Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis
The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.
KeywordsMild Cognitive Impairment Functional Connectivity Mild Cognitive Impairment Patient Functional Connectivity Network Thresholding Strategy
- 1.Wang, T., et al.: Abnormal changes of brain cortical anatomy and the association with plasma microRNA107 level in amnestic mild cognitive impairment. Front. Aging Neurosci. 8, 112 (2016)Google Scholar
- 3.Wang, T., et al.: Multilevel deficiency of white matter connectivity networks in Alzheimer’s disease: a diffusion MRI study with DTI and HARDI models. Neural Plast. 2016, 2947136 (2016)Google Scholar
- 4.Jin, Y., et al.: Automated multi-atlas labeling of the fornix and its integrity in Alzheimer’s disease. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 140–143(2015)Google Scholar