Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic...

Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to severe stages, including Alzheimer's disease (AD). Brain connectivity network inferred from the functional magnetic resonance imaging data has been prevalently used to identify the individuals with MCI/AD from the normal controls. The capability to detect the causal or effective connectivity is highly desirable for understanding directed functional interactions between brain regions and further helping the detection of MCI. In this paper, we proposed a novel sparse constrained effective connectivity inference method and an elastic multilayer perceptron classifier for MCI identification. Specifically, a ultra-group constrained structure detection algorithm is first designed to identify the parsimonious topology of the effective connectivity network, in which the weak derivatives of the observable data are considered. Second, based on the identified topology structure, an effective connectivity network is then constructed by using an ultra-orthogonal forward regression algorithm to minimize the shrinking effect of the group constraint-based method. Finally, the effective connectivity network is validated in MCI identification using an elastic multilayer perceptron classifier, which extracts lower to higher level information from initial input features and hence improves the classification performance. Relatively high classification accuracy is achieved by the proposed method when compared with the state-of-the-art classification methods. Furthermore, the network analysis results demonstrate that MCI patients suffer a rich club effect loss and have decreased connectivity among several brain regions. These findings suggest that the proposed method not only improves the classification performance but also successfully discovers critical disease-related neuroimaging biomarkers.