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Chen J, Liu T, Shi H. End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion. PLoS One 2024; 19:e0305079. [PMID: 38870175 PMCID: PMC11175492 DOI: 10.1371/journal.pone.0305079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
The function and structure of brain networks (BN) may undergo changes in patients with end-stage renal disease (ESRD), particularly in those accompanied by mild cognitive impairment (ESRDaMCI). Many existing methods for fusing BN focus on extracting interaction features between pairs of network nodes from each mode and combining them. This approach overlooks the correlation between different modal features during feature extraction and the potentially valuable information that may exist between more than two brain regions. To address this issue, we propose a model using a multi-head self-attention mechanism to fuse brain functional networks, white matter structural networks, and gray matter structural networks, which results in the construction of brain fusion networks (FBN). Initially, three networks are constructed: the brain function network, the white matter structure network, and the individual-based gray matter structure network. The multi-head self-attention mechanism is then applied to fuse the three types of networks, generating attention weights that are transformed into an optimized model. The optimized model introduces hypergraph popular regular term and L1 norm regular term, leading to the formation of FBN. Finally, FBN is employed in the diagnosis and prediction of ESRDaMCI to evaluate its classification performance and investigate the correlation between discriminative brain regions and cognitive dysfunction. Experimental results demonstrate that the optimal classification accuracy achieved is 92.80%, which is at least 3.63% higher than the accuracy attained using other methods. This outcome confirms the effectiveness of our proposed method. Additionally, the identification of brain regions significantly associated with scores on the Montreal cognitive assessment scale may shed light on the underlying pathogenesis of ESRDaMCI.
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Affiliation(s)
- Jie Chen
- Department of Security, Huaide College of Changzhou University, Jingjiang, Jiangsu, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
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Song Z, Li H, Zhang Y, Zhu C, Jiang M, Song L, Wang Y, Ouyang M, Hu F, Zheng Q. s 2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01178-3. [PMID: 38869733 DOI: 10.1007/s10334-024-01178-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVE To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.
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Affiliation(s)
- Zhiwei Song
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Honglun Li
- Department of Radiology, Yantai Yuhuangding Hospital Affiliated with Qingdao University Medical College, Yantai, 264099, China
| | - Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000, China
| | - Yi Wang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
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Lu Y, Liu T, Sheng Q, Zhang Y, Shi H, Jiao Z. Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:3838-3859. [PMID: 38549310 DOI: 10.3934/mbe.2024171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.
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Affiliation(s)
- Yu Lu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Quan Sheng
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Yutao Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
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Song C, Liu T, Shi H, Jiao Z. HCTMFS: A multi-modal feature selection framework with higher-order correlated topological manifold for ESRDaMCI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107905. [PMID: 37931582 DOI: 10.1016/j.cmpb.2023.107905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/21/2023] [Accepted: 10/27/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The diagnosis of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) mainly relies on objective cognitive assessment, clinical observation, and neuro-psychological evaluation, while only adopting clinical tools often limits the diagnosis accuracy. METHODS We proposed a multi-modal feature selection framework with higher-order correlated topological manifold (HCTMFS) to classify ESRDaMCI patients and identify the discriminative brain regions. It constructed brain structural and functional networks with diffuse kurtosis imaging (DKI) and functional magnetic resonance imaging (fMRI) data, and extracted node efficiency and clustering coefficient from the brain networks to construct multi-modal feature matrices. The topological relationship matrices were constructed to measure the lower-order topological correlation between features. Then the consensus matrices were learned to approximate the topological relationship matrices at different confidence levels and eliminate the noise influence of individual matrices. RESULTS The higher-order topological correlation between features was explored by the Laplacian matrix of the hypergraph, which was calculated through the consensus matrix. The new framework achieved an accuracy rate of 93.56 % for classifying ESRDaMCI patients, and outperformed the existing state-of-the-art methods in terms of sensitivity, specificity, and area under the curve. CONCLUSIONS This study contributes to effectively reflect the functional neural degradation of ESRDaMCI and provide a reference for the diagnosis of ESRDaMCI by selecting discriminative brain regions.
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Affiliation(s)
- Chaofan Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
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Song C, Liu T, Wang H, Shi H, Jiao Z. Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14827-14845. [PMID: 37679161 DOI: 10.3934/mbe.2023664] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.
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Affiliation(s)
- Chaofan Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Huan Wang
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
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Xi Z, Liu T, Shi H, Jiao Z. Hypergraph representation of multimodal brain networks for patients with end-stage renal disease associated with mild cognitive impairment. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1882-1902. [PMID: 36899513 DOI: 10.3934/mbe.2023086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The structure and function of brain networks (BN) may be altered in patients with end-stage renal disease (ESRD). However, there are relatively few attentions on ESRD associated with mild cognitive impairment (ESRDaMCI). Most studies focus on the pairwise relationships between brain regions, without taking into account the complementary information of functional connectivity (FC) and structural connectivity (SC). To address the problem, a hypergraph representation method is proposed to construct a multimodal BN for ESRDaMCI. First, the activity of nodes is determined by connection features extracted from functional magnetic resonance imaging (fMRI) (i.e., FC), and the presence of edges is determined by physical connections of nerve fibers extracted from diffusion kurtosis imaging (DKI) (i.e., SC). Then, the connection features are generated through bilinear pooling and transformed into an optimization model. Next, a hypergraph is constructed according to the generated node representation and connection features, and the node degree and edge degree of the hypergraph are calculated to obtain the hypergraph manifold regularization (HMR) term. The HMR and L1 norm regularization terms are introduced into the optimization model to achieve the final hypergraph representation of multimodal BN (HRMBN). Experimental results show that the classification performance of HRMBN is significantly better than that of several state-of-the-art multimodal BN construction methods. Its best classification accuracy is 91.0891%, at least 4.3452% higher than that of other methods, verifying the effectiveness of our method. The HRMBN not only achieves better results in ESRDaMCI classification, but also identifies the discriminative brain regions of ESRDaMCI, which provides a reference for the auxiliary diagnosis of ESRD.
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Affiliation(s)
- Zhengtao Xi
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
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Wu X, Gao S, Sun J, Zhang Y, Wang S. Classification of Alzheimer's Disease Based on Weakly Supervised Learning and Attention Mechanism. Brain Sci 2022; 12:brainsci12121601. [PMID: 36552061 PMCID: PMC9775321 DOI: 10.3390/brainsci12121601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/15/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022] Open
Abstract
The brain lesions images of Alzheimer's disease (AD) patients are slightly different from the Magnetic Resonance Imaging of normal people, and the classification effect of general image recognition technology is not ideal. Alzheimer's datasets are small, making it difficult to train large-scale neural networks. In this paper, we propose a network model (WS-AMN) that fuses weak supervision and an attention mechanism. The weakly supervised data augmentation network is used as the basic model, the attention map generated by weakly supervised learning is used to guide the data augmentation, and an attention module with channel domain and spatial domain is embedded in the residual network to focus on the distinctive channels and spaces of images respectively. The location information enhances the corresponding features of related features and suppresses the influence of irrelevant features.The results show that the F1-score is 99.63%, the accuracy is 99.61%. Our model provides a high-performance solution for accurate classification of AD.
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Affiliation(s)
- Xiaosheng Wu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
| | - Shuangshuang Gao
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
- Correspondence: (Y.Z.); (S.W.)
| | - Shuihua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
- Correspondence: (Y.Z.); (S.W.)
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Meng X, Liu J, Fan X, Bian C, Wei Q, Wang Z, Liu W, Jiao Z. Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease. Front Aging Neurosci 2022; 14:911220. [PMID: 35651528 PMCID: PMC9149574 DOI: 10.3389/fnagi.2022.911220] [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: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Junlong Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Xiang Fan
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Chenyuan Bian
- Shandong Provincial Key Laboratory of Digital Medicine and Computer-Assisted Surgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qingpeng Wei
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Ziwei Wang
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
- *Correspondence: Wenjie Liu,
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- Zhuqing Jiao,
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Meng X, Wu Y, Liu W, Wang Y, Xu Z, Jiao Z. Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine. Front Neuroinform 2022; 16:856295. [PMID: 35418845 PMCID: PMC8995748 DOI: 10.3389/fninf.2022.856295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Yue Wu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, China
| | - Zhe Xu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- *Correspondence: Zhuqing Jiao,
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10
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Ji Y, Zhang Y, Shi H, Jiao Z, Wang SH, Wang C. Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification. Front Neurosci 2021; 15:669345. [PMID: 33867931 PMCID: PMC8047143 DOI: 10.3389/fnins.2021.669345] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/11/2021] [Indexed: 12/15/2022] Open
Abstract
Brain functional networks (BFNs) constructed via manifold regularization (MR) have emerged as a powerful tool in finding new biomarkers for brain disease diagnosis. However, they only describe the pair-wise relationship between two brain regions, and cannot describe the functional interaction between multiple brain regions, or the high-order relationship, well. To solve this issue, we propose a method to construct dynamic BFNs (DBFNs) via hyper-graph MR (HMR) and employ it to classify mild cognitive impairment (MCI) subjects. First, we construct DBFNs via Pearson's correlation (PC) method and remodel the PC method as an optimization model. Then, we use k-nearest neighbor (KNN) algorithm to construct the hyper-graph and obtain the hyper-graph manifold regularizer based on the hyper-graph. We introduce the hyper-graph manifold regularizer and the L1-norm regularizer into the PC-based optimization model to optimize DBFNs and obtain the final sparse DBFNs (SDBFNs). Finally, we conduct classification experiments to classify MCI subjects from normal subjects to verify the effectiveness of our method. Experimental results show that the proposed method achieves better classification performance compared with other state-of-the-art methods, and the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under the curve (AUC) reach 82.4946 ± 0.2827%, 77.2473 ± 0.5747%, 87.7419 ± 0.2286%, and 0.9021 ± 0.0007, respectively. This method expands the MR method and DBFNs with more biological significance. It can effectively improve the classification performance of DBFNs for MCI, and has certain reference value for the research and auxiliary diagnosis of Alzheimer's disease (AD).
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Affiliation(s)
- Yixin Ji
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Yutao Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Shui-Hua Wang
- School of Informatics, University of Leicester, Leicester, United Kingdom
| | - Chuang Wang
- School of Medicine, Ningbo University, Ningbo, China
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11
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Jiao Z, Ji Y, Zhang J, Shi H, Wang C. Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification. Front Cell Dev Biol 2021; 8:610569. [PMID: 33505965 PMCID: PMC7829545 DOI: 10.3389/fcell.2020.610569] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 11/12/2020] [Indexed: 12/25/2022] Open
Abstract
Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the t-test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer’s disease (AD).
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Affiliation(s)
- Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China.,School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Yixin Ji
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Jiahao Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People's Hospital Affiliated to Nanjing Medical University, Changzhou, China
| | - Chuang Wang
- School of Medicine, Ningbo University, Ningbo, China
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Elshatoury H, Avots E, Anbarjafari G. Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine. J Alzheimers Dis 2019; 72:515-524. [PMID: 31609690 DOI: 10.3233/jad-190704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervised learning algorithms were used to train classifiers in which the accuracies are being compared. The database used is from The Alzheimer's Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting.
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Affiliation(s)
- Heba Elshatoury
- iCV Research Lab, Institute of Technology, University of Tartu, Tartu, Estonia
| | - Egils Avots
- iCV Research Lab, Institute of Technology, University of Tartu, Tartu, Estonia
| | - Gholamreza Anbarjafari
- iCV Research Lab, Institute of Technology, University of Tartu, Tartu, Estonia.,Department of Electrical and Electronic Engineering, Hasan Kalyoncu University, Gaziantep, Turkey
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13
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Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50. Brain Sci 2019; 9:brainsci9090212. [PMID: 31443556 PMCID: PMC6770938 DOI: 10.3390/brainsci9090212] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 12/27/2022] Open
Abstract
Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
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14
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Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz JM. Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization. J Alzheimers Dis 2019; 65:855-869. [PMID: 28731432 DOI: 10.3233/jad-170069] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system. OBJECTIVE In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images. METHODS First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier. RESULTS Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed. CONCLUSION In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.
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Affiliation(s)
- Yudong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, P. R. China.,School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, P. R. China
| | - Shuihua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, P. R. China.,School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, P. R. China
| | - Yuxiu Sui
- Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing, P. R.China
| | - Ming Yang
- Department of Radiology, Children's Hospital of Nanjing Medical University, Nanjing, P. R. China
| | - Bin Liu
- Department of Radiology, Zhong-Da Hospital of Southeast University, Nanjing, P. R. China
| | - Hong Cheng
- Department of Neurology, First Affiliated Hospital of Nanjing Medical University, Nanjing, P. R. China
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, P. R. China
| | - Wenjuan Jia
- School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, P. R. China
| | - Preetha Phillips
- West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
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15
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Multi-class Alzheimer's disease classification using image and clinical features. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.019] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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16
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Glozman T, Solomon J, Pestilli F, Guibas L. Shape-Attributes of Brain Structures as Biomarkers for Alzheimer's Disease. J Alzheimers Dis 2018; 56:287-295. [PMID: 27911322 PMCID: PMC5240557 DOI: 10.3233/jad-160900] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods.
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Affiliation(s)
- Tanya Glozman
- Department of Electrical Engineering, Stanford, CA, USA
| | - Justin Solomon
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Franco Pestilli
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
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17
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Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning. Sci Rep 2018; 8:557. [PMID: 29323205 PMCID: PMC5765025 DOI: 10.1038/s41598-017-18931-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 12/20/2017] [Indexed: 11/09/2022] Open
Abstract
By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher’s iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.
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18
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Zhang XY, Yang ZL, Lu GM, Yang GF, Zhang LJ. PET/MR Imaging: New Frontier in Alzheimer's Disease and Other Dementias. Front Mol Neurosci 2017; 10:343. [PMID: 29163024 PMCID: PMC5672108 DOI: 10.3389/fnmol.2017.00343] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 10/10/2017] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia; a progressive neurodegenerative disease that currently lacks an effective treatment option. Early and accurate diagnosis, in addition to quick elimination of differential diagnosis, allows us to provide timely treatments that delay the progression of AD. Imaging plays an important role for the early diagnosis of AD. The newly emerging PET/MR imaging strategies integrate the advantages of PET and MR to diagnose and monitor AD. This review introduces the development of PET/MR imaging systems, technical considerations of PET/MR imaging, special considerations of PET/MR in AD, and the system's potential clinical applications and future perspectives in AD.
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Affiliation(s)
- Xin Y Zhang
- Medical Imaging Center, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhen L Yang
- Medical Imaging Center, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guang M Lu
- Medical Imaging Center, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Gui F Yang
- Medical Imaging Center, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Long J Zhang
- Medical Imaging Center, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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19
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Ferreira LK, Rondina JM, Kubo R, Ono CR, Leite CC, Smid J, Bottino C, Nitrini R, Busatto GF, Duran FL, Buchpiguel CA. Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals. ACTA ACUST UNITED AC 2017; 40:181-191. [PMID: 28977066 PMCID: PMC6900774 DOI: 10.1590/1516-4446-2016-2083] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 05/08/2017] [Indexed: 12/01/2022]
Abstract
Objective: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer’s disease (AD). Method: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation. Results: The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC) 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities. Conclusion: In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.
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Affiliation(s)
- Luiz K Ferreira
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil
| | - Jane M Rondina
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, United Kingdom
| | - Rodrigo Kubo
- Laboratório de Medicina Nuclear (LIM43), Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Carla R Ono
- Laboratório de Medicina Nuclear (LIM43), Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil.,Serviço de Medicina Nuclear, Hospital do Coração da Associação Sanatório Sírio, São Paulo, SP, Brazil
| | - Claudia C Leite
- Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Jerusa Smid
- Departamento de Neurologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Cassio Bottino
- Departamento de Psiquiatria, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Ricardo Nitrini
- Departamento de Neurologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Geraldo F Busatto
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil.,Departamento de Psiquiatria, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Fabio L Duran
- Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil
| | - Carlos A Buchpiguel
- Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), USP, São Paulo, SP, Brazil.,Laboratório de Medicina Nuclear (LIM43), Departamento de Radiologia e Oncologia, Faculdade de Medicina, USP, São Paulo, SP, Brazil.,Serviço de Medicina Nuclear, Hospital do Coração da Associação Sanatório Sírio, São Paulo, SP, Brazil
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20
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Al-Shaikhli SDS, Yang MY, Rosenhahn B. Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:329-339. [PMID: 28110736 DOI: 10.1016/j.cmpb.2016.09.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 08/07/2016] [Accepted: 09/09/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. METHODS The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. RESULTS The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. CONCLUSIONS In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease.
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Affiliation(s)
- Saif Dawood Salman Al-Shaikhli
- School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; Institut für Informationsverarbeitung, Leibniz Universität Hannover, Appelstr. 9A, 30167 Hannover, Germany.
| | - Michael Ying Yang
- ITC - Faculty of Geo-Information Science and Earth Observation, Department of Earth Observation Science, University of Twente, Netherlands
| | - Bodo Rosenhahn
- Institut für Informationsverarbeitung, Leibniz Universität Hannover, Appelstr. 9A, 30167 Hannover, Germany
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21
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Wang S, Yang M, Du S, Yang J, Liu B, Gorriz JM, Ramírez J, Yuan TF, Zhang Y. Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning. Front Comput Neurosci 2016; 10:106. [PMID: 27807415 PMCID: PMC5069288 DOI: 10.3389/fncom.2016.00106] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 09/28/2016] [Indexed: 12/17/2022] Open
Abstract
HighlightsWe develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging. Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems. The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls.
Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.
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Affiliation(s)
- Shuihua Wang
- School of Electronic Science and Engineering, Nanjing UniversityNanjing, China; School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademyChangsha, China
| | - Ming Yang
- Department of Radiology, Nanjing Children's Hospital, Nanjing Medical UniversityNanjing, China; Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University, Quanzhou Normal UniversityQuanzhou, China
| | - Sidan Du
- School of Electronic Science and Engineering, Nanjing University Nanjing, China
| | - Jiquan Yang
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing, China
| | - Bin Liu
- Department of Radiology, Zhong-Da Hospital of Southeast University Nanjing, China
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain
| | - Ti-Fei Yuan
- School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; State Key Lab of CAD & CG, Zhejiang UniversityHangzhou, China
| | - Yudong Zhang
- School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Key Laboratory of Statistical Information Technology and Data Mining, State Statistics BureauChengdu, China
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Wang S, Chen M, Li Y, Shao Y, Zhang Y, Du S, Wu J. Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine. PeerJ 2016; 4:e2207. [PMID: 27547530 PMCID: PMC4958009 DOI: 10.7717/peerj.2207] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 06/12/2016] [Indexed: 11/20/2022] Open
Abstract
Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm–RTSVM— which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.
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Affiliation(s)
- Shuihua Wang
- School of Electronic Science and Engineering, Nanjing University, Jiangsu, China; School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Mengmeng Chen
- Department of Neurology, Northwestern University School of Medicine, Chicago, USA; State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yang Li
- School of Electronic Science and Engineering, Nanjing University , Jiangsu , China
| | - Ying Shao
- School of Psychology, Nanjing Normal University , Nanjing, Jiangsu , China
| | - Yudong Zhang
- School of Computer Science and Technology, Nanjing Normal University , Nanjing , Jiangsu , China
| | - Sidan Du
- School of Electronic Science and Engineering, Nanjing University , Jiangsu , China
| | - Jane Wu
- School of Electronic Science and Engineering, Nanjing University, Jiangsu, China; Department of Neurology, Northwestern University School of Medicine, Chicago, USA; State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
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Zhang Y, Sun Y, Phillips P, Liu G, Zhou X, Wang S. A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy. J Med Syst 2016; 40:173. [DOI: 10.1007/s10916-016-0525-2] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Accepted: 05/16/2016] [Indexed: 12/23/2022]
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