Diagnosis of Brain Diseases via Multi-Scale Time-Series Model - Zhang, Zehua; Xu, Junhai; Tang, Jijun; Zou, Quan; Guo, Fei (Frontiers in Neuroscience, 2019)
The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. However, many existing methods still has some drawbacks, such as the limitations of graph theory, the local sensitivity of functional connectivity and the missing temporal or context information. The fMRI time series data not only contains specific numerical information, but also involves contextual information and global fluctuation information. Here, we propose a novel time-series model based on Jensen-Shannon divergence for identifying the brain disease via fMRI data. First of all, we extract the discrete probability distribution of co-activity in multi-scale time series data. In addition, the contextual information is taken into account in analyzing the correlation and causality among the fMRI data. Then, we design a time-series model to measure the similarity of brain functional connectivity. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks.