Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder.
Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder.
复制标题
通过分层监督局部 CCA 进行特征融合，用于诊断自闭症谱系障碍
DOI：
10.1007/s11682-016-9587-5
复制
发表时间：
2017-08
影响因子：
3.2
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Zhao F;Qiao L;Shi F;Yap PT;Shen D
自闭症谱系障碍（ASD）的早期诊断对于及时进行医疗干预、提高患者生活质量、减轻社会经济负担至关重要。基于神经成像的ASD诊断的关键问题是识别区分特征，然后将它们融合以产生准确的诊断。在本文中，我们提出了一个新的框架，融合互补和歧视性的功能，从不同的成像方式。具体来说，我们集成的Fisher判别准则和局部相关信息的典型相关分析（CCA）的框架，给出了一种新的特征融合方法，称为监督本地CCA（SL-CCA），专门迎合本地和全球多模态功能。为了缓解与SL-CCA相关的邻域选择问题，我们进一步提出了一种分层SL-CCA（HSL-CCA），通过执行SL-CCA逐渐变化的邻域大小。在多模态ABIDE数据库上的实验表明，该方法具有上级性能.此外，基于特征权重分析，我们发现只有少数特定的大脑区域在ASD诊断中发挥积极作用。这些大脑区域包括壳核、楔前叶和眶额皮层，它们与人类的情绪调节和记忆形成高度相关。这些发现与ASD的行为表型一致。
Early diagnosis of autism spectrum disorder (ASD) is critical for timely medical intervention, for improving patient quality of life, and for reducing the financial burden borne by the society. A key issue in neuroimaging-based ASD diagnosis is the identification of discriminating features and then fusing them to produce accurate diagnosis. In this paper, we propose a novel framework for fusing complementary and discriminating features from different imaging modalities. Specifically, we integrate the Fisher discriminant criterion and local correlation information into the canonical correlation analysis (CCA) framework, giving a new feature fusion method, called Supervised Local CCA (SL-CCA), which caters specifically to local and global multimodal features. To alleviate the neighborhood selection problem associated with SL-CCA, we further propose a hierarchical SL-CCA (HSL-CCA), by performing SL-CCA with the gradually varying neighborhood sizes. Extensive experiments on the multimodal ABIDE database show that the proposed method achieves superior performance. In addition, based on feature weight analysis, we found that only a few specific brain regions play active roles in ASD diagnosis. These brain regions include the putamen, precuneus, and orbitofrontal cortex, which are highly associated with human emotional modulation and memory formation. These finding are consistent with the behavioral phenotype of ASD.