中间性中心性
注意缺陷多动障碍
计算机科学
功能磁共振成像
动态功能连接
人工智能
网络分析
网络动力学
注意力网络
功率图分析
韵律学
机器学习
图形
中心性
神经科学
心理学
理论计算机科学
数学
布线(电子设计自动化)
精神科
计算机网络
物理
离散数学
组合数学
量子力学
静态路由
路由协议
作者
Harun Pirim,Miaolin Fan,Haifeng Wang
标识
DOI:10.1109/ichi54592.2022.00030
摘要
This research examines the dynamics of brain resting-state functional connectivity (rs-FC) using functional magnetic resonance imaging (fMRI) data for attention-deficit/hyperactivity disorder (ADHD). Machine learning is a high potential approach for brain disorder diagnosis based on the constructed rs-FC brain network. The dynamics of brain connectivity directly impact the choice of algorithm design and model performance evaluation. In this study, we applied a sliding window to fMRI time series data from ADHD-200 dataset for constructing a time-varying network, and we experimented three window sizes (30, 40, and 60 seconds). Then, 10 different network metrics are calculated for each network, and being compared between the ADHD vs. Control groups. We considered the brain rs-FC network as temporal graphs and provided a comprehensive statistical analysis to understand how the network metrics can help differentiate ADHD vs. Control groups. The experimental results show that the graph dynamics have a significant influence on the selection of the key network metrics. However, average shortest path and betweenness centrality show high potential to be used to diagnose ADHD in the Control groups. This study is expected to provide a preliminary study of using temporal network approaches for computer-aided ADHD diganosis.
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