Diagnosis of Mild Cognitive Impairment with Ordinal Pattern Kernel

Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis

Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the edges but also express the hierarchical relationships of nodes in brain networks. On OPT, nodes are connected by ordinal edges which are constructed by using the ordinal pattern relationships of weighted edges. We represent brain networks as OPTs and further develop a new graph kernel called optimal transport (OT) based ordinal pattern tree (OT-OPT) kernel to measure the similarity between paired brain networks. In OT-OPT kernel, the OT distances are used to calculate the transport costs between the nodes on the OPTs. Based on these OT distances, we use exponential function to calculate OT-OPT kernel which is proved to be positive definite. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments on ADHD-200, ABIDE and ADNI datasets. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph methods in the classification and regression tasks.

Analysis of the relationship between mild cognitive impairment and serum klotho protein and insulin-like growth factor-1 in the elderly

BACKGROUND

Mild cognitive impairment (MCI) is a mild memory or cognitive impairment.

OBJECTIVE

To explore the relationship between serum klotho (K1) protein and insulin-like growth factor-1 and mild cognitive impairment in the elderly in order to provide accurate and appropriate indicators for clinical diagnosis and treatment of MCI.

METHODS

This randomized stratified study adopted a multistage cluster sampling method. 161 elderly patients with mild cognitive impairment were included as the MCI group, and 161 healthy people matched with the MCI group in gender, age and education were selected as the control group.

RESULTS

The levels of serum K1 protein and insulin-like growth factor-1 in the MCI group were lower than those in the control group (P< 0.05). Both IGF-1 and K1 had predictive value for MCI (P< 0.05). The area under the curve (AUC) of IGF-1 for predicting MCI was 0.859 (95% CI: 0.790∼0.929), and the AUC of K1 for predicting MCI was 0.793 (95% CI: 0.694∼0.892). The value of joint prediction of the two indicators was the highest, with an AUC of 0.939 (95% CI: 0.896-0.993).

CONCLUSION

High serum K1 and insulin-like growth factor-1 are the protective factors of cognitive impairment in MCI patients. Both IGF-1 and serum K1 proteins have predictive value for MCI, and the combination of the two indicators has the highest predictive value.

Personalized Functional Connectivity Based Spatio-Temporal Aggregated Attention Network for MCI Identification

Functional connectivity (FC) networks deri- ved from resting-state magnetic resonance image (rs-fMRI) are effective biomarkers for identifying mild cognitive impairment (MCI) patients. However, most FC identification methods simply extract features from group-averaged brain templates, and neglect inter-subject functional variations. Furthermore, the existing methods generally concentrate on spatial correlation among brain regions, resulting in the inefficient capture of the fMRI temporal features. To address these limitations, we propose a novel personalized functional connectivity based dual-branch graph neural network with spatio-temporal aggregated attention (PFC-DBGNN-STAA) for MCI identification. Specifically, a personalized functional connectivity (PFC) template is firstly constructed to align 213 functional regions across samples and generate discriminative individualized FC features. Secondly, a dual-branch graph neural network (DBGNN) is conducted by aggregating features from the individual- and group-level templates with the cross-template FC, which is beneficial to improve the feature discrimination by considering dependency between templates. Finally, a spatio-temporal aggregated attention (STAA) module is investigated to capture the spatial and dynamic relationships between functional regions, which solves the limitation of insufficient temporal information utilization. We evaluate our proposed method on 442 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieve the accuracies of 90.1%, 90.3%, 83.3% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification tasks, respectively, indicating that our method boosts MCI identification performance and outperforms state-of-the-art methods.