![]() The popular diagnosis models include Bayesian network, support vector machine (SVM), deep neural networks, multi-task and sparse learning, graph learning, multi-view learning, etc. As a widely used non-invasive technique for measuring brain activities, functional magnetic resonance imaging (fMRI) has been successfully applied to explore early diagnosis of MCI before the occurrence of clinical symptoms. ![]() Rapid advances in neuroimaging techniques provide great potentials for the study of MCI. Therefore, identifying which individuals have MCI and what biomarkers relate to MCI are major goals of current researches. An early treatment is believed to be important to slow down the progression of AD, either at the MCI stage or during the preclinical state. In some recent statistical researches, in each year, nearly 10–15% MCI patients tend to progress to probable AD. Mild cognitive impairment (MCI) is often regarded as a prodromal stage of Alzheimer’s disease (AD). Despite its simplicity, our proposed method is more effective than the baseline methods in modeling discriminative FBNs, as demonstrated by the superior MCI classification accuracy of 82.4% and the area under curve (AUC) of 0.910. Finally, we conduct experiments to identify subjects with MCI from normal controls (NCs) based on the estimated FBNs. Specifically, we first construct a high-quality network “template” based on the source data, and then use the template to guide or constrain the target of FBN estimation by a weighted l 1-norm regularizer. Inspired by the idea of transfer learning, we attempt to transfer information in high-quality data from source domain (e.g., human connectome project in this paper) into the target domain towards a better FBN estimation, and propose a novel method, namely NERTL (Network Estimation via Regularized Transfer Learning). ![]() However, there are still some challenges to estimate a “good” FBN, particularly due to the poor quality and limited quantity of functional magnetic resonance imaging (fMRI) data from the target domain (i.e., MCI study). ![]() It is believed that early treatment of MCI could slow down the progression of AD, and functional brain network (FBN) could provide potential imaging biomarkers for MCI diagnosis and response to treatment. Mild cognitive impairment (MCI) is an intermediate stage of brain cognitive decline, associated with increasing risk of developing Alzheimer’s disease (AD). ![]()
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