Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Lama, Ramesh Kumar; Gwak, Jeonghwan; Park, Jeong-Seon; Lee, Sang-Woong

doi:https://doi.org/10.1155/2017/5485080

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Research Article | Open Access

Volume 2017 | Article ID 5485080 | https://doi.org/10.1155/2017/5485080

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Ramesh Kumar Lama,^1,2Jeonghwan Gwak,^1,3Jeong-Seon Park,⁴and Sang-Woong Lee^1,2

Academic Editor: Ashish Khare

Received04 Dec 2016

Revised30 Mar 2017

Accepted13 Apr 2017

Published18 Jun 2017

Abstract

Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

1. Introduction

Alzheimer’s disease (AD) is a slow fatal neurodegenerative disease affecting people over the age of 65 years [1], while early-onset AD is also diagnosed before 65. The deposition of two abnormal protein fragments known as plagues and tangles in the brain causes the death of neuron cells. The hippocampus, where the memories are first formed, is the initially affected region by AD, and thus early symptoms of AD include memory problems resulting difficulties in word finding and thinking processes [2]. AD patients suffer from a lack of initiative, changes in personality or behavior in day-to-day functions at home, or at work, and in taking care of oneself, eventually, leading to death. The brain volume reduces dramatically through time and affects most of its functions with the progression of AD.

With the increase in the population of elderly people in developed countries, AD is going to be a major problem in socioeconomic implications. According to the recent report [3], it is expected that the number of affected people will be doubled in the next 20 years and one in two aged above 85 years will suffer from AD by 2050. Thus, accurate diagnosis of AD is very important, especially, at its early stage. Conventionally, the diagnosis of AD is performed by a neuropsychological examination in support of structural imaging. It is reported in [4] that (1) in the early stage of AD, degeneration of neurons takes place in the medial temporal lobe, (2) gradually affecting the entorhinal cortex, the hippocampus, and the limbic system, and (3) neocortical areas are affected at the final stage. Therefore, the study of medial temporal lobe atrophy (MTA), particularly in the hippocampus, the entorhinal cortex, and the amygdala provides the evidence of the progression of AD. Generally, MTA is measured in terms of voxel-based [5], vertex-based [6], and ROI-based [7] approaches. However, as the disease progresses, other regions in the brain are also affected. In such cases, whole-brain methods are preferred rather than a specific region-based method; then, the characterization of brain atrophy for differentiating AD and MCI patients can be performed more efficiently.

In recent years, major advances in neuroimaging have provided opportunities to study neurological-related diseases, resulting improvements in early and accurate detection of AD [5, 6, 8]. Magnetic resonance imaging (MRI) is more widely used in AD-related studies because of its noninvasive nature and lack of pain to patients. In addition, MRI provides an excellent spatial resolution and good contrast [5–7, 9]. Thus, several studies have used structural MRI- (sMRI-) based biomarkers to classify AD [10–19], which describes brain atrophy and change in the size of brain tissues. Similarly, functional MRI (fMRI) [20] can be utilized to characterize the hemodynamic response relevant to neural activity and functional/structural connectivity [21–23], which can be used to describe neurological disorders in the whole brain at the connectivity level. In this paper, we focused only on AD classification using sMRI. The intensity and stage of the neurodegeneration can be identified by the help of atrophy measured by sMRI [24]. Thus, sMRI-based feature extraction has attracted the attention for researchers of AD classification. These studies include morphometric methods such as region of interest (ROI)/volume of interest (VOI) grey matter voxels in the automatic segmentation of images [25] and the sMRI measurement of the hippocampus and the medial temporal lobe [26].

Several machine learning techniques have been used to distinguish AD subjects from elderly control subjects using different biomarkers. The commonly used classifiers include support vector machine (SVM), artificial neural network (ANN), and other ensemble classifiers. Among them, SVM and the variants have been widely studied due to its relatively good accuracy and ability to deal with high-dimensional data. A SVM-type classifier (e.g., Magnin et al. [27]) begins with a learning stage from the training dataset consisting of well-characterized subjects with known states (i.e., labels for the subjects are given). Then, the classifier aims to maximize the margin of the training data by constructing the optimal separating hyperplane or a set of hyperplanes in a single- or higher-dimensional space. At a testing stage, classification is performed for test dataset based on the learned hyperplane(s). In general, three-dimensional (3D) T1-weighted MR images of each subject were automatically parcellated into ROIs. Grey matter from each ROI is extracted, as shown in Figure 1, as a feature for classification.

Zhang et al. [10] proposed a multimodal classification approach by utilizing multiple-kernel SVM based on the biomarkers including sMRI [18, 19], positron emission tomography (PET) [6], and cerebrospinal fluid (CSF) [28] to discriminate AD (or MCI) and normal control (NC) subjects. From the binary classification (i.e., AD versus NC and MCI versus NC) results, their proposed model could obtain a good accuracy for AD classification and an encouraging accuracy for MCI classification. Liu et al. [29, 30] proposed deep learning-based multiclass classification among normal controls (NC), MCI nonconverters (ncMCI), MCI converters (cMCI), and AD subjects based on 83 ROIs of sMRI images and the corresponding registered PET images. Stacked autoencoders (SAE) were used as unsupervised learning to obtain high-level features, and then softmax logistic regression was adopted as the classifier. While the experimental results showed reasonably good performance, it is still arguing that the denoising nature of SAE can increase the difficulty of suitable feature learning and thus it may be difficult for practical use. Li et al. [31] proposed fine-grained new features based on principle component analysis (PCA), stability selection, dropout, and multitask learning, where restricted Boltzmann machine (RBM) model was used as the deep learning architecture. 93 ROIs of MRI and PET images, together with CSF biomarkers, are used. Ye et al. [32] introduced conceptual machine learning-based multimodal data fusion approach using MRI, PET, genetic, CSF, demographic for AD-related research, and functional connectivity analysis. Recently, Rama et al. [33] proposed IVM-based classification approach for multiclass classification. In this method, only the subset of features from structural MRI was used as input to kernel logistic regression thus reducing the computational cost. This method used total 65 ROIs as features for training and testing and achieved the accuracy of up to 70% while classifying AD, MCI, and HC and 76.9% for binary classification of HC and AD [33].

While several approaches have been proposed for classification of different AD stages, with relatively small dataset, it is very difficult to extract effective information. This work focuses on comparing and presenting efficient classification approaches working robustly for a relatively small dataset. To this end, we present and compare three representative classifiers, with an efficient feature selection approach, including SVM, an import vector machine (IVM) and a regularized extreme learning machine (RELM) for the multiclass classification of different stages of AD progression.

2. Materials for Study

2.1. sMRI Dataset

Data used in preparation of this paper were obtained from the Alzheimer’s disease neuroimaging initiative database (ADNI) (http://adni.loni.usc.edu/). The ADNI database was launched in 2003 as a public-private partnership. The primary goal of ADNI has been to test whether the serial MRI, PET, other biological markers, and clinical and neurophysical assessment can be combined to measure the progression of midcognitive impairment and the early AD.

2.2. Subjects

The ADNI dataset consists of more than 6000 subjects aged from 18 to 96. From it, we selected 214 subjects aged between 65 and 96. The selected participants met the criteria defined in the ADNI protocol. We constructed balanced dataset consisting of 214 subjects as follows: (1)70 NC subjects: 33 males, 37 females; age ± SD= 76.3 ± 5.4 years, range = 60–90 years; mini-mental state estimation (MMSE) score = 29.2 ± 1.0, range = 25–30.(2)74 MCI subjects who had not converted to AD within 18 months: 38 males, 36 females; age ± SD = 74.5 ± 7.2 years, range = 58–88 years; MMSE score = 27.2 ± 1.7, range = 24–30.(3)70 AD subjects: 39 males, 31 females; age ± SD = 76.0 ± 7.3 years, range = 55–91 years; MMSE = 23.2 ± 2.0, range = 18–27.

Table 1 shows a summary of demographic status of the selected subjects.

All structural MR (sMR) scans used in this work were acquired from 3T scanners. The main focus of this work was to elaborate the supervised multiclass classification among NC, MCI, and AD based on different classifiers. Thus, to obtain unbiased estimates of the classifier performance, the selected subjects were randomly split up into two groups of the training dataset and the testing dataset. The algorithms were trained on a training set, and the performances of the diagnostic sensitivity and specificity together with accuracy were evaluated on an independent test dataset. The division process considers balanced age and sex distributions.

2.3. Preprocessing of sMRI Data

We used a fully automated pipeline of the FreeSurfer 5.3.0 software package for reconstruction and volumetric segmentation from all the sMRI images and extracted the pattern of useful data. The software performs a series of preprocessing operations with the FreeSurfer’s recon-all processing pipeline on the original sMRI data as shown in Figure 2. The preprocessing steps include motion correction, T1-weighted image averaging, registration of volume to the Talairach space, skull striping with a deformable template model. The white surface and the pial surface are generated for each hemisphere using encoding the shape of the corpus callosum and pons in the Talairach space and following the intensity gradients from the white matter. The accurate matching of the morphologically homologous cortical locations across subjects was estimated using the mapping of the atlas based on a cortical surface to a sphere aligning the cortical patterns. Cortical thickness at each vertex of the cortex is denoted by the average shortest distance between white and pial surfaces. The area of every triangle in a standardized spherical surface tessellation provides the surface area. Similarly, the registration surface based on the folding pattern was used to compute the local curvature. The method developed by Schaer [34] was used to measure the folding index over the whole cortical surface. All the extracted features are explained in terms of feature measures as in Table 2.

2.4. Details of the sMRI Data

We perform binary classification for NC versus AD and multiclass classification using the one-versus-all (OVA) class setting for NC, MCI, and AD. For the subjects and groups chosen as in Table 2, volumetric features, fM5, in Table 2, were used for the study, and for each feature, we computed the grey matter tissue volume from the individual subject’s sMRI image. Block brain regions selected for the classification are shown in Figure 3. Each tissue is discriminated from other tissues by using color code defined by FreeSurfer software package. The left column presents the coronal view followed by the saggital view in the middle column and the axial view at the rightmost column. We followed neurological convention for the view. All sMR scans used in this paper were acquired from 3T scanners.

3. Proposed Methods: Classification of Stages of AD Progression

We used the three representative machine-learning classification algorithms, SVM, IVM, and RELM. The stepwise block diagram of the classification of stages of AD progression is shown in Figure 4.

3.1. Efficient Feature Selection

In neuroimaging analysis, the number of features per subject can be very high compared to the number of subjects, which is commonly referred to as the curse of dimensionality. We perform an efficient feature selection method based on PCA which is a method widely used to reduce the dimensionality of a high-dimensional (imaging) data [25]. As the result, the most information representative dimensions are kept while the least important ones are excluded. PCA generates new features which are a linear combination of the initial features and maps each instance of the given dataset present in a d-dimensional space to a k-dimensional subspace such that . The set of new dimensions generated are called the principal components (PCs), and each PC is directed towards maximum variance excluding the variance already accounted for in all its preceding components. Subsequently, the first component covers the maximum variance, and each component that follows it covers a lesser value of variance. PCs can be represented as where is the ith PC, is the original feature , and represents the numerical coefficient for .

3.2. SVM Classifier

SVM [35] is basically a binary classifier which is useful for the classification of both separable and nonseparable data. It has been used in the neuroimaging field and considered as one of the most popular machine learning tools in the neuroscience domain in the last decade. It is a supervised classification algorithm and finds the optimal hyperplane that separates both classes with maximum margin from support vectors during the training phase. For the testing of new data points, the classifier’s decision is based on the estimated hyperplane. For the linearly separable patterns, linear SVM is used. However, linear SVM cannot guarantee better performance in complex cases with nonseparable patterns. In such scenario, linear SVM is extended using kernel trick. The input patterns are mapped into a higher dimensional space using linear and nonlinear functions known as kernels. Linear and nonlinear radial basis function (RBF) kernels are widely used SVM kernels.

3.3. IVM Classifier

The fundamental principle of IVM proposed by Zhu and Hastie [36] is built on kernel logistic regression (KLR). It has not merely performed well in the binary classification as SVM, and it can be naturally generalized to the multiclass classification. Thus, we begin with the explanation of logistic regression. Let represent observed samples with class labels pattern classes. The training set is represented as. For the binary class problem, where input samples are independent and identically distributed, the conditional class posterior probability is estimated using the following logistic regression model:

The logistic regression predicts the class based on probabilities which are either for or for . Thus, we can express the cost function of logistic regression as

In order to fit the parameters for the given model by training the given data points, we try to find the parameter that minimizes . As a result, is selected, which is most likely to generate the labels as the same as in the training set. The minimization can be obtained by using the gradient and the Hessian. In order to prevent overfitting, one may introduce a prior over the parameters and optimize

Therefore, the iteration scheme could simply be formulated with the Newton-Raphson iteration method. To extend the linear model to a nonlinear one, the original features are transformed into the higher dimensional space using a kernel function

The model of kernel logistic regression now presumes the a posteriori probabilities are given by with as the nth column of the kernel matrix K, and the unknown parameter refers to c classes. The parameters are determined in an iterative way by optimizing the regularized objective function. One of the limitations of the standard KLR is that all possible training samples are used to evaluate the kernel function, thus increasing the computational complexity and the memory requirement for large datasets. Meanwhile, the complexity of the classifier can be controlled by enforcing the sparseness in the learning model. The sparse kernel machine uses only the kernel function evaluated at a subset of the training data points for prediction of new inputs. The most common methods to implement sparseness are by introducing a suitable prior or by a subset selection. One of the popular examples for sparse kernel machine is SVM, which only supports that vectors are used to predict new inputs. The main idea of incorporating sparseness into KLR is to select a subset of feature vectors out of the training set T. Thus, the kernel matrix only consists of the selected a subset of important kernels from all samples T. IVM uses a smaller fraction of training data to realize the sparse KLR. The subset is determined by a greedy manner. This method begins with empty set and then constructs the set of import vectors by successively adding data samples. The construction process of sets stops once the convergence criterion is reached. The convergence criterion is used by the ratio with a small integer such as the regularization, and the kernel parameter defines the threshold for excluding import vectors. Consequently, this criterion influences the sparseness of the model.

3.4. RELM Classifier

Single hidden-layer feed forward neural networks (SLFNs), such as the back propagation (BP) learning algorithm, are widely used machine learning techniques for research in various fields. These methods minimize the cost function to maintain the accuracy within an acceptable range by searching the specific input weights and hidden layer biases, which leads to increase in computational cost. Extreme learning machine (ELM) is a learning algorithm implemented without iteratively tuning the artificial hidden nodes, thus decreasing the computation time [37]. ELM is an effective solution for SLFNs. The SLFN with L hidden nodes and an activation function g(x) is expressed as where is an output weight matrix between the hidden nodes and output nodes. is the hidden node output. Unlike SVM and other BP-based methods, the parameters of the hidden layer such as the input weight w_i and the hidden layer biases b_i need not to be tuned and can be generated randomly before the training samples are acquired. Given N training samples , ELM solves the learning problem by minimizing the error between and : where

Here, H is called the hidden layer output matrix. The output weights can be calculated as where is the Moore-Penrose generalized inverse of the matrix with the advantage of speed. ELM is well-suited for the tasks related to neuroimaging and big data for the classification of binary and multiclass settings. However, the decrease in computation time increases the error in the output, thus decreasing the accuracy. To increase the accuracy, ELM is combined with sparse representation. This hybrid algorithm performs classification in two fundamental steps [38–40]. In the first stage, the ELM network is trained with the conventional training approach. However, in the testing stage, reliability-based classification is used. In reliability-based classification, the ELM classifier is employed if the test data is correctly classified; otherwise, the sparse representation-based classification is used [41]. Additionally, a regularization term is added to improve generalization performance and make the solution more robust. Finally, the output weight of the RELM can be expressed as

4. Experimental Results and Analysis

4.1. Permutation Testing

Permutation testing can be applied to assess the statistical significance of the classifier [42]. The assessment proceeds with the selection of the test statistic of the classifier and assigns random labels to the classifier by permuting the class labels for the training dataset. Permutation testing involves performing cross validation (CV) on data for which the diagnostic label has been randomly permuted. This leads to a distribution of classification results under the null hypothesis that the classifier cannot accurately predict the clinical labels from the data. The value of the permuted prediction rate against the prediction rate with the original data labels indicates the significance of the classifier. In this work, we used 70/30 CV, 10-fold CV, and leave-one-out (LOO) CV methods. Experiments for both binary and multiclass classification were carried out with the same setup.

4.2. Performance Evaluation Methods

We evaluated the performance of the proposed algorithm with the IVM, SVM, and RELM classifiers for each specific test including binary and multiclass classification tasks. The performance of the binary classification for the two subjects S1 and S2 can be visualized in a form of a confusion matrix as shown in Table 3. Diagonal elements of the matrix indicate the number of correct predictions by the classifier. The elements can be further divided into true positive (TP) and true negative (TN), which represent correctly identified controls. Similarly, the number of wrongly classified subjects may be represented by false positive (FP) and false negative (FN).

The accuracy measures the proportion of examples that are correctly labeled by the classifier.

However, for dataset with very unbalanced class distribution, accuracy in (12) may be a misleading performance metric. Thus, two performance metrics known as sensitivity and specificity are also used.

The sensitivity in (13) measures the rate of true positives while the specificity in (14) measures the rate of true negatives. The performance metrics for the multiclass classification are easily extended as the averaged ones on the OVA setting.

4.3. Binary Classification: Results and Analysis

The experimental results of binary classification (NC versus AD) are shown in Table 4, and those with feature selection are in Table 5. 141 subjects were randomly selected for the binary classification. Initially, we randomly segregated the training and testing dataset and used the first 111 randomly chosen subjects from each group for training and the remaining 30 subjects for testing the classifier. Similarly, in 10-fold cross-validation, all 141 subjects were randomly divided into equally sized subsets, that is, 10% testing subjects and 90% training subjects, for each of the 10-fold sets of the CV. In addition, for the nested validation, we repeated the classification experiment 10 times in the case of the 10-fold CV and leave-one-out CV, and 100 times in the case of the conventional 70/30 CV, to ensure the robustness of the classification results. The mean accuracy of all the repetitions was calculated by the final results.

In Table 4 showing the baseline performance of different classifiers, all classifiers except IVM obtained good performance. There was no substantial difference, in terms of accuracy, between the results obtained with IVM and SVM, and RELM is better than the others in 10-fold CV; however, SVM is better than RELM in LOO CV. For the feature selection, the datasets of size were mapped to the given principal component framework and transformed into the dataset of size , where is the number of subjects and is the original number of features. The dataset originally consists of total 54 features. The number of PCs represented as ranging from 2 to 20, with an incremental offset of two, was checked and the best one was selected for each classifier. From repeated simulations, we achieved the generally good accuracy when the value of is set to 10. As shown in Table 5, by adopting feature selection, the similar performance characteristic was observed in terms of accuracy. From Figure 5, it is easily observed the effectiveness of the feature selection approach in 10-fold CV and LOO CV cases. It is meaningful that from our repeated simulations, we found that the results of 70/30 CV case, which is a widely used setting, are not stable (i.e., it has large variance with different trials) mainly due to overfitting problems, and thus, the results were not listed in this work.

(a)

(b)

4.4. Multiclass Classification: Results and Analysis

For multiclass classification, we adopted all labeled 214 subjects in Table 1. The same subjects were used in binary classification, and we adopted three CV methods. From Tables 6 and 7, it is easily observed that RELM outperforms SVM and IVM in terms of accuracy. From Figure 6, we also could see the effectiveness of the feature selection approach in 10-fold CV and LOO CV cases. Similar to binary classification cases, on the 70/30 CV case, we obtained the experimental results with large variance, and thus, they were also excluded from the analysis. From the results, it is naturally driven that multiclass classification (which is the general form in clinical diagnosis of AD) assisted by RELM is effective compared to the other considered representative classifies.

(a)

(b)

4.5. Discussion on the Results

It has been known that in many problem tasks, IVM generally performs similar with SVM in terms of accuracy and provides probabilistic output. From our experiments, we could confirm that SVM generates better accuracy compared to IVM, which is mainly attributed to the robustness of SVM to outliers. The main impetus of this study was to compare representative classifiers, SVM, IVM, and RELM for binary and multiclass classification tasks. Trivially, the accuracy of the binary classification cases was higher than the corresponding multiclass classification cases. Also, the experimental results on large dataset of 214 subjects verified that RELM-based AD diagnosis framework (significantly) outperform the others with higher accuracy. To the best of our knowledge, this is the first study in which the RELM framework was used for multiclass classification on sMRI data obtained from the ADNI dataset. To classify the effectiveness of feature selection in combination with the classifiers, we utilized the PCA-based feature selection method as an efficient approach to validate its efficiency. It selects features that represent higher degrees of significance based on the internal linear SVM-based classification scores, and thus, it has the possibility of making classification significantly more accurate. The experimental results also support that such adoption of feature selection can be beneficial to improve the accuracy of the classifiers, SVM, IVM, and RELM. From the noteworthy results, we could conclude that the approaches for the stage classification can be used as an effective assistive tool for the establishment of a clinical diagnosis.

5. Conclusions and Future Work

The early diagnosis of AD and MCI is essential for patient care and research, and it is widely accepted that preventive measures plays an important role to delay or alleviate the progression of AD. For the classification task of different stages of AD progression, the smaller number of training samples and the larger number of feature representations are the major challenges. In this study, we investigated SVM, IVM, and RELM for the classification problem. In IVM, only the subsets of the input vectors of KLR are selected by minimizing the regularized cost function to reduce computation time. RELM is an effective solution for SLFNs implemented without iteratively tuning the artificial hidden nodes and adopts reliability-based classification where ELM is adopted if the test data is correctly classified, and sparse representation is selected for the other cases. Experiments on the ADNI dataset showed that RELM-based classifier could significantly improve accuracy in both binary and multiclass classification tasks. In addition, we could observe that adoption of the PCA-based feature selection could improve the accuracy slightly. While this study is focusing on the stage diagnosis of AD progression using sMRI alone, further study is still being carried out to improve the accuracy by elaborating the classifiers, possibly using a model ensemble approach, and feature selection. Also, the studies of adding more modalities such as fMRI and PET in combination with sMRI are also one of our future researches.

Conflicts of Interest

The authors declare that there are no competing interests regarding the publication of this paper.

Acknowledgments

This work was supported by the National Research Foundation (NRF) grant funded by the Korean government (NRF-2015R1D1A1A01060422, NRF-2014M3C7A1046050, and NRF-2016M3C7A1905477) and partially supported by the MISP (Ministry of Science, ICT & Future Planning), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & Communications Technology Promotion). Data collection and sharing were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense, Award no. W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provide funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California. The Alzheimer’s Disease Neuroimaging Initiative data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

References

American Psychiatric Association and Task Force on DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, vol. xxv of DSM-IV, American Psychiatric Association, Washington, DC, 4th edition, 1994.
D. Schmitter, A. Roche, B. Maréchal et al., “An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease,” NeuroImage: Clinical, vol. 7, pp. 7–17, 2015.
View at: Publisher Site | Google Scholar
Alzheimer’s association, “2016 Alzheimer’s disease facts and figures,” Alzheimer’s and Dementia, vol. 12, no. 4, pp. 459–509, 2016.
View at: Publisher Site | Google Scholar
K. A. Johnson, N. C. Fox, R. A. Sperling, and W. E. Klunk, “Brain imaging in Alzheimer disease,” Cold Spring Harbor Perspectives in Medicine, vol. 2, no. 4, article a006213, 2012.
View at: Publisher Site | Google Scholar
H. Hanyu, T. Sato, K. Hirao, H. Kanetaka, T. Iwamoto, and K. Koizumi, “The progression of cognitive deterioration and regional cerebral blood flow patterns in Alzheimer’s disease: a longitudinal SPECT study,” Journal of the Neurological Sciences, vol. 290, no. 1-2, pp. 96–101, 2010.
View at: Publisher Site | Google Scholar
K. R. Gray, R. Wolz, R. A. Heckemann, P. Aljabar, A. Hammers, and D. Rueckert, “Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease,” NeuroImage, vol. 60, no. 1, pp. 221–229, 2012.
View at: Publisher Site | Google Scholar
Y. J. Chen, G. Deutsch, R. Satya, H. G. Liu, and J. M. Mountz, “A semi-quantitative method for correlating brain disease groups with normal controls using SPECT: Alzheimer’s disease versus vascular dementia,” Computerized Medical Imaging and Graphics, vol. 37, no. 1, pp. 40–47, 2013.
View at: Publisher Site | Google Scholar
F. Liu, L. Zhou, C. Shen, and J. Yin, “Multiple kernel learning in the primal for multi-modal Alzheimer’s disease classification,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 984–990, 2014.
View at: Publisher Site | Google Scholar
J. M. Górriz, F. Segovia, J. Ramírez, A. Lassl, and D. S. Gonzalez, “GMM based SPECT image classification for the diagnosis of Alzheimer’s disease,” Applied Soft Computing, vol. 11, no. 2, pp. 2313–2325, 2011.
View at: Publisher Site | Google Scholar
D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen, “Multimodal classification of Alzheimer’s disease and mild cognitive impairment,” NeuroImage, vol. 55, no. 3, pp. 856–867, 2011.
View at: Publisher Site | Google Scholar
G. A. Papakostas, A. Savio, M. Graña, and V. G. Kaburlasos, “A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data,” Neurocomputing, vol. 150, pp. 37–42, 2015.
View at: Publisher Site | Google Scholar
I. Beheshti and H. Demirel, “Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease,” Computers in Biology and Medicine, vol. 64, pp. 208–216, 2015.
View at: Publisher Site | Google Scholar
C. Aguilar, E. Westman, J. S. Muehlboeck, P. Mecocci, B. Vellas, and M. Tsolaki, “Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment,” Psychiatry Research: Neuroimaging, vol. 212, no. 2, pp. 89–98, 2013.
View at: Publisher Site | Google Scholar
E. Westman, J. S. Muehlboeck, and A. Simmons, “Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion,” NeuroImage, vol. 62, no. 1, pp. 229–238, 2012.
View at: Publisher Site | Google Scholar
M. Li, Y. Qin, F. Gao, W. Zhu, and X. He, “Discriminative analysis of multivariate features from structural MRI and diffusion tensor images,” Magnetic Resonance Imaging, vol. 32, no. 8, pp. 1043–1051, 2014.
View at: Publisher Site | Google Scholar
E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, “Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects,” NeuroImage, vol. 104, pp. 398–412, 2015.
View at: Publisher Site | Google Scholar
E. E. Bron, M. Smits, H. Vrenken, F. Barkhof, and P. Scheltens, “Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CAD dementia challenge,” NeuroImage, vol. 111, pp. 562–579, 2015.
View at: Publisher Site | Google Scholar
S. Farhan, M. A. Fahiem, and H. Tauseef, “An ensemble-of-classifiers based approach for early diagnosis of Alzheimer’s disease: classification using structural features of brain images,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 862307, 11 pages, 2014.
View at: Publisher Site | Google Scholar
Y. Zhang, Z. Dong, P. Phillips et al., “Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning,” Frontier in Computational Neuroscience, vol. 9, p. 66, 2015.
View at: Publisher Site | Google Scholar
A. H. Andersen, W. S. Rayens, Y. Liu, and C. D. Smith, “Partial least squares for discrimination in fMRI data,” Magnetic Resonance Imaging, vol. 30, no. 3, pp. 446–452, 2012.
View at: Publisher Site | Google Scholar
L. Mesrob, “DTI and structural MRI classification in Alzheimer’s disease,” Advances in Molecular Imaging, vol. 02, pp. 12–20, 2012.
View at: Google Scholar
Y. Fan, S. M. Resnick, X. Wu, and C. Davatzikos, “Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study,” NeuroImage, vol. 41, no. 2, pp. 277–285, 2008.
View at: Publisher Site | Google Scholar
M. Graña, M. Termenon, A. Savio, A. P. Gonzalez, J. Echeveste, and J. M. Pérez, “Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation,” Neuroscience Letters, vol. 502, no. 3, pp. 225–229, 2011.
View at: Publisher Site | Google Scholar
W. Lee, B. Park, and K. Han, “Classification of diffusion tensor images for the early detection of Alzheimer’s disease,” Computers in Biology and Medicine, vol. 43, no. 10, pp. 1313–1320, 2013.
View at: Publisher Site | Google Scholar
K. I. Diamantaras and S. Y. Kung, Principal Component Neural Networks, Wiley, New York, 1996.
J. Barnes, J. W. Bartlett, L. A. van de Pol et al., “A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease,” Neurobiology of Aging, vol. 30, no. 11, pp. 1711–1723, 2009.
View at: Publisher Site | Google Scholar
B. Magnin, L. Mesrob, S. Kinkingnéhun et al., “Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI,” Diagnoistic Neuroradiology, vol. 51, no. 2, pp. 73–83, 2009.
View at: Publisher Site | Google Scholar
A. M. Fjell, K. B. Walhovd, C. Fennema-Notestine et al., “CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease,” The Journal of Neuroscience, vol. 30, no. 6, pp. 2088–2101, 2010.
View at: Publisher Site | Google Scholar
S. Liu, S. Liu, W. Cai et al., “Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease,” IEEE Transactions of Biomedical Engineering, vol. 62, no. 4, pp. 1132–1140, 2015.
View at: Publisher Site | Google Scholar
S. Liu, S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early diagnosis of Alzheimer’s disease with deep learning,” in Proceedings/IEEE International Symposium on Biomedical Imaging: from nano to macro, pp. 1015–1018, 2014.
View at: Publisher Site | Google Scholar
F. Li, L. Tran, K. H. Thung, S. Ji, D. Shen, and J. Li, “A robust deep model for improved classification of AD/MCI patients,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 5, pp. 1610–1616, 2015.
View at: Publisher Site | Google Scholar
J. Ye, T. Wu, J. Li, and K. Chen, “Machine learning approaches for the neuroimaging study of Alzheimer’s disease,” IEEE Computer, vol. 4, no. 4, pp. 99–101, 2011.
View at: Publisher Site | Google Scholar
R. K. Rama, H. C. Park, and S.-W. Lee, “Sparse feature selection using import vector machines for classification of Alzheimer’s disease,” in Proceedings of 2016 KING Fall Conference, 2016.
View at: Publisher Site | Google Scholar
M. Schaer, M. B. Cuadra, N. Schmansky, B. Fischl, J. P. Thiran, and S. Eliez, “How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index,” Journal of Visualized Experiments, no. 59, article e3417, 2012.
View at: Publisher Site | Google Scholar
C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
View at: Publisher Site | Google Scholar
J. Zhu and T. Hastie, “Kernel logistic regression and the import vector machine,” Journal of Computational and Graphical Statistics, vol. 14, no. 1, pp. 185–205, 2005.
View at: Google Scholar
G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
View at: Google Scholar
W. Zhang, H. Shen, Z. Ji, G. Meng, and B. Wang, “Identification of mild cognitive impairment using extreme learning machines model,” in Intelligent Computing Theories and Methodologies: 11th International Conference, ICIC 2015, August 2015.
View at: Publisher Site | Google Scholar
X. Peng, P. Lin, T. Zhang, and J. Wang, “Extreme learning machine-based classification of ADHD using brain structural MRI data,” PloS One, vol. 8, no. 11, article e79476, 2013.
View at: Publisher Site | Google Scholar
M. N. I. Qureshi, B. Min, H. J. Jo, and B. Lee, “Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study,” PloS One, vol. 11, no. 8, 2016.
View at: Publisher Site | Google Scholar
E. Cambria and G. B. Huang, “Extreme learning machines,” IEEE Intelligent Systems, vol. 28, no. 6, pp. 2–31, 2013.
View at: Google Scholar
P. Golland and B. Fischl, “Permutation tests for classification: towards statistical significance in image based studies,” in Information Processing in Medical Imaging: 18th International Conference, C. Taylor and J. A. Noble, Eds., IPMI, 2003.
View at: Google Scholar

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Copyright © 2017 Ramesh Kumar Lama et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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