Hierarchical Synchronization Estimation of Low- and High-Order Functional Connectivity Based on Sub-Network Division for the Diagnosis of Autism Spectrum Disorder

A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis

Autism spectrum disorder (ASD) is a lifelong neurological disease, which seriously reduces the patients' life quality. Generally, an early diagnosis is beneficial to improve ASD children's life quality. Current methods based on samples from multiple sites for ASD diagnosis perform poorly in generalization due to the heterogeneity of the data from multiple sites. To address this problem, this paper presents a similarity measure-based approach for ASD diagnosis. Specifically, the few-shot learning strategy is used to measure potential similarities in the RS-fMRI data distributions, and, furthermore, a similarity function for samples from multiple sites is trained to enhance the generalization. On the ABIDE database, the presented approach is compared to some representative methods, such as SVM and random forest, in terms of accuracy, precision, and F1 score. The experimental results show that the experimental indicators of the proposed method are better than those of the comparison methods to varying degrees. For example, the accuracy on the TRINITY site is more than 5% higher than that of the comparison method, which clearly proves that the presented approach achieves a better generalization performance than the compared methods.

A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection

Alzheimer's disease is an irreversible neurological disease, therefore prompt diagnosis during its early stage, i.e., early mild cognitive impairment (MCI), is crucial for effective treatment. In this paper, we propose an automatic diagnosis method, a few-shot learning-based pairwise functional connectivity (FC) similarity measure method, to detect early MCI. We first employ a sliding window strategy to generate a dynamic functional connectivity network (FCN) using each subject's rs-fMRI data. Then, normal controls (NCs) and early MCI patients are distinguished by measuring the similarity between the dynamic FC series of corresponding brain regions of interest (ROIs) pairs in different subjects. However, previous studies have shown that FC patterns in different ROI-pairs contribute differently to disease classification. To enable the FCs of different ROI-pairs to make corresponding contributions to disease classification, we adopt a self-attention mechanism to weight the FC features. We evaluated the suggested strategy using rs-fMRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results point to the viability of our approach for detecting MCI at an early stage.

A Spatial Spectrum Estimation Method for Optimization and Improvement of Resource Allocation and Management of Public Sport and Health Facilities

With the improvement of people's health awareness, the state increases the construction of public sport facilities, which complicates the allocation and management of resources. The existing spatial spectrum estimation method cannot eliminate the relevant interference data, resulting in duplicate data in the results of resource allocation and management, reducing the accuracy of resource allocation and management. In order to optimize the allocation and management of public sport facilities resources, this study proposes a spatial spectrum estimation method, which is used to deeply tap the potential information in public sport facilities resources and optimize the allocation and management. First, analyze the resources of public sport facilities and put forward the feature vector of allocation and management optimization. Then, use the spatial spectrum estimation method to learn the test samples, get the optimal threshold and weight, and build the resource allocation and management model of public sport facilities. Finally, the accuracy of spatial spectrum estimation method is 98% and the variation range is (0, 10), which is better than the accuracy of the original algorithm is 80% and the variation range is (0, 20). Moreover, change of the spatial spectrum estimation method is smoother, and the correlation between various analyses is better. In unit time, the amount of configuration and management data of spatial spectrum estimation method is higher than that of existing algorithms, which indirectly indicate that the configuration and management time of the spatial spectrum estimation method is short. At the same time, unstructured data account for a large proportion of the data tested this time. Therefore, the accuracy and variation range of the spatial spectrum estimation method is good, which is suitable for the construction of public sport facilities and realizes the optimization of resource allocation and management.