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自闭症谱系障碍的诊断基于基于深度展开的空间约束表示

Dajiang Lei¹, Tao Zhang¹, Yue Wu¹

¹School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.

Medical & biological engineering & computing

|July 24, 2023

概括

这项研究引入了一种新的深度学习模型,用于自闭症谱系障碍 (ASD) 诊断,使用fMRI数据的功能性大脑网络 (FBNs). 新方法通过捕捉非线性关系并将网络构建与分类集成来提高准确性.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

准确的自闭症谱系障碍 (ASD) 诊断对于治疗和预后至关重要.
来自fMRI的功能性大脑网络 (FBNs) 用于ASD诊断,但现有的方法在与非线性关系和单独的网络构建/分类步骤中扎.
这种分离导致了主体间的变化,并降低了FBN的统计能力.

研究的目的:

通过将一个新的FBN构建模型与卷积分类器集成,为ASD识别提出一个新的端到端框架.
解决现有方法在捕捉非线性关系和减少FBN中主体间的变异性方面的局限性.

主要方法:

开发了基于深度解滚的空间约束表示 (DUSCR) 模型,用于FBN的构建,使用近接梯度下降算法和深度解滚.
集成的DUSCR与卷积原型学习分类器,用于端到端的ASD识别框架.
预先将静态fMRI数据处理成时间序列和3D坐标,用于输入DUSCR模型.

主要成果:

拟议的DUSCR模型与卷积分类器集成,在ABIDE I数据集上显著改善了模型性能和分类准确性.
端到端的框架有效地捕捉了fMRI数据中的非线性关系.
观察到FBN估计的个体间变异性减少,增强了统计能力.

结论:

拟议的基于深度展开的空间约束表示 (DUSCR) 模型提供了一种更有效的方法来构建功能性大脑网络用于ASD诊断.
与传统方法相比,综合端到端框架显著提高了ASD识别的准确性.
这种方法有望改善自闭症谱系障碍的诊断工具.

关键词:

自闭症谱系障碍自闭症谱系障碍分类分类分类分类.深度展开的展开以诊断为导向的诊断方式.功能性大脑网络功能性大脑网络

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