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一个功能系统信息图神经网络框架,用于量化ASD中可解释的大脑功能障碍.

Yong Jiao1, Xinxu Wei2, Lifang He3

  • 1Department of Bioengineering, Lehigh University, Bethlehem, 18015, PA, USA.

Neural networks : the official journal of the International Neural Network Society
|November 22, 2025
PubMed
概括
此摘要是机器生成的。

一个新的AI框架,功能系统信息图神经网络 (FS-GNN),使用脑成像准确诊断自闭症谱系障碍 (ASD). 这种方法利用大脑连接来识别可靠的神经模式,以改善ASD诊断.

关键词:
自闭症谱系障碍 自闭症谱系障碍功能连接性的功能连接性.功能性磁共振成像技术 功能性磁共振成像技术图形神经网络是一个神经网络.大规模的功能系统.

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 自闭症谱系障碍 (ASD) 诊断依赖于主观评估,缺乏明确的病理理解.
  • 大脑连接和功能系统对于理解神经发育障碍至关重要.
  • 目前对ASD的诊断方法需要提高准确性和客观性.

研究的目的:

  • 开发一个先进的AI框架来诊断ASD使用功能磁共振成像 (fMRI).
  • 为了利用大脑连接组拓和功能,提高ASD检测.
  • 为了揭示与ASD相关的大脑功能障碍的新见解.

主要方法:

  • 提出了一个功能系统信息图形神经网络 (FS-GNN) 框架.
  • 集成可学习的位置编码,用于感兴趣的大脑区域 (ROI).
  • 纳入大规模的大脑系统作为先验知识,并使用系统驱动的规范化来对ROI进行加权.

主要成果:

  • 在ABIDE数据集上,FS-GNN在ASD诊断中实现了75.02%的准确性,73.22%的精度和71.64%的回忆.
  • 超越了现有的机器学习和GNN方法.
  • 在ROI和系统水平上确定了与已知的ASD生物标志物相一致的脑功能障碍.

结论:

  • 通过利用大脑连接组信息,FS-GNN在诊断ASD方面表现出有效性.
  • 该框架为准确的ASD诊断提供可解释和可靠的神经模式.
  • 这种方法为客观的ASD评估和理解提供了一个有希望的方向.