Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease

Abstract

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the *

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

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Optimal Transport-Based Early Detection of Mild Cognitive Impairment Patients Based on Magnetic Resonance Images

“…There is no conflict of interest. Additional Files ADdata FDG.csv This csv file contains the pre-processed FDG features from [16]. Label 1, 3, 4, 5 correspond to NC, E-MCI, L-MCI and AD subjects respectively.…”

“…The feature extraction process includes image registration, region of interests selection, and feature quantification. We specifically use the morphometry features extracted from VBM-MRI and FDG-PET images previously extracted by previous study [16] and denote the two classes of features as VBM and FDG features. The details of feature extraction can be found in [16].…”