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Bašić J, Milošević V, Djordjević B, Stojiljković V, Živanović M, Stefanović N, Aracki Trenkić A, Stojanov D, Jevtović Stoimenov T, Stojanović I. Matrix Remodeling Enzymes as Potential Fluid Biomarkers of Neurodegeneration in Alzheimer's Disease. Int J Mol Sci 2024; 25:5703. [PMID: 38891891 PMCID: PMC11171655 DOI: 10.3390/ijms25115703] [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: 03/30/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
This study investigated the diagnostic accuracy of plasma biomarkers-specifically, matrix metalloproteinase (MMP-9), tissue inhibitor of metalloproteinase (TIMP-1), CD147, and the MMP-/TIMP-1 ratio in patients with Alzheimer's disease (AD) dementia. The research cohort comprised patients diagnosed with probable AD dementia and a control group of cognitively unimpaired (CU) individuals. Neuroradiological assessments included brain magnetic resonance imaging (MRI) following dementia protocols, with subsequent volumetric analysis. Additionally, cerebrospinal fluid (CSF) AD biomarkers were classified using the A/T/N system, and apolipoprotein E (APOE) ε4 carrier status was determined. Findings revealed elevated plasma levels of MMP-9 and TIMP-1 in AD dementia patients compared to CU individuals. Receiver operating characteristic (ROC) curve analysis demonstrated significant differences in the areas under the curve (AUC) for MMP-9 (p < 0.001) and TIMP-1 (p < 0.001). Notably, plasma TIMP-1 levels were significantly lower in APOE ε4+ patients than in APOE ε4- patients (p = 0.041). Furthermore, APOE ε4+ patients exhibited reduced hippocampal volume, particularly in total, right, and left hippocampal measurements. TIMP-1 levels exhibited a positive correlation, while the MMP-9/TIMP-1 ratio showed a negative correlation with hippocampal volume parameters. This study sheds light on the potential use of TIMP-1 as a diagnostic marker and its association with hippocampal changes in AD.
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Affiliation(s)
- Jelena Bašić
- Department of Biochemistry, Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (B.D.); (V.S.); (T.J.S.); (I.S.)
| | - Vuk Milošević
- Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (V.M.); (A.A.T.); (D.S.)
- Clinic of Neurology, University Clinical Center Niš, 18000 Niš, Serbia
| | - Branka Djordjević
- Department of Biochemistry, Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (B.D.); (V.S.); (T.J.S.); (I.S.)
| | - Vladana Stojiljković
- Department of Biochemistry, Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (B.D.); (V.S.); (T.J.S.); (I.S.)
| | - Milica Živanović
- Center for Radiology, University Clinical Center Niš, 18000 Niš, Serbia;
| | - Nikola Stefanović
- Department of Pharmacy, Faculty of Medicine, University of Niš, 18000 Niš, Serbia;
| | - Aleksandra Aracki Trenkić
- Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (V.M.); (A.A.T.); (D.S.)
- Center for Radiology, University Clinical Center Niš, 18000 Niš, Serbia;
| | - Dragan Stojanov
- Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (V.M.); (A.A.T.); (D.S.)
- Center for Radiology, University Clinical Center Niš, 18000 Niš, Serbia;
| | - Tatjana Jevtović Stoimenov
- Department of Biochemistry, Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (B.D.); (V.S.); (T.J.S.); (I.S.)
| | - Ivana Stojanović
- Department of Biochemistry, Faculty of Medicine, University of Niš, 18000 Niš, Serbia; (B.D.); (V.S.); (T.J.S.); (I.S.)
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Park B, Kim Y, Park J, Choi H, Kim SE, Ryu H, Seo K. Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study. J Med Internet Res 2024; 26:e54538. [PMID: 38631021 PMCID: PMC11063880 DOI: 10.2196/54538] [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: 11/15/2023] [Revised: 12/29/2023] [Accepted: 03/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. OBJECTIVE We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. METHODS The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. RESULTS The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). CONCLUSIONS The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.
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Affiliation(s)
- Bogyeom Park
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Yuwon Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
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Yang M, Meng S, Wu F, Shi F, Xia Y, Feng J, Zhang J, Li C. Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging. Front Med (Lausanne) 2024; 11:1305565. [PMID: 38283620 PMCID: PMC10811129 DOI: 10.3389/fmed.2024.1305565] [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: 10/02/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
Purpose Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). Method This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Result Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.
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Affiliation(s)
- Mingguang Yang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Shan Meng
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Jinan, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Jinrui Zhang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
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Zhang Y, Li H, Zheng Q. A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer's disease: deep learning analysis in 3238 participants worldwide. Eur Radiol 2023; 33:5385-5397. [PMID: 36892643 DOI: 10.1007/s00330-023-09519-x] [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: 08/10/2022] [Revised: 12/19/2022] [Accepted: 02/02/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES Hippocampal characterization is one of the most significant hallmarks of Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal characterization is pivotal for developing a well-performing biomarker for AD. To verify whether a comprehensive characterization of hippocampal features of gray matter volume, segmentation probability, and radiomics features could better distinguish AD from normal control (NC), and to investigate whether the classification decision score could serve as a robust and individualized brain signature. METHODS A total of 3238 participants' structural MRI from four independent databases were employed to conduct a 3D residual attention network (3DRA-Net) to classify NC, mild cognitive impairment (MCI), and AD. The generalization was validated under inter-database cross-validation. The neurobiological basis of the classification decision score as a neuroimaging biomarker was systematically investigated by association with clinical profiles, as well as longitudinal trajectory analysis to reveal AD progression. All image analyses were performed only upon the single modality of T1-weighted MRI. RESULTS Our study exhibited an outstanding performance (ACC = 91.6%, AUC = 0.95) of the comprehensive characterization of hippocampal features in distinguishing AD (n = 282) from NC (n = 603) in Alzheimer's Disease Neuroimaging Initiative cohort, and ACC = 89.2% and AUC = 0.93 under external validation. More importantly, the constructed score was significantly correlated with clinical profiles (p < 0.05), and dynamically altered over the AD longitudinal progression, provided compelling evidence of a solid neurobiological basis. CONCLUSIONS This systemic study highlights the potential of the comprehensive characterization of hippocampal features to provide an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD. KEY POINTS • The comprehensive characterization of hippocampal features exhibited ACC = 91.6% (AUC = 0.95) in classifying AD from NC under intra-database cross-validation, and ACC = 89.2% (AUC = 0.93) in external validation. • The constructed classification score was significantly associated with clinical profiles, and dynamically altered over the AD longitudinal progression, which highlighted its potential of being an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.
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Affiliation(s)
- Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai City, 264005, Shandong Province, China
| | - Hongming Li
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai City, 264005, Shandong Province, China.
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Zhou K, Piao S, Liu X, Luo X, Chen H, Xiang R, Geng D. A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction. Front Aging Neurosci 2023; 14:1073909. [PMID: 36726800 PMCID: PMC9884698 DOI: 10.3389/fnagi.2022.1073909] [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: 10/19/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning. Methods In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve. Results Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification. Discussion The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
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Affiliation(s)
- Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China,*Correspondence: Daoying Geng,
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