Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems

Casanova, Ramon; Hsu, Fang‐Chi

doi:10.1371/journal.pone.0044877

Cited by 41 publications

(33 citation statements)

References 40 publications

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“…The feature selection uses the regularized logistic regression framework (Friedman et al, 2010) that produces a path of feature subsets with different cardinalities (called regularization path ), and has been used widely in previous works (Huttunen et al, 2012, 2013; Ryali et al, 2010) for the multi-voxel pattern analyses of functional neuroimaging data as well as for AD related studies using structural MRI data (Ye et al, 2012; Casanova et al, 2011a,b, 2012; Shen et al, 2011; Janoušová et al, 2012). As the RLR procedure is a supervised learning method, the input has to be fully labeled data.…”

Section: Methodsmentioning

confidence: 99%

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

et al. 2015

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Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer’s disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.

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Section: Methodsmentioning

confidence: 99%

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

et al. 2015

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“…An alternative approach that operates directly in the voxel space was proposed by Casanova et al [241] who used penalized logistic regression and coordinate-wise descent optimization to overcome these problems of large scale classification. A subsequent paper by the same group [395] examined classification methods from structural MRI from the perspective of linear ill-posed problems and in the absence of dimensionality reduction techniques. They found that logistic regression, linear regression, and SVM classifiers were robust to increased dimensionality.…”

Section: Methods Papersmentioning

confidence: 99%

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2015

Alzheimer's & Dementia

272

224

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The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer’s Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding ...

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“…Casanova et al (2012) used linear SVM, regularized logistic regression, and linear regression classifier (LRC) for the classification of patients with AD. Rangini and Jiji (2013) used SVM and AdaSVM for hippocampal segmentation.…”

Section: Support Vector Machinementioning

confidence: 99%

“…Casanova et al (2012) used LRC to classify the structural MRI images in AD and compared the performance of LRC and SVM based on different dimensions and sample sizes. In that work, the classification feature vectors were created as GM voxels obtained by segmentation.…”

Section: Logistic Regression Classificationmentioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

107

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AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

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Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems

Cited by 41 publications

References 40 publications

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

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