.6 +/- 2.6% and 0.8295, respectively. Comparison with existing methods: The analysis of network models and the directionality of information flow is suitable for analysis of nonlinear EEG signals for assessment of the functional state of the brain. Compared with traditional network features, our proposed improved features more comprehensively evaluate transmission efficiency and density of the brain. Conclusion: In this study, we demonstrate that an improved efficiency density feature is helpful for enhancing classification the accuracy of aMCI. Moreover, directed brain network models exhibit better classification for aMCI diagnosis than undirected networks.