Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

• URL: http://arxiv.org/abs/2209.11372v1
• Date: Fri, 23 Sep 2022 02:17:27 GMT
• Title: Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis
• Authors: Jun Yu, Zhaoming Kong, Liang Zhan, Li Shen, and Lifang He
• Abstract summary: We propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities.
• Score: 25.958167380664083
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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