1
|
Lei B, Zhu Y, Liang E, Yang P, Chen S, Hu H, Xie H, Wei Z, Hao F, Song X, Wang T, Xiao X, Wang S, Han H. Federated Domain Adaptation via Transformer for Multi-Site Alzheimer's Disease Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3651-3664. [PMID: 37527297 DOI: 10.1109/tmi.2023.3300725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.
Collapse
|
2
|
Lei D, Zhang T, Wu Y, Li W, Li X. Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation. Med Biol Eng Comput 2023; 61:2829-2842. [PMID: 37486440 DOI: 10.1007/s11517-023-02859-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/25/2023] [Indexed: 07/25/2023]
Abstract
Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy. The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.
Collapse
Affiliation(s)
- Dajiang Lei
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Tao Zhang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yue Wu
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Weisheng Li
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xinwei Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China.
| |
Collapse
|
3
|
Li C, Liu M, Xia J, Mei L, Yang Q, Shi F, Zhang H, Shen D. Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning. IEEE J Biomed Health Inform 2023; 27:5430-5438. [PMID: 37616143 DOI: 10.1109/jbhi.2023.3306460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-β deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-β PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aβ-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aβ-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.
Collapse
|
4
|
Yang J, Li XX, Liu F, Nie D, Lio P, Qi H, Shen D. Fast Multi-Contrast MRI Acquisition by Optimal Sampling of Information Complementary to Pre-Acquired MRI Contrast. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1363-1373. [PMID: 37015608 DOI: 10.1109/tmi.2022.3227262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Recent studies on multi-contrast MRI reconstruction have demonstrated the potential of further accelerating MRI acquisition by exploiting correlation between contrasts. Most of the state-of-the-art approaches have achieved improvement through the development of network architectures for fixed under-sampling patterns, without considering inter-contrast correlation in the under-sampling pattern design. On the other hand, sampling pattern learning methods have shown better reconstruction performance than those with fixed under-sampling patterns. However, most under-sampling pattern learning algorithms are designed for single contrast MRI without exploiting complementary information between contrasts. To this end, we propose a framework to optimize the under-sampling pattern of a target MRI contrast which complements the acquired fully-sampled reference contrast. Specifically, a novel image synthesis network is introduced to extract the redundant information contained in the reference contrast, which is exploited in the subsequent joint pattern optimization and reconstruction network. We have demonstrated superior performance of our learned under-sampling patterns on both public and in-house datasets, compared to the commonly used under-sampling patterns and state-of-the-art methods that jointly optimize the reconstruction network and the under-sampling patterns, up to 8-fold under-sampling factor.
Collapse
|
5
|
Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
Collapse
Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
| |
Collapse
|
6
|
A Multiview Deep Learning Method for Brain Functional Connectivity Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5782569. [PMID: 36254204 PMCID: PMC9569226 DOI: 10.1155/2022/5782569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/24/2022] [Indexed: 12/01/2022]
Abstract
The brain functional connectivity classification based on deep learning is a research hotspot nowadays. However, the classification performance is far behind the demand of clinical applications. To alleviate the problem, this paper proposes a multiview deep learning method for brain functional connectivity classification. Firstly, the proposed method adopts multiple brain atlases to identify brain regions and thereby builds different brain functional connectivity of different views. Secondly, it uses a multiview feature selection strategy to select out the most discriminative features of each view with the assistance of other views. Then, it trains a stacked autoencoder to extract deep features of the brain functional connectivity of each view. At last, it utilizes a multiview fusion strategy to take full advantage of complementary information of different views for brain functional connectivity classification. The proposed method has been compared with several deep learning-based brain functional connectivity classification methods on three public datasets of neuropsychiatric disorders. The experimental results have validated the superior performance of the proposed method.
Collapse
|
7
|
Zhang Y, Zhou T, Wu W, Xie H, Zhu H, Zhou G, Cichocki A. Improving EEG Decoding via Clustering-Based Multitask Feature Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3587-3597. [PMID: 33556021 DOI: 10.1109/tnnls.2021.3053576] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution and, hence, can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multitask feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multitask learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses. We then train a linear support vector machine with the optimized features for EEG pattern decoding. Extensive experimental studies are conducted on three EEG data sets to validate the effectiveness of our algorithm in comparison with other state-of-the-art approaches. The improved experimental results demonstrate the outstanding superiority of our algorithm, suggesting its prominent performance for EEG pattern decoding in BCI applications.
Collapse
|
8
|
Liu L, Wang YP, Wang Y, Zhang P, Xiong S. An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders. Med Image Anal 2022; 81:102550. [PMID: 35872360 DOI: 10.1016/j.media.2022.102550] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 10/17/2022]
Abstract
It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.
Collapse
Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China.
| | - Yu-Ping Wang
- Dthe Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
| | - Yi Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Pei Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Shufeng Xiong
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| |
Collapse
|
9
|
Bai T, Du M, Zhang L, Ren L, Ruan L, Yang Y, Qian G, Meng Z, Zhao L, Deen MJ. A novel Alzheimer’s disease detection approach using GAN-based brain slice image enhancement. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
10
|
Hong X, Huang K, Lin J, Ye X, Wu G, Chen L, Chen E, Zhao S. Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification. Front Aging Neurosci 2022; 14:891433. [PMID: 35721019 PMCID: PMC9199857 DOI: 10.3389/fnagi.2022.891433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/19/2022] [Indexed: 12/03/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. To distinguish the stage of the disease, AD classification technology challenge has been proposed in Pattern Recognition and Computer Vision 2021 (PRCV 2021) which provides the gray volume and average cortical thickness data extracted in multiple atlases from magnetic resonance imaging (MRI). Traditional methods either train with convolutional neural network (CNN) by MRI data to adapt the spatial features of images or train with recurrent neural network (RNN) by temporal features to predict the next stage. However, the morphological features from the challenge have been extracted into discrete values. We present a multi-atlases multi-layer perceptron (MAMLP) approach to deal with the relationship between morphological features and the stage of the disease. The model consists of multiple multi-layer perceptron (MLP) modules, and morphological features extracted from different atlases will be classified by different MLP modules. The final vote of all classification results obtains the predicted disease stage. Firstly, to preserve the diversity of brain features, the most representative atlases are chosen from groups of similar atlases, and one atlas is selected in each group. Secondly, each atlas is fed into one MLP to fetch the score of the classification. Thirdly, to obtain more stable results, scores from different atlases are combined to vote the result of the classification. Based on this approach, we rank 10th among 373 teams in the challenge. The results of the experiment indicate as follows: (1) Group selection of atlas reduces the number of features required without reducing the accuracy of the model; (2) The MLP architecture achieves better performance than CNN and RNN networks in morphological features; and (3) Compared with other networks, the combination of multiple MLP networks has faster convergence of about 40% and makes the classification more stable.
Collapse
Affiliation(s)
- Xin Hong
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
- Key Laboratory of Computer Vision and Machine Learning (Huaqiao University), Fujian Province University, Xiamen, China
- *Correspondence: Xin Hong ;
| | - Kaifeng Huang
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
| | - Jie Lin
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
| | - Xiaoyan Ye
- Fuzhou Comvee Network and Technology Co., Ltd, Fuzhou, China
- Xiaoyan Ye
| | - Guoxiang Wu
- College of Foreign Languages, Huaqiao University, Quanzhou, China
| | - Longfei Chen
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - E. Chen
- Department of Neurosurgery, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
- E. Chen
| | - Siyu Zhao
- College of Computer Science and Technology, Huaqiao University, Xiamen, China
| |
Collapse
|
11
|
WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer's Disease. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9554768. [PMID: 35602645 PMCID: PMC9117080 DOI: 10.1155/2022/9554768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/02/2022] [Accepted: 02/25/2022] [Indexed: 01/15/2023]
Abstract
Alzheimer's disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.
Collapse
|
12
|
Liu R, Li G, Gao M, Cai W, Ning X. Large Margin and Local Structure Preservation Sparse Representation Classifier for Alzheimer’s Magnetic Resonance Imaging Classification. Front Aging Neurosci 2022; 14:916020. [PMID: 35693338 PMCID: PMC9177229 DOI: 10.3389/fnagi.2022.916020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive dementia in which the brain shrinks as the disease progresses. The use of machine learning and brain magnetic resonance imaging (MRI) for the early diagnosis of AD has a high probability of clinical value and social significance. Sparse representation classifier (SRC) is widely used in MRI image classification. However, the traditional SRC only considers the reconstruction error and classification error of the dictionary, and does not consider the global and local structural information between images, which results in unsatisfactory classification performance. Therefore, a large margin and local structure preservation sparse representation classifier (LMLS-SRC) is developed in this manuscript. The LMLS-SRC algorithm uses the classification large margin term based on the representation coefficient, which results in compactness between representation coefficients of the same class and a large margin between representation coefficients of different classes. The LMLS-SRC algorithm uses local structure preservation term to inherit the manifold structure of the original data. In addition, the LMLS-SRC algorithm imposes the ℓ2,1-norm on the representation coefficients to enhance the sparsity and robustness of the model. Experiments on the KAGGLE Alzheimer’s dataset show that the LMLS-SRC algorithm can effectively diagnose non AD, moderate AD, mild AD, and very mild AD.
Collapse
Affiliation(s)
- Runmin Liu
- College of Sports Engineering and Information Technology, Wuhan Sports University, Wuhan, China
| | - Guangjun Li
- College of Sports Engineering and Information Technology, Wuhan Sports University, Wuhan, China
- *Correspondence: Guangjun Li,
| | - Ming Gao
- College of Sports Science and Technology, Wuhan Sports University, Wuhan, China
| | - Weiwei Cai
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
- AiTech Artificial Intelligence Research Institute, Changsha, China
| | - Xin Ning
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
13
|
AlSaeed D, Omar SF. Brain MRI Analysis for Alzheimer's Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22082911. [PMID: 35458896 PMCID: PMC9025443 DOI: 10.3390/s22082911] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/24/2022] [Accepted: 04/01/2022] [Indexed: 05/27/2023]
Abstract
Alzheimer's disease is the most common form of dementia and the fifth-leading cause of death among people over the age of 65. In addition, based on official records, cases of death from Alzheimer's disease have increased significantly. Hence, early diagnosis of Alzheimer's disease can increase patients' survival rates. Machine learning methods on magnetic resonance imaging have been used in the diagnosis of Alzheimer's disease to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on MRI images is complicated, requiring the involvement of an expert user. Therefore, implementing deep learning as an automatic feature extraction method could minimize the need for feature extraction and automate the process. In this study, we propose a pre-trained CNN deep learning model ResNet50 as an automatic feature extraction method for diagnosing Alzheimer's disease using MRI images. Then, the performance of a CNN with conventional Softmax, SVM, and RF evaluated using different metric measures such as accuracy. The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.
Collapse
|
14
|
Feng J, Zhang SW, Chen L, Zuo C. Detection of Alzheimer’s Disease Using Features of Brain Region-of-Interest-Based Individual Network Constructed with the sMRI Image. Comput Med Imaging Graph 2022; 98:102057. [DOI: 10.1016/j.compmedimag.2022.102057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 02/18/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
|
15
|
Das S, Panigrahi P, Chakrabarti S. Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques. J Alzheimers Dis Rep 2021; 5:771-788. [PMID: 34870103 PMCID: PMC8609489 DOI: 10.3233/adr-210314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 01/25/2023] Open
Abstract
Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.
Collapse
Affiliation(s)
- Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India
| | - Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), Kolkata, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad, Uttar Pradesh, India
| |
Collapse
|
16
|
Zeng A, Rong H, Pan D, Jia L, Zhang Y, Zhao F, Peng S. Discovery of Genetic Biomarkers for Alzheimer's Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies. Interdiscip Sci 2021; 13:787-800. [PMID: 34410590 DOI: 10.1007/s12539-021-00470-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/01/2021] [Accepted: 08/01/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To identify candidate neuroimaging and genetic biomarkers for Alzheimer's disease (AD) and other brain disorders, especially for little-investigated brain diseases, we advocate a data-driven approach which incorporates an adaptive classifier ensemble model acquired by integrating Convolutional Neural Network (CNN) and Ensemble Learning (EL) with Genetic Algorithm (GA), i.e., the CNN-EL-GA method, into Genome-Wide Association Studies (GWAS). METHODS Above all, a large number of CNN models as base classifiers were trained using coronal, sagittal, or transverse magnetic resonance imaging slices, respectively, and the CNN models with strong discriminability were then selected to build a single classifier ensemble with the GA for classifying AD, with the help of the CNN-EL-GA method. While the acquired classifier ensemble exhibited the highest generalization capability, the points of intersection were determined with the most discriminative coronal, sagittal, and transverse slices. Finally, we conducted GWAS on the genotype data and the phenotypes, i.e., the gray matter volumes of the top ten most discriminative brain regions, which contained the ten most points of intersection. RESULTS Six genes of PCDH11X/Y, TPTE2, LOC107985902, MUC16 and LINC01621 as well as Single-Nucleotide Polymorphisms, e.g., rs36088804, rs34640393, rs2451078, rs10496214, rs17016520, rs2591597, rs9352767 and rs5941380, were identified. CONCLUSION This approach overcomes the limitations associated with the impact of subjective factors and dependence on prior knowledge while adaptively achieving more robust and effective candidate biomarkers in a data-driven way. SIGNIFICANCE The approach is promising to facilitate discovering effective candidate genetic biomarkers for brain disorders, as well as to help improve the effectiveness of identified candidate neuroimaging biomarkers for brain diseases.
Collapse
Affiliation(s)
- An Zeng
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Huabin Rong
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Dan Pan
- School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, People's Republic of China.
| | - Longfei Jia
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Yiqun Zhang
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Fengyi Zhao
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, School of Computer Science, National University of Defense Technology, Peng Cheng Lab, Shenzhen, 518000, People's Republic of China.
| |
Collapse
|
17
|
Chen Z, Lei H, Huang Z, Lei B. Latent Space Learning and Feature Learning using Multi-template for Multi-classification of Alzheimer's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1844-1847. [PMID: 34891646 DOI: 10.1109/embc46164.2021.9630795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is a common brain disease in the elderly that leads to thinking, memory, and behavior disorders. As the population ages, the proportion of AD patients is also increasing. Accordingly, computer-aided diagnosis of AD attracts more and more attention recently. In this paper, we propose a novel model combining latent space learning and feature learning using features extracted from multiple templates for AD multi-classification. Specifically, latent space learning is employed to obtain the inter-relationship between multiple templates, and feature learning is performed to explore the intrinsic relation in feature space. Finally, the most discriminative features are selected to boost the multi-classification performance. Our proposed model uses the data from the Alzheimer's disease neuroimaging initiative dataset. Furthermore, a series of comparative experiments indicate that our proposed model is quite competitive.
Collapse
|
18
|
Yang C, Wang P, Tan J, Liu Q, Li X. Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Comput Biol Med 2021; 139:104963. [PMID: 34700253 DOI: 10.1016/j.compbiomed.2021.104963] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/13/2021] [Accepted: 10/17/2021] [Indexed: 11/16/2022]
Abstract
The accurate diagnosis of autism spectrum disorder (ASD), a common mental disease in children, has always been an important task in clinical practice. In recent years, the use of graph neural network (GNN) based on functional brain network (FBN) has shown powerful performance for disease diagnosis. The challenge to construct "ideal" FBN from resting-state fMRI data remained. Moreover, it remains unclear whether and to what extent the non-Euclidean structure of different FBNs affect the performance of GNN-based disease classification. In this paper, we proposed a new method named Pearson's correlation-based Spatial Constraints Representation (PSCR) to estimate the FBN structures that were transformed to brain graphs and then fed into a graph attention network (GAT) to diagnose ASD. Extensive experiments on comparing different FBN construction methods and classification frameworks were conducted on the ABIDE I dataset (n = 871). The results demonstrated the superiority of our PSCR method and the influence of different FBNs on the GNN-based classification results. The proposed PSCR and GAT framework achieved promising classification results for ASD (accuracy: 72.40%), which significantly outperformed competing methods. This will help facilitate patient-control separation, and provide a promising solution for future disease diagnosis based on the FBN and GNN framework.
Collapse
Affiliation(s)
- Chunde Yang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China; School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Panyu Wang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jia Tan
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Qingshui Liu
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xinwei Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China.
| |
Collapse
|
19
|
Bi XA, Zhou W, Li L, Xing Z. Detecting Risk Gene and Pathogenic Brain Region in EMCI Using a Novel GERF Algorithm Based on Brain Imaging and Genetic Data. IEEE J Biomed Health Inform 2021; 25:3019-3028. [PMID: 33750717 DOI: 10.1109/jbhi.2021.3067798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.
Collapse
|
20
|
Zhang L, Wang L, Gao J, Risacher SL, Yan J, Li G, Liu T, Zhu D. Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Med Image Anal 2021; 72:102082. [PMID: 34004495 DOI: 10.1016/j.media.2021.102082] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/20/2021] [Accepted: 04/13/2021] [Indexed: 01/22/2023]
Abstract
Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring "deep relations" between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.
Collapse
Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Li Wang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA; Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Jean Gao
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gang Li
- Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7160, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA.
| | | |
Collapse
|
21
|
Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
22
|
Wang M, Hao X, Huang J, Wang K, Shen L, Xu X, Zhang D, Liu M. Hierarchical Structured Sparse Learning for Schizophrenia Identification. Neuroinformatics 2020; 18:43-57. [PMID: 31016571 DOI: 10.1007/s12021-019-09423-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.
Collapse
Affiliation(s)
- Mingliang Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.,The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China
| | - Xiaoke Hao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China
| | - Jiashuang Huang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China
| | - Kangcheng Wang
- Department of Psychology, Southwest University, Chongqing, China
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
23
|
Kang H, Xia L, Yan F, Wan Z, Shi F, Yuan H, Jiang H, Wu D, Sui H, Zhang C, Shen D. Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2606-2614. [PMID: 32386147 DOI: 10.1109/tmi.2020.2992546] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.
Collapse
|
24
|
Huang F, Tan EL, Yang P, Huang S, Ou-Yang L, Cao J, Wang T, Lei B. Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation. Med Image Anal 2020; 63:101662. [PMID: 32442865 DOI: 10.1016/j.media.2020.101662] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 01/13/2020] [Accepted: 01/31/2020] [Indexed: 11/25/2022]
Abstract
As a kind of neurodevelopmental disease, autism spectrum disorder (ASD) can cause severe social, communication, interaction, and behavioral challenges. To date, many imaging-based machine learning techniques have been proposed to address ASD diagnosis issues. However, most of these techniques are restricted to a single template or dataset from one imaging center. In this paper, we propose a novel multi-template multi-center ensemble classification scheme for automatic ASD diagnosis. Specifically, based on different pre-defined templates, we construct multiple functional connectivity (FC) brain networks for each subject based on our proposed Pearson's correlation-based sparse low-rank representation. After extracting features from these FC networks, informative features to learn optimal similarity matrix are then selected by our self-weighted adaptive structure learning (SASL) model. For each template, the SASL method automatically assigns an optimal weight learned from the structural information without additional weights and parameters. Finally, an ensemble strategy based on the multi- template multi-center representations is applied to derive the final diagnosis results. Extensive experiments are conducted on the publicly available Autism Brain Imaging Data Exchange (ABIDE) database to demonstrate the efficacy of our proposed method. Experimental results verify that our proposed method boosts ASD diagnosis performance and outperforms state-of-the-art methods.
Collapse
Affiliation(s)
- Fanglin Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
| | - Ee-Leng Tan
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
| | - Peng Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
| | - Shan Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Information Engineering, Shenzhen University, Shenzhen 518060, China
| | - Jiuwen Cao
- Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang 310010, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China.
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China.
| |
Collapse
|
25
|
Wang L, Liu Y, Zeng X, Cheng H, Wang Z, Wang Q. Region-of-Interest based sparse feature learning method for Alzheimer's disease identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105290. [PMID: 31927305 DOI: 10.1016/j.cmpb.2019.105290] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 12/17/2019] [Accepted: 12/19/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE In recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer's disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data. METHODS A new subspace-based sparse feature learning method is proposed, which builds a union-of-subspace representation model to realize feature extraction and disease identification. Specifically, the proposed method estimates feature dimensions reasonably, at the same time, it protects local features for the specified ROIs of the brain, and realizes image-based feature extraction and classification automatically instead of computing the volume of GM or cortical thickness preliminarily. RESULTS Experimental results illustrate the effectiveness and robustness of the proposed method on feature extraction and classification, which are based on the sampled clinical dataset from Peking University Third Hospital of China and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The extracted image-based feature parameters describe the atrophy of ROIs of the brain well as clinical parameters but show better performance in AD identification than clinical parameters. Based on them, the important ROIs for AD identification can be identified even for correlated variables. CONCLUSION The extracted features and the proposed identification parameters show high correlation with the volume of GM and the clinical mini-mental state examination (MMSE) score respectively. The proposed method will be useful in denoting the changes of cerebral pathology and cognitive function in AD patients.
Collapse
Affiliation(s)
- Ling Wang
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yan Liu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049 China.
| | - Xiangzhu Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, 100191 China.
| | - Hong Cheng
- Center for Robotics, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Zheng Wang
- Department of Radiology, Peking University Third Hospital, Beijing, 100191 China
| | - Qiang Wang
- Beijing Union University, Beijing, 100101 China
| |
Collapse
|
26
|
Wang M, Zhang D, Huang J, Yap PT, Shen D, Liu M. Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:644-655. [PMID: 31395542 PMCID: PMC7169995 DOI: 10.1109/tmi.2019.2933160] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early intervention of ASD. While multi-site data increase sample size and statistical power, they suffer from inter-site heterogeneity. To address this issue, we propose a multi-site adaption framework via low-rank representation decomposition (maLRR) for ASD identification based on functional MRI (fMRI). The main idea is to determine a common low-rank representation for data from the multiple sites, aiming to reduce differences in data distributions. Treating one site as a target domain and the remaining sites as source domains, data from these domains are transformed (i.e., adapted) to a common space using low-rank representation. To reduce data heterogeneity between the target and source domains, data from the source domains are linearly represented in the common space by those from the target domain. We evaluated the proposed method on both synthetic and real multi-site fMRI data for ASD identification. The results suggest that our method yields superior performance over several state-of-the-art domain adaptation methods.
Collapse
Affiliation(s)
- Mingliang Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China
| | - Jiashuang Huang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA
| |
Collapse
|
27
|
Forouzannezhad P, Abbaspour A, Li C, Fang C, Williams U, Cabrerizo M, Barreto A, Andrian J, Rishe N, Curiel RE, Loewenstein D, Duara R, Adjouadi M. A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. J Neurosci Methods 2020; 333:108544. [PMID: 31838182 PMCID: PMC11163390 DOI: 10.1016/j.jneumeth.2019.108544] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 11/26/2019] [Accepted: 12/05/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression. NEW METHOD We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation. This integrated method serves to define multivariate normal distributions in order to classify different stages of AD, with the focus placed on detecting EMCI subjects in the most challenging classification of CN vs. EMCI. RESULTS Using 896 participants classified into the four categories of CN, EMCI, late mild cognitive impairment (LMCI) and AD, the results show that the EMCI group can be delineated from the CN group with a relatively high accuracy of 78.8% and sensitivity of 81.3%. COMPARISON WITH EXISTING METHOD(S) The feature selection model and classifier are compared with some other prominent algorithms. Although higher accuracy has been achieved using the Gaussian process (GP) model (78.8%) over the SVM classifier (75.6%) for CN vs. EMCI classification, with 0.05 being the cutoff for significance, and based on student's t-test, it was determined that the differences for accuracy, sensitivity, specificity between the GP method and support vector machine (SVM) are not statistically significant. CONCLUSION Addressing the challenging classification of CN vs. EMCI provides useful information to help clinicians and researchers determine essential measures that can help in the early detection of AD.
Collapse
Affiliation(s)
- Parisa Forouzannezhad
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Alireza Abbaspour
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Chunfei Li
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Chen Fang
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Ulyana Williams
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Armando Barreto
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Jean Andrian
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Naphtali Rishe
- School of Computing and Information Sciences, Florida International University, Miami, FL, USA
| | - Rosie E Curiel
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
| | - David Loewenstein
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Ranjan Duara
- 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA; Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA; 1Florida Alzheimer's Disease Research Center (ADRC), University of Florida, Gainesville, FL, USA
| |
Collapse
|
28
|
Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med Image Anal 2020; 61:101632. [PMID: 32028212 DOI: 10.1016/j.media.2019.101632] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/17/2019] [Accepted: 12/20/2019] [Indexed: 12/20/2022]
Abstract
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 to appear after the onset of neuro-degeneration. This paper proposes an adaptive feature learning framework using multiple templates for early diagnosis. A multi-classification scheme is developed based on multiple brain parcellation atlases with various regions of interest. Different sets of features are extracted and then fused, and a feature selection is applied with an adaptively chosen sparse degree. In addition, both linear discriminative analysis and locally preserving projections are integrated to construct a least square regression model. Finally, we propose a feature space to predict the severity of the disease by the guidance of clinical scores. Our proposed method is validated on both Alzheimer's disease neuroimaging initiative and Parkinson's progression markers initiative databases. Extensive experimental results suggest that the proposed method outperforms the state-of-the-art methods, such as the multi-modal multi-task learning or joint sparse learning. Our method demonstrates that accurate feature learning facilitates the identification of the highly relevant brain regions with significant contribution in the prediction of disease progression. This may pave the way for further medical analysis and diagnosis in practical applications.
Collapse
|
29
|
Choi BK, Madusanka N, Choi HK, So JH, Kim CH, Park HG, Bhattacharjee S, Prakash D. Convolutional Neural Network-based MR Image Analysis for Alzheimer’s Disease Classification. Curr Med Imaging 2020; 16:27-35. [DOI: 10.2174/1573405615666191021123854] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/11/2019] [Accepted: 10/12/2019] [Indexed: 01/28/2023]
Abstract
Background:
In this study, we used a convolutional neural network (CNN) to classify
Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects
based on images of the hippocampus region extracted from magnetic resonance (MR) images of
the brain.
Materials and Methods:
The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging
Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR
images were matched to the International Consortium for Brain Mapping template (ICBM) using
3D-Slicer software. Using prior knowledge and anatomical annotation label information,
the hippocampal region was automatically extracted from the brain MR images.
Results:
The area of the hippocampus in each image was preprocessed using local entropy minimization
with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method.
To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI,
and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for
AD/MCI, and 78.1% for MCI/NC.
Conclusion:
The results of this study were compared to those of previous studies, and summarized
and analyzed to facilitate more flexible analyses based on additional experiments. The classification
accuracy obtained by the proposed method is highly accurate. These findings suggest
that this approach is efficient and may be a promising strategy to obtain good AD, MCI and
NC classification performance using small patch images of hippocampus instead of whole slide
images.
Collapse
Affiliation(s)
- Boo-Kyeong Choi
- Department of Digital Anti-Aging Healthcare, u-AHRC, Inje University, Gimhae, Korea
| | - Nuwan Madusanka
- Department of Computer Engineering, u-AHRC, Inje University, Gimhae, Korea
| | - Heung-Kook Choi
- Department of Computer Engineering, u-AHRC, Inje University, Gimhae, Korea
| | - Jae-Hong So
- Department of Digital Anti-Aging Healthcare, u-AHRC, Inje University, Gimhae, Korea
| | - Cho-Hee Kim
- Department of Digital Anti-Aging Healthcare, u-AHRC, Inje University, Gimhae, Korea
| | - Hyeon-Gyun Park
- Department of Computer Engineering, u-AHRC, Inje University, Gimhae, Korea
| | | | - Deekshitha Prakash
- Department of Computer Engineering, u-AHRC, Inje University, Gimhae, Korea
| |
Collapse
|
30
|
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks. J Med Syst 2019; 44:37. [PMID: 31853655 DOI: 10.1007/s10916-019-1475-2] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/11/2019] [Indexed: 01/29/2023]
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.
Collapse
|
31
|
Punjabi A, Martersteck A, Wang Y, Parrish TB, Katsaggelos AK. Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks. PLoS One 2019; 14:e0225759. [PMID: 31805160 PMCID: PMC6894831 DOI: 10.1371/journal.pone.0225759] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 11/12/2019] [Indexed: 12/28/2022] Open
Abstract
Automated methods for Alzheimer’s disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer’s dementia classification using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.
Collapse
Affiliation(s)
- Arjun Punjabi
- Department of Electrical Engineering and Computer Science/McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
- * E-mail:
| | - Adam Martersteck
- Department of Radiology/Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
- Mesulam Cognitive Neurology and Alzheimer’s Disease Center, Northwestern University, Chicago, Illinois, United States of America
| | - Yanran Wang
- Department of Electrical Engineering and Computer Science/McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Todd B. Parrish
- Department of Radiology/Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Aggelos K. Katsaggelos
- Department of Electrical Engineering and Computer Science/McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
| | | |
Collapse
|
32
|
Bai Y, Pascal Z, Hu W, Calhoun VD, Wang YP. Biomarker Identification Through Integrating fMRI and Epigenetics. IEEE Trans Biomed Eng 2019; 67:1186-1196. [PMID: 31395533 DOI: 10.1109/tbme.2019.2932895] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor to fill the gap by combining epigenetics (e.g., DNA methylation) with imaging data (e.g., fMRI) to identify biomarkers for schizophrenia (SZ). METHODS We propose to combine linear regression with canonical correlation analysis (CCA) in a relaxed yet coupled manner to extract discriminative features for SZ that are co-expressed in the fMRI and DNA methylation data. RESULT After validation through simulations, we applied our method to real imaging epigenetics data of 184 subjects from the Mental Illness and Neuroscience Discovery Clinical Imaging Consortium. After significance test, we identified 14 brain regions and 44 cytosine-phosphate-guanine(CpG) sites. Average classification accuracy is [Formula: see text]. By linking the CpG sites to genes, we identified pathways Guanosine ribonucleotides de novo biosynthesis and Guanosine nucleotides de novo biosynthesis, and a GO term Perikaryon. CONCLUSION This imaging epigenetics study has identified both brain regions and genes that are associated with neuron development and memory processing. These biomarkers contribute to a good understanding of the mechanism underlying SZ but are overlooked by previous imaging genetics studies. SIGNIFICANCE Our study sheds light on the understanding and diagnosis of SZ with a imaging epigenetics approach, which is demonstrated to be effective in extracting novel biomarkers associated with SZ.
Collapse
|
33
|
Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.023] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
34
|
Classification of amyloid PET images using novel features for early diagnosis of Alzheimer's disease and mild cognitive impairment conversion. Nucl Med Commun 2019; 40:242-248. [PMID: 30507747 DOI: 10.1097/mnm.0000000000000953] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND New PET tracers could have a substantial impact on the early diagnosis of Alzheimer's disease (AD), particularly if they are accompanied by optimised image analysis and machine learning methods. Fractal dimension (FD) analysis, a measure of shape complexity, has been proven useful in MRI but its application to fluorine-18 amyloid PET has not yet been demonstrated. Shannon entropy (SE) has also been proposed as a measure of image complexity in DTI imaging, but it is not yet widely used in radiology. MATERIALS AND METHODS In this study, one volumetric FD method and one volumetric SE method were applied to fluorine-18-flutemetamol and fluorine-18-florbetapir 3D amyloid images from 65 and 281 participants, respectively, including healthy volunteers, and patients with probable Alzheimer's disease (pAD) or mild cognitive impairment (MCI). RESULTS The group average FD of white matter surface and SE of white matter volume for healthy volunteers were higher than for pAD patients. Both FD and SE are effective in the identification of MCI patients who progress to pAD during the 2-year follow-up (ground truth). Finally, we developed a support vector machine multimodal classification framework using both PET and MRI features, which showed higher accuracy compared to traditional standard uptake value ratio or using PET alone. The classification accuracy for flutemetamol and florbetapir is 88.9 and 83.3%, respectively, for MCI progression, which is competitive with existing literature. CONCLUSION The results presented in this study demonstrate the potential of FD and SE methods for the analysis of brain PET scans in early AD diagnosis and in the prediction of MCI-AD conversion.
Collapse
|
35
|
Adeli E, Thung KH, An L, Wu G, Shi F, Wang T, Shen D. Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019; 41:515-522. [PMID: 29994560 PMCID: PMC6050136 DOI: 10.1109/tpami.2018.2794470] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson's and Alzheimer's diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.
Collapse
Affiliation(s)
- Ehsan Adeli
- Stanford University, Stanford, CA 94305, USA. Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Kim-Han Thung
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Le An
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Feng Shi
- Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center and the Alzheimer’s Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Dinggang Shen
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, 27599, USA. Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| |
Collapse
|
36
|
Cui R, Liu M. RNN-based longitudinal analysis for diagnosis of Alzheimer's disease. Comput Med Imaging Graph 2019; 73:1-10. [PMID: 30763637 DOI: 10.1016/j.compmedimag.2019.01.005] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 09/30/2018] [Accepted: 01/21/2019] [Indexed: 12/19/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and other mental functions. Magnetic resonance images (MRI) have been widely used as an important imaging modality of brain for AD diagnosis and monitoring the disease progression. The longitudinal analysis of sequential MRIs is important to model and measure the progression of the disease along the time axis for more accurate diagnosis. Most existing methods extracted the features capturing the morphological abnormalities of brain and their longitudinal changes using MRIs and then designed a classifier to discriminate different groups. However, these methods have several limitations. First, since the feature extraction and classifier model are independent, the extracted features may not capture the full characteristics of brain abnormalities related to AD. Second, longitudinal MR images may be missing at some time points for some subjects, which results in difficulties for extraction of consistent features for longitudinal analysis. In this paper, we present a classification framework based on combination of convolutional and recurrent neural networks for longitudinal analysis of structural MR images in AD diagnosis. First, Convolutional Neural Networks (CNN) is constructed to learn the spatial features of MR images for the classification task. After that, recurrent Neural Networks (RNN) with cascaded three bidirectional gated recurrent units (BGRU) layers is constructed on the outputs of CNN at multiple time points for extracting the longitudinal features for AD classification. Instead of independently performing feature extraction and classifier training, the proposed method jointly learns the spatial and longitudinal features and disease classifier, which can achieve optimal performance. In addition, the proposed method can model the longitudinal analysis using RNN from the imaging data at various time points. Our method is evaluated with the longitudinal T1-weighted MR images of 830 participants including 198 AD, 403 mild cognitive impairment (MCI), and 229 normal controls (NC) subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves classification accuracy of 91.33% for AD vs. NC and 71.71% for pMCI vs. sMCI, demonstrating the promising performance for longitudinal MR image analysis.
Collapse
Affiliation(s)
- Ruoxuan Cui
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, 200240 China
| | - Manhua Liu
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, 200240 China.; Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai Jiao Tong University, China.
| | | |
Collapse
|
37
|
Hazarika A, Barthakur M, Dutta L, Bhuyan M. F-SVD based algorithm for variability and stability measurement of bio-signals, feature extraction and fusion for pattern recognition. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
38
|
Zhou T, Thung KH, Liu M, Shen D. Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model. IEEE Trans Biomed Eng 2019; 66:165-175. [PMID: 29993426 PMCID: PMC6342004 DOI: 10.1109/tbme.2018.2824725] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to identify the associations between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants [i.e., single nucleotide polymorphism (SNP)] in Alzheimer's disease (AD). The main challenges of this study include the data heterogeneity, complex phenotype-genotype associations, high-dimensional data (e.g., thousands of SNPs), and the existence of phenotype outliers. Previous BW-GWA studies, while addressing some of these challenges, did not consider the diagnostic label information in their formulations, thus limiting their clinical applicability. To address these issues, we present a novel joint projection and sparse regression model to discover the associations between the phenotypes and genotypes. Specifically, to alleviate the negative influence of data heterogeneity, we first map the genotypes into an intermediate imaging-phenotype-like space. Then, to better reveal the complex phenotype-genotype associations, we project both the mapped genotypes and the original imaging phenotypes into a diagnostic-label-guided joint feature space, where the intraclass projected points are constrained to be close to each other. In addition, we use l2,1-norm minimization on both the regression loss function and the transformation coefficient matrices, to reduce the effect of phenotype outliers and also to encourage sparse feature selections of both the genotypes and phenotypes. We evaluate our method using AD neuroimaging initiative dataset, and the results show that our proposed method outperforms several state-of-the-art methods in term of the average root-mean-square error of genome-to-phenotype predictions. Besides, the associated SNPs and brain regions identified in this study have also been shown in the previous AD-related studies, thus verifying the effectiveness and potential of our proposed method in AD pathogenesis study.
Collapse
Affiliation(s)
- Tao Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ()
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ()
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ()
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599 USA, and also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea ()
| |
Collapse
|
39
|
Lei B, Yang P, Zhuo Y, Zhou F, Ni D, Chen S, Xiao X, Wang T. Neuroimaging Retrieval via Adaptive Ensemble Manifold Learning for Brain Disease Diagnosis. IEEE J Biomed Health Inform 2018; 23:1661-1673. [PMID: 30281500 DOI: 10.1109/jbhi.2018.2872581] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative and non-curable disease, with serious cognitive impairment, such as dementia. Clinically, it is critical to study the disease with multi-source data in order to capture a global picture of it. In this respect, an adaptive ensemble manifold learning (AEML) algorithm is proposed to retrieve multi-source neuroimaging data. Specifically, an objective function based on manifold learning is formulated to impose geometrical constraints by similarity learning. The complementary characteristics of various sources of brain disease data for disorder discovery are investigated by tuning weights from ensemble learning. In addition, a generalized norm is explicitly explored for adaptive sparseness degree control. The proposed AEML algorithm is evaluated by the public AD neuroimaging initiative database. Results obtained from the extensive experiments demonstrate that our algorithm outperforms the traditional methods.
Collapse
|
40
|
Liu M, Zhang J, Nie D, Yap PT, Shen D. Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis. IEEE J Biomed Health Inform 2018; 22:1476-1485. [PMID: 29994175 PMCID: PMC6238951 DOI: 10.1109/jbhi.2018.2791863] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer's disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer's disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer's disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.
Collapse
|
41
|
Hazarika A, Sarmah A, Borah R, Boro M, Dutta L, Kalita P, Choudhury BK. Discriminant feature level fusion based learning for automatic staging of EEG signals. Healthc Technol Lett 2018. [DOI: 10.1049/htl.2018.5024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- Anil Hazarika
- Electronics and Communication Engineering, Tezpur University, Tezpur Napaam Assam India
| | - Arup Sarmah
- Department of Computer Science Gauhati University, Ghy Guwahati Assam India
| | - Rupam Borah
- Department of Chemistry IIT Roorkee Roorkee Uttarakhand India
| | - Meenakshi Boro
- Electronics and Communication Engineering, Tezpur University, Tezpur Napaam Assam India
| | - Lachit Dutta
- Electronics and Communication Engineering, Tezpur University, Tezpur Napaam Assam India
| | - Pankaj Kalita
- Department of Biophysics Pub Kamrup College Kamrup Assam India
| | | |
Collapse
|
42
|
Pellegrini E, Ballerini L, Hernandez MDCV, Chappell FM, González-Castro V, Anblagan D, Danso S, Muñoz-Maniega S, Job D, Pernet C, Mair G, MacGillivray TJ, Trucco E, Wardlaw JM. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2018; 10:519-535. [PMID: 30364671 PMCID: PMC6197752 DOI: 10.1016/j.dadm.2018.07.004] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method. DISCUSSION Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.
Collapse
Affiliation(s)
- Enrico Pellegrini
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Lucia Ballerini
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Maria del C. Valdes Hernandez
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Francesca M. Chappell
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Victor González-Castro
- Department of Electrical, Systems and Automatics Engineering, Universidad de León, León, Spain
| | - Devasuda Anblagan
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Samuel Danso
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Susana Muñoz-Maniega
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Dominic Job
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Cyril Pernet
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Grant Mair
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Tom J. MacGillivray
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
- VAMPIRE project, University of Edinburgh, Scotland, UK
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Joanna M. Wardlaw
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
- UK Dementia Institute, University of Edinburgh, Scotland, UK
| |
Collapse
|
43
|
Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N, Nahavandi S. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:119-127. [PMID: 29903478 DOI: 10.1016/j.cmpb.2018.05.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 04/24/2018] [Accepted: 05/03/2018] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.
Collapse
Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
| | - Mohammad Javad Hosseini
- Department of Computer Science and Engineering, University of Washington, Seattle, United States
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia.
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences,Isfahan,Iran & Faculty of Medicine, SPPH, University of British Columbia, Vancouver,BC, Canada
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
| |
Collapse
|
44
|
Kim H, Han H. Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease. Bioinformation 2018; 14:167-180. [PMID: 29983487 PMCID: PMC6016757 DOI: 10.6026/97320630014167] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 04/29/2018] [Accepted: 04/30/2018] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.
Collapse
Affiliation(s)
- Hyunjo Kim
- Department of Medical Informatics, Ajou Medical University Hospital, Suwon, Kyeounggido province, South Korea
| | - Hyunwook Han
- Department of Informatics, School of Medicine, CHA University, Seongnam, South Korea
- Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, South Korea
| |
Collapse
|
45
|
Rasero J, Amoroso N, La Rocca M, Tangaro S, Bellotti R, Stramaglia S. Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease. PLoS One 2017; 12:e0187281. [PMID: 29135998 PMCID: PMC5685585 DOI: 10.1371/journal.pone.0187281] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 10/17/2017] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.
Collapse
Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Marianna La Rocca
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | | | - Roberto Bellotti
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
- * E-mail:
| | | |
Collapse
|
46
|
Xi X, Xu H, Shi H, Zhang C, Ding HY, Zhang G, Tang Y, Yin Y. Robust texture analysis of multi-modal images using Local Structure Preserving Ranklet and multi-task learning for breast tumor diagnosis. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.06.082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
|
47
|
|
48
|
Kankanhalli M. Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:1781-1794. [PMID: 27429453 DOI: 10.1109/tcyb.2016.2582918] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Human action recognition is an active research area in both computer vision and machine learning communities. In the past decades, the machine learning problem has evolved from conventional single-view learning problem, to cross-view learning, cross-domain learning and multitask learning, where a large number of algorithms have been proposed in the literature. Despite having large number of action recognition datasets, most of them are designed for a subset of the four learning problems, where the comparisons between algorithms can further limited by variances within datasets, experimental configurations, and other factors. To the best of our knowledge, there exists no dataset that allows concurrent analysis on the four learning problems. In this paper, we introduce a novel multimodal and multiview and interactive (M2I) dataset, which is designed for the evaluation of human action recognition methods under all four scenarios. This dataset consists of 1760 action samples from 22 action categories, including nine person-person interactive actions and 13 person-object interactive actions. We systematically benchmark state-of-the-art approaches on M2I dataset on all four learning problems. Overall, we evaluated 13 approaches with nine popular feature and descriptor combinations. Our comprehensive analysis demonstrates that M2I dataset is challenging due to significant intraclass and view variations, and multiple similar action categories, as well as provides solid foundation for the evaluation of existing state-of-the-art algorithms.
Collapse
|
49
|
Cheng B, Liu M, Shen D, Li Z, Zhang D. Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease. Neuroinformatics 2017; 15:115-132. [PMID: 27928657 PMCID: PMC5444948 DOI: 10.1007/s12021-016-9318-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multi-domain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.
Collapse
Affiliation(s)
- Bo Cheng
- Key Laboratory of Advanced Network and Intellectual Technology, Chongqing Three Gorges University, Chongqing, 404120, China
| | - Mingxia Liu
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China
| | - Daoqiang Zhang
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China.
| |
Collapse
|
50
|
An L, Adeli E, Liu M, Zhang J, Lee SW, Shen D. A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis. Sci Rep 2017; 7:45269. [PMID: 28358032 PMCID: PMC5372170 DOI: 10.1038/srep45269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 02/23/2017] [Indexed: 11/09/2022] Open
Abstract
Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer's disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.
Collapse
Affiliation(s)
- Le An
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC 27599, USA
| | - Ehsan Adeli
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC 27599, USA
| | - Jun Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC 27599, USA
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| |
Collapse
|