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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 Alzheimer's Disease Neuroimaging Initiative (ADNI) show that the proposed method has better classification performance than the start-of-the-art multimodality-based methods. Specifically, we achieved higher accuracy and area under the curve (AUC) for AD versus normal controls (NC), MCI versus NC, and MCI converters (MCI-C) versus MCI non-converters (MCI-NC) on ADNI datasets. Therefore, the proposed model not only outperforms the traditional method in terms of AD/MCI classification, but also discovers the characteristics associated with the disease, demonstrating its promise for improving disease-related mechanistic understanding.
This article was published in the following journal.
Name: Medical image analysis
Traditional feature matching methods, such as scale-invariant feature transform (SIFT), usually use image intensity or gradient information to detect and describe feature points; however, both intensi...
Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive impairment (MCI). Howeve...
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years t...
Owing to the advantages of low storage cost and high query efficiency, cross-modal hashing has received increasing attention recently. As failing to bridge the inherent modality gap between modalities...
Feature selection is a preprocessing technique that identifies the key features of a given problem. It has traditionally been applied in a wide range of problems that include biological data processin...
The main goal of this study is to identify abnormal functional and anatomical brain reorganization associated with hand, foot, and shoulder function in children (0-18 years old) with cereb...
Designing food and drink that maximizes satiety has long been an ambition of industry and public health. For obvious reasons, foods that fill faster and for longer are desirable to consum...
Aim: To evaluate the short-term effects of a multi-modal exercise program on physical performance variables in older adults with knee osteoarthritis.
Aim: To evaluate the short-term effects of a multi-modal exercise program on physical performance variables in older adults.
Patients presenting for lumbar spine surgery experience pain related to their spine condition. Following surgery, these patients also experience surgical pain resulting from disruption of ...
Adverse of favorable selection bias exhibited by insurers or enrollees resulting in disproportionate enrollment of certain groups of people.
The selection or choice of sexual partner in animals. Often this reproductive preference is based on traits in the potential mate, such as coloration, size, or behavioral boldness. If the chosen ones are genetically different from the rejected ones, then NATURAL SELECTION is occurring.
Non-invasive methods of visualizing the CENTRAL NERVOUS SYSTEM, especially the brain, by various imaging modalities.
The introduction of error due to systematic differences in the characteristics between those selected and those not selected for a given study. In sampling bias, error is the result of failure to ensure that all members of the reference population have a known chance of selection in the sample.
A chronic multi-system disorder of CONNECTIVE TISSUE. It is characterized by SCLEROSIS in the SKIN, the LUNGS, the HEART, the GASTROINTESTINAL TRACT, the KIDNEYS, and the MUSCULOSKELETAL SYSTEM. Other important features include diseased small BLOOD VESSELS and AUTOANTIBODIES. The disorder is named for its most prominent feature (hard skin), and classified into subsets by the extent of skin thickening: LIMITED SCLERODERMA and DIFFUSE SCLERODERMA.
Neurology - Central Nervous System (CNS)
Alzheimer's Disease Anesthesia Anxiety Disorders Autism Bipolar Disorders Dementia Epilepsy Multiple Sclerosis (MS) Neurology Pain Parkinson's Disease Sleep Disorders Neurology is the branch of me...
Biological therapy involves the use of living organisms, substances derived from living organisms, or laboratory-produced versions of such substances to treat disease. Some biological therapies for cancer use vaccines or bacteria to stimulate the body&rs...