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Hu Y, Wang J, Zhu H, Li J, Shi J. Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3126-3136. [PMID: 38625767 DOI: 10.1109/tmi.2024.3389747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed by the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer's Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.
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Qi W, Zhu X, He D, Wang B, Cao S, Dong C, Li Y, Chen Y, Wang B, Shi Y, Jiang G, Liu F, Boots LMM, Li J, Lou X, Yao J, Lu X, Kang J. Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis. J Med Internet Res 2024; 26:e57830. [PMID: 39116438 PMCID: PMC11342017 DOI: 10.2196/57830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.
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Affiliation(s)
- Wenhao Qi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaohong Zhu
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Danni He
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Bin Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Shihua Cao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Chaoqun Dong
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yunhua Li
- College of Education, Chengdu College of Arts and Sciences, Sichuan, China
| | - Yanfei Chen
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Bingsheng Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yankai Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Guowei Jiang
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Fang Liu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Lizzy M M Boots
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Jiaqi Li
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiajing Lou
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Jiani Yao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaodong Lu
- Department of Neurology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Junling Kang
- Department of Neurology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Ma H, Wang Y, Hao Z, Yu Y, Jia X, Li M, Chen L. Classification of Alzheimer's disease: application of a transfer learning deep Q-network method. Eur J Neurosci 2024; 59:2118-2127. [PMID: 38282277 DOI: 10.1111/ejn.16261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/25/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q-network (DQN) could effectively distinguish AD patients using local metrics of resting-state functional magnetic resonance imaging (rs-fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO/2. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre-trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (p < 0.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis.
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Affiliation(s)
- Huibin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi, China
| | - Zeqi Hao
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Yang Yu
- Department of Psychiatry, the second affiliated hospital of Zhejiang University school of Medicine, Zhejiang, China
| | - Xize Jia
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Mengting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Lanfen Chen
- School of Medical Imaging, Weifang Medical University, Weifang, China
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Qin T, Wang L, Xu H, Liu C, Shao Y, Li F, Wang Y, Jiang J, Lin H. rTMS concurrent with cognitive training rewires AD brain by enhancing GM-WM functional connectivity: a preliminary study. Cereb Cortex 2024; 34:bhad460. [PMID: 38037857 DOI: 10.1093/cercor/bhad460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) and cognitive training for patients with Alzheimer's disease (AD) can change functional connectivity (FC) within gray matter (GM). However, the role of white matter (WM) and changes of GM-WM FC under these therapies are still unclear. To clarify this problem, we applied 40 Hz rTMS over angular gyrus (AG) concurrent with cognitive training to 15 mild-moderate AD patients and analyzed the resting-state functional magnetic resonance imaging before and after treatment. Through AG-based FC analysis, corona radiata and superior longitudinal fasciculus (SLF) were identified as activated WM tracts. Compared with the GM results with AG as seed, more GM regions were found with activated WM tracts as seeds. The averaged FC, fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) of the above GM regions had stronger clinical correlations (r/P = 0.363/0.048 vs 0.299/0.108, 0.351/0.057 vs 0.267/0.153, 0.420/0.021 vs 0.408/0.025, for FC/fALFF/ReHo, respectively) and better classification performance to distinguish pre-/post-treatment groups (AUC = 0.91 vs 0.88, 0.65 vs 0.63, 0.87 vs 0.82, for FC/fALFF/ReHo, respectively). Our results indicated that rTMS concurrent with cognitive training could rewire brain network by enhancing GM-WM FC in AD, and corona radiata and SLF played an important role in this process.
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Affiliation(s)
- Tong Qin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Luyao Wang
- School of Life Science, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Huanyu Xu
- School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Chunyan Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Yuxuan Shao
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Fangjie Li
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai 201203, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Jiehui Jiang
- School of Life Science, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
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Zhu J, Margulies D, Qiu A. White matter functional gradients and their formation in adolescence. Cereb Cortex 2023; 33:10770-10783. [PMID: 37727985 DOI: 10.1093/cercor/bhad319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/21/2023] Open
Abstract
It is well known that functional magnetic resonance imaging (fMRI) is a widely used tool for studying brain activity. Recent research has shown that fluctuations in fMRI data can reflect functionally meaningful patterns of brain activity within the white matter. We leveraged resting-state fMRI from an adolescent population to characterize large-scale white matter functional gradients and their formation during adolescence. The white matter showed gray-matter-like unimodal-to-transmodal and sensorimotor-to-visual gradients with specific cognitive associations and a unique superficial-to-deep gradient with nonspecific cognitive associations. We propose two mechanisms for their formation in adolescence. One is a "function-molded" mechanism that may mediate the maturation of the transmodal white matter via the transmodal gray matter. The other is a "structure-root" mechanism that may support the mutual mediation roles of the unimodal and transmodal white matter maturation during adolescence. Thus, the spatial layout of the white matter functional gradients is in concert with the gray matter functional organization. The formation of the white matter functional gradients may be driven by brain anatomical wiring and functional needs.
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Affiliation(s)
- Jingwen Zhu
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Daniel Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, 45 Rue des Saint-Pères, 75006 Paris, France
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- NUS (Suzhou) Research Institute, National University of Singapore, No. 377 Linquan Street, Suzhou 215000, China
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr, Singapore 117456, Singapore
- Institute of Data Science, National University of Singapore, 3 Research Link, #04-06, Singapore 117602, Singapore
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Kowloon, Hong Kong
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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Chelladurai A, Narayan DL, Divakarachari PB, Loganathan U. fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network. Brain Sci 2023; 13:893. [PMID: 37371371 DOI: 10.3390/brainsci13060893] [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: 05/04/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.
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Affiliation(s)
- Aarthi Chelladurai
- Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode 637205, Tamil Nadu, India
| | - Dayanand Lal Narayan
- Department of Computer Science Engineering, GITAM School of Technology, GITAM University, Bengaluru 561203, Karnataka, India
| | | | - Umasankar Loganathan
- Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai 600077, Tamilnadu, India
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Ahmadi H, Fatemizadeh E, Motie-Nasrabadi A. A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer's Patients. J Biomed Phys Eng 2023; 13:125-134. [PMID: 37082543 PMCID: PMC10111107 DOI: 10.31661/jbpe.v0i0.2007-1134] [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: 07/04/2020] [Accepted: 09/07/2020] [Indexed: 03/14/2023]
Abstract
Background Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption. Objective One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated. In fMRI analysis, the Pearson correlation is used while there are other methods. This study aims to investigate the different correlation methods in functional connectivity analysis. Material and Methods In this analytical research, based on fMRI signals of Alzheimer's Disease (AD) and healthy individuals from the ADNI database, brain functional networks were generated using correlation techniques, including Pearson, Kendall, and Spearman. Then, the global and nodal measures were calculated in the whole brain and in the most important resting-state network called Default Mode Network (DMN). The statistical analysis was performed using non-parametric permutation test. Results Results show that although in nodal analysis, the performance of correlation methods was almost similar, in global features, the Spearman and Kendall were better in distinguishing AD subjects. Note that, nodal analysis reveals that the functional connectivity of the posterior areas in the brain was more damaged because of AD in comparison to frontal areas. Moreover, the functional connectivity of the dominant hemisphere was disrupted more. Conclusion Although the Pearson method has limitations in capturing non-linear relationships, it is the most prevalent method. To have a comprehensive analysis, investigating non-linear methods such as distance correlation is recommended.
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Affiliation(s)
- Hessam Ahmadi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Emad Fatemizadeh
- School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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Zhang X, Li Y, Guan Q, Dong D, Zhang J, Meng X, Chen F, Luo Y, Zhang H. Distance-dependent reconfiguration of hubs in Alzheimer's disease: a cross-tissue functional network study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.24.532772. [PMID: 36993290 PMCID: PMC10055319 DOI: 10.1101/2023.03.24.532772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimer's disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive network's GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience network's GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.
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Affiliation(s)
- Xingxing Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Yingjia Li
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Fuyong Chen
- Department of Neurosurgery, Shenzhen Hospital of University of Hong Kong, Shenzhen, China
| | - Yuejia Luo
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Haobo Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
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Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study. Brain Sci 2023; 13:brainsci13020265. [PMID: 36831808 PMCID: PMC9954618 DOI: 10.3390/brainsci13020265] [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: 12/24/2022] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.
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Cheng L, Zhan L, Huang L, Zhang H, Sun J, Huang G, Wang Y, Li M, Li H, Gao Y, Jia X. The atypical functional connectivity of Broca's area at multiple frequency bands in autism spectrum disorder. Brain Imaging Behav 2022; 16:2627-2636. [PMID: 36163448 DOI: 10.1007/s11682-022-00718-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2022] [Indexed: 11/30/2022]
Abstract
As a developmental disorder, autism spectrum disorder (ASD) has drawn much attention due to its severe impacts on one's language capacity. Broca's area, an important brain region of the language network, is largely involved in language-related functions. Using the Autism Brain Image Data Exchange (ABIDE) dataset, a mega-analysis was performed involving a total of 1454 participants (including 618 individuals with ASD and 836 healthy controls (HCs). To detect the neural pathophysiological mechanism of ASD from the perspective of language, we conducted a functional connectivity (FC) analysis with Broca's area as the seed in multiple frequency bands (conventional: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz). We found that compared with HC, ASD patients demonstrated increased FC in the left thalamus, left precuneus, left anterior cingulate and paracingulate gyri, and left medial orbital of the superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The results of the slow-5 frequency band (0.01-0.027 Hz) presented increased FC values of the left precuneus, left medial orbital of the superior frontal gyrus, right medial orbital of the superior frontal gyrus and right thalamus. No significant cluster was detected in the slow-4 frequency band (0.027-0.073 Hz). In conclusion, the abnormal functional connectivity in patients with ASD has frequency-specific properties. Furthermore, the slow-5 frequency band (0.01-0.027 Hz) mainly contributed to the findings of the conventional frequency band (0.01-0.08 Hz). The current study might shed new light on the neural pathophysiological mechanism of language impairments in people with ASD.
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Affiliation(s)
- Lulu Cheng
- School of Foreign Studies, China University of Petroleum (East China), Qingdao, 266580, China.,Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Hongqiang Zhang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China. .,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China. .,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
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11
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Yan B, Li Y, Li L, Yang X, Li TQ, Yang G, Jiang M. Quantifying the impact of Pyramid Squeeze Attention mechanism and filtering approaches on Alzheimer's disease classification. Comput Biol Med 2022; 148:105944. [PMID: 35969934 DOI: 10.1016/j.compbiomed.2022.105944] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/23/2022] [Accepted: 08/06/2022] [Indexed: 11/20/2022]
Abstract
Brain medical imaging and deep learning are important foundations for diagnosing and predicting Alzheimer's disease. In this study, we explored the impact of different image filtering approaches and Pyramid Squeeze Attention (PSA) mechanism on the image classification of Alzheimer's disease. First, during the image preprocessing, we register MRI images and remove skulls, then apply median filtering, Gaussian blur filtering, and anisotropic diffusion filtering to obtain different experimental images. After that, we add the Squeeze and Excitation (SE) mechanism and Pyramid Squeeze Attention (PSA) mechanism to the Fully Convolutional Network (FCN) model respectively, to obtain each MRI image's corresponding feature information of disease probability map. Besides, we also construct Multi-Layer Perceptron (MLP) model's framework, combining feature information of disease probability map with age, gender, and Mini-Mental State Examination (MMSE) of each sample, to get the final classification performance of model. Among them, the accuracy of the MLP-C model combining anisotropic diffusion filtering with the Pyramid Squeeze Attention mechanism can reach 98.85%. The corresponding quantitative experimental results show that different image filtering approaches and attention mechanisms provide effective assistance for the diagnosis and classification of Alzheimer's disease.
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Affiliation(s)
- Bin Yan
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Yang Li
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Lin Li
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Xiaocheng Yang
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Tie-Qiang Li
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 171 77, Stockholm, Sweden.
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
| | - Mingfeng Jiang
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
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12
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Hu M, Yu Y, He F, Su Y, Zhang K, Liu X, Liu P, Liu Y, Peng G, Luo B. Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2535954. [PMID: 36035823 PMCID: PMC9417789 DOI: 10.1155/2022/2535954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/12/2022] [Accepted: 07/06/2022] [Indexed: 11/22/2022]
Abstract
The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.
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Affiliation(s)
- Mengjie Hu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
- Department of General Practice, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Yang Yu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Fangping He
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Yujie Su
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Kan Zhang
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Xiaoyan Liu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Ping Liu
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Ying Liu
- Department of General Practice, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Guoping Peng
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Benyan Luo
- Department of Neurology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
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13
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Jiang Y, Wang P, Wen J, Wang J, Li H, Biswal BB. Hippocampus-based static functional connectivity mapping within white matter in mild cognitive impairment. Brain Struct Funct 2022; 227:2285-2297. [PMID: 35864361 DOI: 10.1007/s00429-022-02521-x] [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: 11/23/2021] [Accepted: 06/04/2022] [Indexed: 11/28/2022]
Abstract
Mild cognitive impairment (MCI) is clinically characterized by memory loss and cognitive impairment closely associated with the hippocampal atrophy. Accumulating studies have confirmed the presence of neural signal changes within white matter (WM) in resting-state functional magnetic resonance imaging (fMRI). However, it remains unclear how abnormal hippocampus activity affects the WM regions in MCI. The current study employs 43 MCI, 71 very MCI (VMCI) and 87 age-, gender-, and education-matched healthy controls (HCs) from the public OASIS-3 dataset. Using the left and right hippocampus as seed points, we obtained the whole-brain functional connectivity (FC) maps for each subject. We then perform one-way ANOVA analysis to investigate the abnormal FC regions among HCs, VMCI, and MCI. We further performed probabilistic tracking to estimate whether the abnormal FC correspond to structural connectivity disruptions. Compared to HCs, MCI and VMCI groups exhibited reduced FC in the right middle temporal gyrus within gray matter, and right temporal pole, right inferior frontal gyrus within white matter. Specific dysconnectivity is shown in the cerebellum Crus II, left inferior temporal gyrus within gray matter, and right frontal gyrus within white matter. In addition, the fiber bundles connecting the left hippocampus and right temporal pole within white matter show abnormally increased mean diffusivity in MCI. The current study proposes a new functional imaging direction for exploring the mechanism of memory decline and pathophysiological mechanisms in different stages of Alzheimer's disease.
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Affiliation(s)
- Yuan Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Jiaping Wen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianlin Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. .,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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14
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Xu X, Xu S, Han L, Yao X. Coupling analysis between functional and structural brain networks in Alzheimer's disease. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:8963-8974. [PMID: 35942744 DOI: 10.3934/mbe.2022416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.
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Affiliation(s)
- Xia Xu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Song Xu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liting Han
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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15
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Zhang X, Guan Q, Li Y, Zhang J, Zhu W, Luo Y, Zhang H. Aberrant Cross-Tissue Functional Connectivity in Alzheimer’s Disease: Static, Dynamic, and Directional Properties. J Alzheimers Dis 2022; 88:273-290. [DOI: 10.3233/jad-215649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: BOLD signals in the gray matter (GM) and white matter (WM) are tightly coupled. However, our understanding of the cross-tissue functional network in Alzheimer’s disease (AD) is limited. Objective: We investigated the changes of cross-tissue functional connectivity (FC) metrics for the GM regions susceptible to AD damage. Methods: For each GM region in the default mode (DMN) and limbic networks, we obtained its low-order static FC with any WM region, and the high-order static FC between any two WM regions based on their FC pattern similarity with multiple GM regions. The dynamic and directional properties of cross-tissue FC were then acquired, specifically for the regional pairs whose low- or high-order static FCs showed significant differences between AD and normal control (NC). Moreover, these cross-tissue FC metrics were correlated with voxel-based GM volumes and MMSE in all participants. Results: Compared to NC, AD patients showed decreased low-order static FCs between the intra-hemispheric GM-WM pairs (right ITG-right fornix; left MoFG-left posterior corona radiata), and increased low-order static, dynamic, and directional FCs between the inter-hemispheric GM-WM pairs (right MTG-left superior/posterior corona radiata). The high-order static and directional FCs between the left cingulate bundle-left tapetum were increased in AD, based on their FCs with the GMs of DMN. Those decreased and increased cross-tissue FC metrics in AD had opposite correlations with memory-related GM volumes and MMSE (positive for the decreased and negative for the increased). Conclusion: Cross-tissue FC metrics showed opposite changes in AD, possibly as useful neuroimaging biomarkers to reflect neurodegenerative and compensatory mechanisms.
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Affiliation(s)
- Xingxing Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Yingjia Li
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuejia Luo
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Haobo Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
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16
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Quan H, Yu T, Lin Y, Pan J, Mao B, Wang X, Xie J, Liu X, Zhao Y. Adiponectin Levels Are Associated with White Matter Lesions (WMLs) and Cognitive Impairment. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9943250. [PMID: 35087911 PMCID: PMC8789410 DOI: 10.1155/2022/9943250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 11/04/2021] [Accepted: 12/22/2021] [Indexed: 11/18/2022]
Abstract
METHOD In the present study, 126 patients, 90 cases in the WML group and 36 cases in the control group, were analyzed to explore the relationship between adiponectin and WMLs. All patients underwent an MRI scan to assess whether white matter lesions happened. And the serum levels of adiponectin were detected by ELISA. RESULTS In this study, according to Fazekas criteria, WMLs were divided into different severity groups. With the increase of WML score, the level of adiponectin decreased, and linear correlation analysis shows that adiponectin is negatively correlated with the severity of white matter lesions (p < 0.001). And adiponectin level was significantly positively correlated with MoCA score (p < 0.05). Moreover, adiponectin in the WMLs combined with the cognitive impairment group was significantly reduced (p < 0.01). CONCLUSION The level of adiponectin is independently associated with WMLs and cognitive function, which suggests that adiponectin may be a protective factor for WMLs and cognitive function.
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Affiliation(s)
- Hui Quan
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Tongya Yu
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Yingying Lin
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Jie Pan
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Bingjie Mao
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Xuan Wang
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Junchao Xie
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Xueyuan Liu
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
| | - Yanxin Zhao
- Shanghai Tenth People's Hospital of Tongji University, Department of Internal Neurology, Middle Yanchang Rd. 301#, Zhabei District, Shanghai, China 200072
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17
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Wang P, Wang Z, Wang J, Jiang Y, Zhang H, Li H, Biswal BB. Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer's Disease. Front Neurosci 2021; 15:697493. [PMID: 34630008 PMCID: PMC8492970 DOI: 10.3389/fnins.2021.697493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/13/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.
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Affiliation(s)
- Pan Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Zedong Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianlin Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuan Jiang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Bharat B Biswal
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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18
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Ibrahim B, Suppiah S, Ibrahim N, Mohamad M, Hassan HA, Nasser NS, Saripan MI. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review. Hum Brain Mapp 2021; 42:2941-2968. [PMID: 33942449 PMCID: PMC8127155 DOI: 10.1002/hbm.25369] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.
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Affiliation(s)
- Buhari Ibrahim
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.,Department of Physiology, Faculty of Basic Medical Sciences, Bauchi State University Gadau, Gadau, Nigeria
| | - Subapriya Suppiah
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Normala Ibrahim
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mazlyfarina Mohamad
- Centre for Diagnostic and Applied Health Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Hasyma Abu Hassan
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nisha Syed Nasser
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - M Iqbal Saripan
- Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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19
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Cheng Y, Zhang G, Zhang X, Li Y, Li J, Zhou J, Huang L, Xie S, Shen W. Identification of minimal hepatic encephalopathy based on dynamic functional connectivity. Brain Imaging Behav 2021; 15:2637-2645. [PMID: 33755921 DOI: 10.1007/s11682-021-00468-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2021] [Indexed: 12/26/2022]
Abstract
To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.
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Affiliation(s)
- Yue Cheng
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Gaoyan Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China.
| | - Xiaodong Zhang
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Yuexuan Li
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China
| | - Jingli Li
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Jiamin Zhou
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Lixiang Huang
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Shuangshuang Xie
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
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Phan LMT, Hoang TX, Vo TAT, Pham HL, Le HTN, Chinnadayyala SR, Kim JY, Lee SM, Cho WW, Kim YH, Choi SH, Cho S. Nanomaterial-based Optical and Electrochemical Biosensors for Amyloid beta and Tau: Potential for early diagnosis of Alzheimer's Disease. Expert Rev Mol Diagn 2021; 21:175-193. [PMID: 33560154 DOI: 10.1080/14737159.2021.1887732] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD), a heterogeneous pathological process representing the most common causes of dementia worldwide, has required early and accurate diagnostic tools. Neuropathological hallmarks of AD involve the aberrant accumulation of Amyloid beta (Aβ) into Amyloid plaques and hyperphosphorylated Tau into neurofibrillary tangles, occurring long before the onset of brain dysfunction.Areas covered:Considering the significance of Aβ and Tau in AD pathogenesis, these proteins have been adopted as core biomarkers of AD, and their quantification has provided precise diagnostic information to develop next-generation AD therapeutic approaches. However, conventional diagnostic methods may not suffice to achieve clinical criteria that are acceptable for proper diagnosis and treatment. The advantages of nanomaterial-based biosensors including facile miniaturization, mass fabrication, ultra-sensitivity, make them useful to be promising tools to measure Aβ and Tau simultaneously for accurate validation of low-abundance yet potentially informative biomarkers of AD.. EXPERT OPINION The study has identified the potential application of advanced biosensors as standardized clinical diagnostic tools for AD, evolving the way for new and efficient AD control with minimum economic and social burden. After clinical trial, nanobiosensors for measuring Aβ and Tau simultaneously possess innovative diagnosis of AD to provide significant contributions to primary Alzheimer's care intervention.
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Affiliation(s)
- Le Minh Tu Phan
- Department of Electronic Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea.,School of Medicine and Pharmacy, The University of Danang, Danang, Vietnam
| | - Thi Xoan Hoang
- Department of Life Science, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Thuy Anh Thu Vo
- Department of Life Science, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hoang Lan Pham
- Department of Life Science, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hien T Ngoc Le
- Department of Electronic Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | | | - Jae Young Kim
- Department of Life Science, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea
| | | | - Won Woo Cho
- Cantis Inc., Ansan-si, Gyeonggi-do, Republic of Korea
| | - Young Hyo Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, School of Medicine, Inha University, Incheon, Republic of Korea
| | - Seong Hye Choi
- Department of Neurology, School of Medicine, Inha University, Incheon, Republic of Korea
| | - Sungbo Cho
- Department of Electronic Engineering, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea.,Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, Republic of Korea
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21
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Yang T, Shen B, Wu A, Tang X, Chen W, Zhang Z, Chen B, Guo Z, Liu X. Abnormal Functional Connectivity of the Amygdala in Mild Cognitive Impairment Patients With Depression Symptoms Revealed by Resting-State fMRI. Front Psychiatry 2021; 12:533428. [PMID: 34335316 PMCID: PMC8319717 DOI: 10.3389/fpsyt.2021.533428] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 05/31/2021] [Indexed: 11/28/2022] Open
Abstract
Convergent evidence indicates that individuals with symptoms of depression exhibit altered functional connectivity (FC) of the amygdala, which is a key brain region in processing emotions. At present, the characteristics of amygdala functional circuits in patients with mild cognitive impairment (MCI) with and without depression are not clear. The current study examined the features of amygdala FC in patients with MCI with depression symptoms (D-MCI) using resting-state functional magnetic resonance imaging. We acquired resting-state functional magnetic resonance imaging data from 16 patients with D-MCI, 18 patients with MCI with no depression (nD-MCI), and 20 healthy controls (HCs) using a 3T scanner and compared the strength of amygdala FC between the three groups. Patients with D-MCI exhibited significant FC differences in the amygdala-medial prefrontal cortex and amygdala-sensorimotor networks. These results suggest that the dysfunction of the amygdala-medial prefrontal cortex network and the amygdala-sensorimotor network might be involved in the neural mechanism underlying depression in MCI.
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Affiliation(s)
- Ting Yang
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Bangli Shen
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Aiqin Wu
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Xinglu Tang
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | | - Bo Chen
- Tongde Hospital of Zhejiang, Hangzhou, China
| | | | - Xiaozheng Liu
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
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22
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Zhang J, Li Y, Gao Y, Hu J, Huang B, Rong S, Chen J, Zhang Y, Wang L, Feng S, Wang L, Nie K. An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease. J Neurol Sci 2020; 418:117077. [PMID: 32798842 DOI: 10.1016/j.jns.2020.117077] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To identify Parkinson's disease with mild cognitive impairment (PD-MCI) through surface-based morphometry (SBM) based machine learning model. METHODS 93 patients with parkinson's disease (35 PD with normal cognition, 58 PD-MCI) were examined, obtaining 276 SBM variables per subject. 20 healthy control subjects were used as the reference. After extracting features with statistically significance, support vector machine (SVM) model with grid search method was applied to identify patients with PD-MCI. Accuracy, matthews correlation coefficient (MCC), receiver operating characteristic curve (ROC), precision-recall curve (PR), AUC-ROC, AUC-PR and leave-one-out cross validation (LOOCV) strategy were employed for model evaluation. RESULTS PD-MCI is characterized by widespread structural abnormality. SVM model with SBM features achieved an accuracy of 80.00% and area under the ROC of 0.86 for identifying PD-MCI. MCC, AUC-PR, and LOOCV classification accuracy were 0.56, 0.89, and 78.08%, respectively. CONCLUSION Automatic identification of PD-MCI could be realized by SBM-based machine learning model.
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Affiliation(s)
- Jiahui Zhang
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - You Li
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Yuyuan Gao
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Jinlong Hu
- School of Computer Science & Engineering, Guangzhou Higher Education Mega Centre South China University of Technology, No.381 Wushan Road, Guangzhou, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Siming Rong
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Jianing Chen
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Limin Wang
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Shujun Feng
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China.
| | - Kun Nie
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China.
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23
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Zhao J, Ding X, Du Y, Wang X, Men G. Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification. Brain Behav 2019; 9:e01407. [PMID: 31512413 PMCID: PMC6790327 DOI: 10.1002/brb3.1407] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 07/02/2019] [Accepted: 08/13/2019] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION Alzheimer's disease (AD) is a chronic neurodegenerative disease that generally starts slowly and leads to deterioration over time. Finding biomarkers more effective to predict AD transition is important for clinical medicine. And current research indicated that the lesion regions occur in both gray matter (GM) and white matter (WM). METHODS This paper extracted BOLD time series from WM and GM, combined WM and GM together for analysis, constructed functional connectivity (FC) of static (sWGFC) and dynamic (dWGFC) between WM and GM, as well as static (sGFC) and dynamic (dGFC) FC within GM in order to evaluate the methods and areas most useful as feature sets for distinguishing NC from AD. These features will be evaluated using support vector machine (SVM) classifiers. RESULTS The FC constructed by WM BOLD time series based on fMRI showed widely differences between the AD group and NC group. In terms of the results of the classification, the performance of feature subsets selected from sWGFC was better than sGFC, and the performance of feature subsets selected from dWGFC was better than dGFC. Overall, the feature subsets selected from dWGFC was the best. CONCLUSION These results indicated that there is a wide range of disconnection between WM and GM in AD, and association between WM and GM based on fMRI only is an effective strategy, and the FC between WM and GM could be a potential biomarker in the process of cognitive impairment and AD.
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Affiliation(s)
- Jie Zhao
- College of Electronic and Information Engineering, Hebei University, Baoding, China.,Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Xuetong Ding
- College of Electronic and Information Engineering, Hebei University, Baoding, China
| | - Yuhang Du
- College of Electronic and Information Engineering, Hebei University, Baoding, China
| | - Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, China.,Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, China.,Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Guozun Men
- School of Economics, Hebei University, Baoding, China
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