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多层次动态大脑网络的联合学习用于自闭症谱系障碍诊断

Na Li¹, Jinjie Xiao², Ning Mao³

¹School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China; Department of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shanxi, China.

Computers in biology and medicine

|February 13, 2024

概括

这项研究引入了一种新的图形卷积网络 (GCN) 架构,用于诊断自闭症谱系障碍 (ASD). 该模型通过实现双向信息流和利用边缘权重来提高诊断准确度来增强大脑网络分析.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
人工智能的人工智能

背景情况:

图形卷积网络 (GCNs) 擅长模拟复杂的大脑网络数据.
目前用于大脑网络的GCN方法经常忽视双向信息交换,并丢失关键边缘权重数据.

研究的目的:

开发一个多层次的动态大脑网络联合学习架构,使用GCN来改善自闭症谱系障碍 (ASD) 诊断.
通过结合双向信息流和边缘重量灵敏度来解决现有GCN模型的局限性.

主要方法:

构建多层次的动态大脑网络.
雇佣基于GCN的联合学习,用于跨网络层面的交互式信息交换.
集成了一个边缘自我注意力机制,将差异权重分配给节点之间的连接,增强特征选择.

主要成果:

在ASD诊断中达到81.5%的准确性.
在大脑网络层之间证明了高阶和低阶信息的有效双向传输.
通过利用边缘权重,展示了ASD相关特征的增强表现.

结论:

拟议的多层次动态GCN架构有效地捕捉了ASD诊断的复杂大脑网络动态.
双向信息交换和边缘重量灵敏度显著提高了诊断性能.
这种方法为了解和诊断神经发育障碍 (如ASD) 提供了一个有前途的工具.

关键词:

自闭症谱系障碍自闭症谱系障碍动态的大脑网络动态的大脑网络边缘自我注意机制边缘自我注意机制图表卷积网络卷积网络联合学习 (joint learning) 是一种联合学习方式.

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相关概念视频

Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.