The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM- MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.
翻译:最近的研究显示,多模式成像技术的结合可以更好地反映病理特征,并有助于更准确地诊断AD和MCI。在本文中,我们建议采用新型的基于高温的多模式特征选择和回归法,用于诊断和从正常控制中生物标志识别AD和MCI。具体地说,我们利用抗压结构利用多模式数据中固有的高水平相关性信息,并调查多线回归模型中的高温层聚变。我们介绍了我们使用三种成像模型(VBM-MRI、FDG-PET和AV45-PET)分析ADNI数据的方法的实际优势,这些模型具有疾病严重程度和认知分数的临床参数。实验结果表明,我们提议的方法在与疾病诊断和确定特定疾病区域和模式差异方面的状态技术相比,表现优异。这一工作的代码可公开查阅 https://github.com/trug22。