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In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer's disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods.
Pubmed ID: 31584953
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Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.
View all literature mentionsDatabase of the results of the ADNI study. ADNI is an initiative to develop biomarker-based methods to detect and track the progression of Alzheimer's disease (AD) that provides access to qualified scientists to their database of imaging, clinical, genomic, and biomarker data.
View all literature mentionsA public charity whose mission is to support the NIH in its mission to improve health, by forming and facilitating public-private partnerships for biomedical research and training. Its vision is Building Partnerships for Discovery and Innovation to Improve Health. The FNIH draws together the world''s foremost researchers and resources, pressing the frontier to advance critical discoveries. They are recognized as the number-one medical research charity in the countryleveraging support, and convening high level partnerships, for the greatest impact on the most urgent medical challenges we face today. Grants are awarded as part of a public-private partnership with the National Heart, Lung, and Blood Institute (NHLBI) on behalf of The Heart Truth in support of women''s heart health education and research. Funding for the Community Action Program is provided by the FNIH through donations from individuals and corporations including The Heart Truth partners Belk Department Stores, Diet Coke, and Swarovski. Successful biomedical research relies upon the knowledge, training and dedication of those who conduct it. Bringing multiple disciplines to bear on health challenges requires innovation and collaboration on the part of scientists. Foundation for NIH partnerships operate in a variety of ways and formats to recruit, train, empower and retain their next generation of researchers. From lectures and multi-week courses, to scholarships and awards through fellowships and residential training programs, their programs respond to the needs of scientists at every level and stage in their careers.
View all literature mentionsA set of useful accessory programs to the LINKAGE package. It simplifies the performance of a large array of parametric and nonparametric tests for linkage and association on data entered in LINKAGE format pedigree and parameter files. (entry from Genetic Analysis Software)
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