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Wu Y, Wang X, Fang Y. Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative. Geriatr Gerontol Int 2024; 24 Suppl 1:96-101. [PMID: 37734954 DOI: 10.1111/ggi.14670] [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: 07/13/2023] [Revised: 08/09/2023] [Accepted: 08/31/2023] [Indexed: 09/23/2023]
Abstract
AIM Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early. METHODS We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model. RESULTS The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI. CONCLUSIONS With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.
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Affiliation(s)
- Yafei Wu
- School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen, China
| | - Xing Wang
- School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen, China
| | - Ya Fang
- School of Public Health, Xiamen University, Xiamen, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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2
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Chen L, Saykin AJ, Yao B, Zhao F. Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood. Comput Struct Biotechnol J 2022; 20:5761-5774. [PMID: 36756173 PMCID: PMC9619306 DOI: 10.1016/j.csbj.2022.10.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2022] Open
Abstract
Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomarkers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. Here, we develop two multi-task deep autoencoders, which are based on the convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representation by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal DNA methylation data collected from the peripheral blood in Alzheimer's Disease Neuroimaging Initiative, we demonstrate that the proposed multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal DNA methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only the historical DNA methylation data and the performance is further improved by including all temporal DNA methylation data. Availability:: https://github.com/lichen-lab/MTAE.
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Affiliation(s)
- Li Chen
- Department of Biostatistics, University of Florida, Gainesville, FL 32603, United States
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Bing Yao
- Department of Human Genetics, Emory University, Atlanta, GA 30322, United States
| | - Fengdi Zhao
- Department of Biostatistics, University of Florida, Gainesville, FL 32603, United States
| | - Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- Department of Biostatistics, University of Florida, Gainesville, FL 32603, United States
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Department of Human Genetics, Emory University, Atlanta, GA 30322, United States
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4
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Huang Z, Li M, Zhang L, Liu Y. Electrochemical immunosensor based on superwettable microdroplet array for detecting multiple Alzheimer's disease biomarkers. Front Bioeng Biotechnol 2022; 10:1029428. [PMID: 36329700 PMCID: PMC9622762 DOI: 10.3389/fbioe.2022.1029428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease caused by neurons damage in the brain, and it poses a serious threat to human life and health. No efficient treatment is available, but early diagnosis, discovery, and intervention are still crucial, effective strategies. In this study, an electrochemical sensing platform based on a superwettable microdroplet array was developed to detect multiple AD biomarkers containing Aβ40, Aβ42, T-tau, and P-tau181 of blood. The platform integrated a superwettable substrate based on nanoAu-modified vertical graphene (VG@Au) into a working electrode, which was mainly used for droplet sample anchoring and electrochemical signal generation. In addition, an electrochemical micro-workstation was used for signals conditioning. This superwettable electrochemical sensing platform showed high sensitivity and a low detection limit due to its excellent characteristics such as large specific surface, remarkable electrical conductivity, and good biocompatibility. The detection limit for Aβ40, Aβ42, T-tau, and P-tau181 were 0.064, 0.012, 0.039, and 0.041 pg/ml, respectively. This study provides a promising method for the early diagnosis of AD.
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Affiliation(s)
- Zhen Huang
- Longgang District Central Hospital of Shenzhen, Shenzhen, China
- Office of Shenzhen Clinical College, Guangzhou University of Chinese Medicine, Longggang District Central Hospital, Shenzhen, China
| | - Mifang Li
- Longgang District Central Hospital of Shenzhen, Shenzhen, China
| | - Lingyan Zhang
- Longgang District Central Hospital of Shenzhen, Shenzhen, China
| | - Yibiao Liu
- Longgang District Central Hospital of Shenzhen, Shenzhen, China
- Office of Shenzhen Clinical College, Guangzhou University of Chinese Medicine, Longggang District Central Hospital, Shenzhen, China
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5
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Liu Y, Liu X, Li M, Liu Q, Xu T. Portable Vertical Graphene@Au-Based Electrochemical Aptasensing Platform for Point-of-Care Testing of Tau Protein in the Blood. BIOSENSORS 2022; 12:564. [PMID: 35892461 PMCID: PMC9331743 DOI: 10.3390/bios12080564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 12/16/2022]
Abstract
Alzheimer's disease (AD) is a long-term neurodegenerative disease that poses a serious threat to human life and health. It is very important to develop a portable quantitative device for AD diagnosis and personal healthcare. Herein, we develop a portable electrochemical sensing platform for the point-of-care detection of AD biomarkers in the blood. Such a portable platform integrates nanoAu-modified vertical graphene (VG@Au) into a working electrode, which can significantly improve sensitivity and reduce detection limit due to the large specific surface, excellent electrical conductivity, high stability, and good biocompatibility. The tau protein, as an important factor in the course of AD, is selected as a key AD biomarker. The results show that the linear range of this sensing platform is 0.1 pg/mL to 1 ng/mL, with a detection limit of 0.034 pg/mL (S/N = 3), indicating that this portable sensing platform meets the demand for the detection of the tau protein in the blood. This work offers great potential for AD diagnosis and personal healthcare.
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Affiliation(s)
- Yibiao Liu
- Longgang District Central Hospital of Shenzhen, Shenzhen 518116, China; (Y.L.); (M.L.)
| | - Xingyun Liu
- School of Biomedical Engineering, Health Science Center, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China;
| | - Mifang Li
- Longgang District Central Hospital of Shenzhen, Shenzhen 518116, China; (Y.L.); (M.L.)
| | - Qiong Liu
- School of Biomedical Engineering, Health Science Center, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China;
| | - Tailin Xu
- School of Biomedical Engineering, Health Science Center, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China;
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6
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Meng X, Wei Q, Meng L, Liu J, Wu Y, Liu W. Feature Fusion and Detection in Alzheimer's Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data. Genes (Basel) 2022; 13:837. [PMID: 35627222 PMCID: PMC9140721 DOI: 10.3390/genes13050837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 01/27/2023] Open
Abstract
Voxel-based morphometry provides an opportunity to study Alzheimer's disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10-6) and cell adhesion molecules (corrected p-value = 5.44 × 10-4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Qingpeng Wei
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Li Meng
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Junlong Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Yue Wu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
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7
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Shang Q, Zhang Q, Liu X, Zhu L. Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3144035. [PMID: 35572832 PMCID: PMC9106502 DOI: 10.1155/2022/3144035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/06/2022] [Accepted: 04/15/2022] [Indexed: 11/17/2022]
Abstract
This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer's disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer's disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group (P < 0.01). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group (P < 0.001). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score (R 2 = 0.1702, 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model (P < 0.01). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model (P < 0.01). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.
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Affiliation(s)
- Qun Shang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Qi Zhang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Xiao Liu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Lingchen Zhu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
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8
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Ardalan Z, Subbian V. Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review. Front Artif Intell 2022; 5:780405. [PMID: 35265830 PMCID: PMC8899512 DOI: 10.3389/frai.2022.780405] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/17/2022] [Indexed: 12/18/2022] Open
Abstract
Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.
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Affiliation(s)
- Zaniar Ardalan
- Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States
- *Correspondence: Zaniar Ardalan
| | - Vignesh Subbian
- Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States
- Department of Biomedical Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States
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Portable electrochemical micro-workstation platform for simultaneous detection of multiple Alzheimer's disease biomarkers. Mikrochim Acta 2022; 189:91. [PMID: 35129691 DOI: 10.1007/s00604-022-05199-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/24/2022] [Indexed: 02/03/2023]
Abstract
Alzheimer's disease, as a most prevalent type of dementia, is quickly becoming one of the most expensive, lethal, and burdening diseases of this century. Though there are still no efficient therapies, early diagnosis and intervention are important directive significance to clinical works. Here, we develop a portable electrochemical micro-workstation platform consisting of an electrochemical micro-workstation and integrated electrochemical microarray for simultaneously detecting multiple AD biomarkers including Aβ40, Aβ42, T-tau, and P-tau181 in serum. The integrated electrochemical microarray is mainly used for droplet sample manipulation and signal generation. The micro-workstation can regulate signals and transfer the signals to a smartphone by Bluetooth embedded inside. This portable electrochemical micro-workstation platform exhibits excellent analysis performance. The LODs for Aβ40, Aβ42, T-tau, and P-tau181 are 0.125 pg/mL, 0.089 pg/mL, 0.142 pg/mL, and 0.176 pg/mL, respectively, which satisfies the needs of detecting AD biomarkers in serum. The combination of portable micro-workstation and integrated electrochemical microarray provides a promising strategy for the early diagnosis of Alzheimer's disease and personal healthcare.
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10
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Liu W, Cao L, Luo H, Wang Y. Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest. Front Psychiatry 2022; 13:861258. [PMID: 35463515 PMCID: PMC9022175 DOI: 10.3389/fpsyt.2022.861258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/22/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is an age-related neurological disease, which is closely associated with hippocampus, and subdividing the hippocampus into voxels can capture subtle signals that are easily missed by region of interest (ROI) methods. Therefore, studying interpretable associations between voxels can better understand the effect of voxel set on the hippocampus and AD. In this study, by analyzing the hippocampal voxel data, we propose a novel method based on clustering genetic random forest to identify the important voxels. Specifically, we divide the left and right hippocampus into voxels to constitute the initial feature set. Moreover, the random forest is constructed using the randomly selected samples and features. The genetic evolution is used to amplify the difference in decision trees and the clustering evolution is applied to generate offspring in genetic evolution. The important voxels are the features that reach the peak classification. The results demonstrate that our method has good classification and stability. Particularly, through biological analysis of the obtained voxel set, we find that they play an important role in AD by affecting the function of the hippocampus. These discoveries demonstrate the contribution of the voxel set to AD.
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Affiliation(s)
- Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Luolong Cao
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
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