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Xu X, Chen P, Li W, Xiang Y, Xie Z, Yu Q, Tang Y, Wang P. Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome. Cereb Cortex 2024; 34:bhad450. [PMID: 38012122 DOI: 10.1093/cercor/bhad450] [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: 09/28/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Abstract
Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Peiying Chen
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Weikai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400064, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 276800, China
| | - Yongsheng Xiang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Zhongfeng Xie
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Qiang Yu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Ying Tang
- Department of Electrical and Computer Engineering, Rowan University, Glassboro, New Jersey 08028, USA
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
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Qu H, Ge H, Wang L, Wang W, Hu C. Volume changes of hippocampal and amygdala subfields in patients with mild cognitive impairment and Alzheimer's disease. Acta Neurol Belg 2023:10.1007/s13760-023-02235-9. [PMID: 37043115 DOI: 10.1007/s13760-023-02235-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/06/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Automated segmentation of hippocampal and amygdala subfields could improve classification accuracy of Mild Cognitive Impairments (MCI) and Alzheimer's Disease (AD) individuals. METHODS We applied T1-weighted magnetic resonance imaging (MRI) for 21 AD, 39 MCI and 32 normal control (NC) participants at 3-Tesla MRI. Twelve hippocampal subfields and 9 amygdala subfields in each hemisphere were analyzed using FreeSurfer 6.0. RESULTS Smaller volumes were observed in right/left whole hippocampus, right/left hippocampal tail, right/left subiculum, right Cornu ammonis 1(CA1), right/left molecular layer, right granule cell-molecular layer-dentate gyrus (GC-ML-DG), right CA4, right fimbria, right whole amygdala, right/left accessory basal, right anterior amygdala area, left central, left medial and right/left cortical nucleus of AD group compared to both MCI and NC groups (p < 0.001). The volumes of right presubiculum, right CA3, right hippocampus-amygdala-transition-area (HATA), right lateral, right basal, right central, right medial, right cortico-amygdaloid transition (CAT) and right paralaminar nucleus were significantly larger in NC than AD group (p ≤ 0.001), while the volumes of right subiculum, right CA1, right molecular layer, right whole hippocampus, right whole amygdala, right basal and right accessory basal were significantly larger in NC than MCI group (p ≤ 0.002). Trend analysis showed that most hippocampus and amygdala subfields have a trend of atrophy with the decline of cognitive function. Six core components were identified by the hierarchical clustering. The combined Receiver operating characteristic (ROC) analysis achieved the diagnostic performances (AUC: 0.81) in differentiating AD from MCI; (AUC: 0.79) in differentiating MCI from NC and (AUC: 0.97) in differentiating AD from NC. CONCLUSIONS Volumetric differences of hippocampus and amygdala were at a finer subfields scale, and the volumes of right basal nucleus, left parasubiculum, left medial nucleus, left GC-ML-DG, left hippocampal fissure, and right fimbria can be employed as neuroimaging biomarkers to assist the clinical diagnosis of MCI and AD.
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Affiliation(s)
- Hang Qu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou Jiangsu, China
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Haitao Ge
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Liping Wang
- Department of Biobank, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wei Wang
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou Jiangsu, China.
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Li Z, Lin H, Zhang Q, Shi R, Xu H, Yang F, Jiang X, Wang L, Han Y, Jiang J. Individual Proportion Loss of Functional Connectivity Strength: A Novel Individual Functional Connectivity Biomarker for Subjective Cognitive Decline Populations. BIOLOGY 2023; 12:564. [PMID: 37106764 PMCID: PMC10135935 DOI: 10.3390/biology12040564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023]
Abstract
High individual variation in the subjective cognitive decline (SCD) population makes functional connectivity (FC) biomarkers unstable. This study proposed a novel individual FC index, named individual proportion loss of functional connectivity strength (IPLFCS), and explored potential biomarkers for SCD using this new index. We proposed an IPLFCS analysis framework and compared it with traditional FC in Chinese and Western cohorts. Post hoc tests were used to determine biomarkers. Pearson's correlation analysis was used to investigate the correlation between neuropsychological scores or cortical amyloid deposits and IPLFCS biomarkers. Receiver operating characteristic curves were utilized to evaluate the ability of potential biomarkers to distinguish between groups. IPLFCS of the left middle temporal gyrus (LMTG) was identified as a potential biomarker. The IPLFC was correlated with the traditional FC (r = 0.956, p < 0.001; r = 0.946, p < 0.001) and cortical amyloid deposition (r = -0.245, p = 0.029; r = -0.185, p = 0.048) in both cohorts. Furthermore, the IPLFCS decreased across the Alzheimer's disease (AD) continuum. Its diagnostic efficiency was superior to that of existing fMRI biomarkers. These findings suggest that IPLFCS of the LMTG could be a potential biomarker of SCD.
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Affiliation(s)
- Zhuoyuan Li
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Qi Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Rong Shi
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Huanyu Xu
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Fan Yang
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Xueyan Jiang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Luyao Wang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai 200444, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- School of Biomedical Engineering, Hainan University, Haikou 570228, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing 100053, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
| | - Jiehui Jiang
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, China
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai 200444, China
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Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
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Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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Lin H, Jiang J, Li Z, Sheng C, Du W, Li X, Han Y. Identification of subjective cognitive decline due to Alzheimer's disease using multimodal MRI combining with machine learning. Cereb Cortex 2023; 33:557-566. [PMID: 35348655 DOI: 10.1093/cercor/bhac084] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/10/2022] [Accepted: 02/02/2022] [Indexed: 02/03/2023] Open
Abstract
Subjective cognitive decline (SCD) is a preclinical asymptomatic stage of Alzheimer's disease (AD). Accurate diagnosis of SCD represents the greatest challenge for current clinical practice. The multimodal magnetic resonance imaging (MRI) features of 7 brain networks and 90 regions of interests from Chinese and ANDI cohorts were calculated. Machine learning (ML) methods based on support vector machine (SVM) were used to classify SCD plus and normal control. To assure the robustness of ML model, above analyses were repeated in amyloid β (Aβ) and apolipoprotein E (APOE) ɛ4 subgroups. We found that the accuracy of the proposed multimodal SVM method achieved 79.49% and 83.13%, respectively, in Chinese and ANDI cohorts for the diagnosis of the SCD plus individuals. Furthermore, adding Aβ pathology and ApoE ɛ4 genotype information can further improve the accuracy to 85.36% and 82.52%. More importantly, the classification model exhibited the robustness in the crossracial cohorts and different subgroups, which outperforms any single and 2 modalities. The study indicates that multimodal MRI imaging combining with ML classification method yields excellent and powerful performances at categorizing SCD due to AD, suggesting potential for clinical utility.
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Affiliation(s)
- Hua Lin
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Xicheng distinct, Changchun street 45, Beijing 100053, China
| | - Jiehui Jiang
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Baoshan distinct, Shangda road 99, Shanghai 200444, China
| | - Zhuoyuan Li
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Baoshan distinct, Shangda road 99, Shanghai 200444, China
| | - Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Xicheng distinct, Changchun street 45, Beijing 100053, China
| | - Wenying Du
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Xicheng distinct, Changchun street 45, Beijing 100053, China
| | - Xiayu Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Xicheng distinct, Changchun street 45, Beijing 100053, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Xicheng distinct, Changchun street 45, Beijing 100053, China.,School of Biomedical Engineering, Hainan University, Renmin road 58, Haikou 570228, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Xichen distinct, Changchun street 45, Beijing 100053, China.,National Clinical Research Center for Geriatric Disorders, Xichen distinct, Changchun street 45, Beijing 100053, China
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Du J, Huang M, Liu L. AI-Aided Disease Prediction in Visualized Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1199:107-126. [PMID: 37460729 DOI: 10.1007/978-981-32-9902-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Artificial intelligence (AI) is playing a vitally important role in promoting the revolution of future technology. Healthcare is one of the promising applications in AI, which covers medical imaging, diagnosis, robotics, disease prediction, pharmacy, health management, and hospital management. Numbers of achievements that made in these fields overturn every aspect in traditional healthcare system. Therefore, to understand the state-of-art AI in healthcare, as well as the chances and obstacles in its development, the applications of AI in disease detection and outlook and the future trends of AI-aided disease prediction were discussed in this chapter.
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Affiliation(s)
- Juan Du
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
| | - Mengen Huang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lin Liu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
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Lin M, Ou H, Zhang P, Meng Y, Wang S, Chang J, Shen A, Hu J. Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer's disease. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 280:121542. [PMID: 35792482 DOI: 10.1016/j.saa.2022.121542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/12/2022] [Accepted: 06/18/2022] [Indexed: 06/15/2023]
Abstract
Alzheimer's disease (AD) is a common nervous system disease to affect mostly elderly people over the age of 65 years. However, the diagnosis of AD is mainly depend on the imaging examination, clinical assessments and neuropsychological tests, which may get error diagnosis results and are not able to detect early AD. Here, a rapid, non-invasive, and high accuracy diagnostic method for AD especially early AD is provided based on the laser tweezers Raman spectroscopy (LTRS) combined with machine learning algorithms. AD platelets from different 3xTg-AD transgenic rats at different stages of disease are captured to collect high signal-to-noise ratio Raman signals without contact by LTRS, which is then combined with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and principal component analysis (PCA)-canonical discriminate function (CDA) for classification. The results show that the normal and diseased platelets at 3-, 6- and 12-month AD are successfully distinguished and the accuracy is 91%, 68% and 97% respectively, which demonstrates the suggested method can provide a precise detection for AD diagnosis at early, middle and advanced stages.
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Affiliation(s)
- Manman Lin
- School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China; College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Haisheng Ou
- School of Physical Sciences and Technology, Guangxi Normal University, Guilin 541004, China
| | - Peng Zhang
- School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
| | - Yanhong Meng
- School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
| | - Shenghao Wang
- School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
| | - Jing Chang
- School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
| | - Aiguo Shen
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China.
| | - Jiming Hu
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China.
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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] [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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Faldu KG, Shah JS. Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis. Expert Rev Mol Diagn 2022; 22:681-703. [PMID: 35855631 DOI: 10.1080/14737159.2022.2104639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD. AREAS COVERED This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments. EXPERT OPINION It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.
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Affiliation(s)
- Khushboo Govind Faldu
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
| | - Jigna Samir Shah
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India
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Alm KH, Soldan A, Pettigrew C, Faria AV, Hou X, Lu H, Moghekar A, Mori S, Albert M, Bakker A. Structural and Functional Brain Connectivity Uniquely Contribute to Episodic Memory Performance in Older Adults. Front Aging Neurosci 2022; 14:951076. [PMID: 35903538 PMCID: PMC9315224 DOI: 10.3389/fnagi.2022.951076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/15/2022] [Indexed: 01/26/2023] Open
Abstract
In this study, we examined the independent contributions of structural and functional connectivity markers to individual differences in episodic memory performance in 107 cognitively normal older adults from the BIOCARD study. Structural connectivity, defined by the diffusion tensor imaging (DTI) measure of radial diffusivity (RD), was obtained from two medial temporal lobe white matter tracts: the fornix and hippocampal cingulum, while functional connectivity markers were derived from network-based resting state functional magnetic resonance imaging (rsfMRI) of five large-scale brain networks: the control, default, limbic, dorsal attention, and salience/ventral attention networks. Hierarchical and stepwise linear regression methods were utilized to directly compare the relative contributions of the connectivity modalities to individual variability in a composite delayed episodic memory score, while also accounting for age, sex, cerebrospinal fluid (CSF) biomarkers of amyloid and tau pathology (i.e., Aβ42/Aβ40 and p-tau181), and gray matter volumes of the entorhinal cortex and hippocampus. Results revealed that fornix RD, hippocampal cingulum RD, and salience network functional connectivity were each significant independent predictors of memory performance, while CSF markers and gray matter volumes were not. Moreover, in the stepwise model, the addition of sex, fornix RD, hippocampal cingulum RD, and salience network functional connectivity each significantly improved the overall predictive value of the model. These findings demonstrate that both DTI and rsfMRI connectivity measures uniquely contributed to the model and that the combination of structural and functional connectivity markers best accounted for individual variability in episodic memory function in cognitively normal older adults.
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Affiliation(s)
- Kylie H. Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xirui Hou
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States,*Correspondence: Arnold Bakker,
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Ding C, Wang L, Han Y, Jiang J. Discrimination of subjective cognitive decline from healthy control based on glucose-oxygen metabolism network coupling features and machine learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3334-3337. [PMID: 36085993 DOI: 10.1109/embc48229.2022.9870934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Our previous studies have proved that preclinical Alzheimer's disease (AD) which including subjective cognitive decline (SCD) stage, can be distinguished from normal control (NC) by glucose-oxygen metabolism coupling at the voxel level, but whether the coupling at the network level worked has not been studied. Therefore, this study aimed to explore the coupling relationship between brain glucose metabolic connectivity network and oxygen functional connectivity network, and whether its feasibility as a biomarker to discriminate SCD from healthy control (HC). METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) and glucose positron emission tomography (PET) based on hybrid PET/MRI scans were used to investigate metabolism-oxygen metabolism coupling in 56 SCD individuals and 54 HCs. Network coupling features were selected by logistic regression-recursive feature elimination (LR-RFE), and then a linear support vector machine (SVM) was used to distinguish SCD and HC by using 5-fold cross-validation. RESULTS The classification average accuracy of network coupling had reached 76.36% with a standard deviation of 9.85% (with a sensitivity of 77.82%±15.13% and a specificity of 75.30%±15.15%). After receiver operating characteristic (ROC) analysis, the average area under curve (AUC) of network coupling was 0.788 (95% confidence interval [Formula: see text]). CONCLUSION This study provided a new perspective for exploring network coupling. The proposed classification method highlighted the potential clinical application by combing glucose-oxygen metabolism coupling and machine learning in identifying SCD.
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Behl T, Kaur I, Sehgal A, Singh S, Albarrati A, Albratty M, Najmi A, Meraya AM, Bungau S. The road to precision medicine: Eliminating the "One Size Fits All" approach in Alzheimer's disease. Biomed Pharmacother 2022; 153:113337. [PMID: 35780617 DOI: 10.1016/j.biopha.2022.113337] [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: 05/03/2022] [Revised: 06/18/2022] [Accepted: 06/24/2022] [Indexed: 11/29/2022] Open
Abstract
The expeditious advancement of Alzheimer's Disease (AD) is a threat to the global healthcare system, that is further supplemented by therapeutic failure. The prevalence of this disorder has been expected to quadrupole by 2050, thereby exerting a tremendous economic pressure on medical sector, worldwide. Thus, there is a dire need of a change in conventional approaches and adopt a novel methodology of disease prevention, treatment and diagnosis. Precision medicine offers a personalized approach to disease management, It is dependent upon genetic, environmental and lifestyle factors associated with the individual, aiding to develop tailored therapeutics. Precision Medicine Initiatives are launched, worldwide, to facilitate the integration of personalized models and clinical medicine. The review aims to provide a comprehensive understanding of the neuroinflammatory processes causing AD, giving a brief overview of the disease interventions. This is further followed by the role of precision medicine in AD, constituting the genetic perspectives, operation of personalized form of medicine and optimization of clinical trials with the 3 R's, showcasing an in-depth understanding of this novel approach in varying aspects of the healthcare industry, to provide an opportunity to the global AD researchers to elucidate suitable therapeutic regimens in clinically and pathologically complex diseases, like AD.
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Affiliation(s)
- Tapan Behl
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India.
| | - Ishnoor Kaur
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Aayush Sehgal
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Sukhbir Singh
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Ali Albarrati
- Rehabilitation Health Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Albratty
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Asim Najmi
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Abdulkarim M Meraya
- Pharmacy Practice Research Unit, Department of Clinical Pharmacy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Simona Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania; Doctoral School of Biomedical Sciences, University of Oradea, Oradea, Romania.
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Liu Q, Shi Z, Wang K, Liu T, Funahashi S, Wu J, Zhang J. Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson’s Patients. Front Comput Neurosci 2022; 16:891384. [PMID: 35720771 PMCID: PMC9204483 DOI: 10.3389/fncom.2022.891384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Previous studies have demonstrated a close relationship between early Parkinson’s disease and functional network abnormalities. However, the pattern of brain changes in the early stages of Parkinson’s disease has not been confirmed, which has important implications for the study of clinical indicators of Parkinson’s disease. Therefore, we investigated the functional connectivity before and after treatment in patients with early Parkinson’s disease, and further investigated the relationship between some topological properties and clinicopathological indicators. We included resting state-fMRI (rs-fMRI) data from 27 patients with early Parkinson’s disease aged 50–75 years from the Parkinson’s Disease Progression Markers Initiative (PPMI). The results showed that the functional connectivity of 6 networks, cerebellum network (CBN), cingulo_opercular network (CON), default network (DMN), fronto-parietal network (FPN), occipital network (OCC), and sensorimotor network (SMN), was significantly changed. Compared to before treatment, the main functional connections were concentrated in the CBN after treatment. In addition, the coefficients of these nodes have also changed. For betweenness centrality (BC), the FPN showed a significant improvement in treatment (p < 0.001). In conclusion, the alteration of functional networks in early Parkinson’s patients is critical for clarifying the mechanisms of early diagnosis of the disease.
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Affiliation(s)
- Qing Liu
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - ZhongYan Shi
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Kexin Wang
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Jinglong Wu,
| | - Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
- Jian Zhang,
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14
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Liu L, Wang T, Du X, Zhang X, Xue C, Ma Y, Wang D. Concurrent Structural and Functional Patterns in Patients With Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:838161. [PMID: 35663572 PMCID: PMC9161636 DOI: 10.3389/fnagi.2022.838161] [Citation(s) in RCA: 2] [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: 12/17/2021] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Amnestic mild cognitive impairment (aMCI) is a clinical subtype of MCI, which is known to have a high risk of developing Alzheimer's disease (AD). Although neuroimaging studies have reported brain abnormalities in patients with aMCI, concurrent structural and functional patterns in patients with aMCI were still unclear. In this study, we combined voxel-based morphometry (VBM), amplitude of low-frequency fluctuations (ALFFs), regional homogeneity (Reho), and resting-state functional connectivity (RSFC) approaches to explore concurrent structural and functional alterations in patients with aMCI. We found that, compared with healthy controls (HCs), both ALFF and Reho were decreased in the right superior frontal gyrus (SFG_R) and right middle frontal gyrus (MFG_R) of patients with aMCI, and both gray matter volume (GMV) and Reho were decreased in the left inferior frontal gyrus (IFG_L) of patients with aMCI. Furthermore, we took these overlapping clusters from VBM, ALFF, and Reho analyses as seed regions to analyze RSFC. We found that, compared with HCs, patients with aMCI had decreased RSFC between SFG_R and the right temporal lobe (subgyral) (TL_R), the MFG_R seed and left superior temporal gyrus (STG_L), left inferior parietal lobule (IPL_L), and right anterior cingulate cortex (ACC_R), the IFG_L seed and left precentral gyrus (PRG_L), left cingulate gyrus (CG_L), and IPL_L. These findings highlighted shared imaging features in structural and functional magnetic resonance imaging (MRI), suggesting that SFG_R, MFG_R, and IFG_L may play a major role in the pathophysiology of aMCI, which might be useful to better understand the underlying neural mechanisms of aMCI and AD.
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Affiliation(s)
- Li Liu
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tenglong Wang
- School of Humanities and Management, Graduate School of Wannan Medical College, Wuhu, China
| | - Xiangdong Du
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiaobin Zhang
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Chuang Xue
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Ma
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Dong Wang
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
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Wang J, Wang K, Liu T, Wang L, Suo D, Xie Y, Funahashi S, Wu J, Pei G. Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease. Front Comput Neurosci 2022; 16:885126. [PMID: 35586480 PMCID: PMC9108158 DOI: 10.3389/fncom.2022.885126] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.
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Affiliation(s)
- Jue Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Kexin Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Li Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Dingjie Suo
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shintaro Funahashi
- Kokoro Research Center, Kyoto University, Kyoto, Japan
- Laboratory of Cognitive Brain Science, Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China
- *Correspondence: Jinglong Wu
| | - Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Guangying Pei
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16
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Zamani J, Sadr A, Javadi AH. Comparison of cortical and subcortical structural segmentation methods in Alzheimer's disease: A statistical approach. J Clin Neurosci 2022; 99:99-108. [PMID: 35278936 DOI: 10.1016/j.jocn.2022.03.004] [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: 08/01/2021] [Revised: 02/13/2022] [Accepted: 03/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in Alzheimer's disease (AD), as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using HIPS, volBrain, CAT and BrainSuite automated segmentation methods between AD, mild cognitive impairment (MCI) and healthy controls (HC). METHODS A total of 182 MRI images were taken from the minimal interval resonance imaging in Alzheimer's disease (MIRIAD; 22 AD and 22 HC) and the Alzheimer's disease neuroimaging initiative database (ADNI; 43 AD, 50 MCI and 45 HC) datasets. Statistical methods were used to compare different groups as well as the correlation between different methods. RESULTS The two methods of volBrain and CAT showed a strong correlation (p's < 0.035 Bonferroni corrected for multiple comparisons). The two methods, however, showed no significant correlation with BrainSuite (p's > 0.820 Bonferroni corrected). Furthermore, BrainSuite did not follow the same trend as the other three methods and only HIPS, volBrain and CAT showed strong conformity with the past literature with strong correlation with mini mental state examination (MMSE) scores. CONCLUSION Our results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of HC, AD and MCI. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, UK; School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.
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17
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Gong L, Xu R, Yang D, Wang J, Ding X, Zhang B, Zhang X, Hu Z, Xi C. Orbitofrontal Cortex Functional Connectivity-Based Classification for Chronic Insomnia Disorder Patients With Depression Symptoms. Front Psychiatry 2022; 13:907978. [PMID: 35873230 PMCID: PMC9299364 DOI: 10.3389/fpsyt.2022.907978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/16/2022] [Indexed: 11/24/2022] Open
Abstract
Depression is a common comorbid symptom in patients with chronic insomnia disorder (CID). Previous neuroimaging studies found that the orbital frontal cortex (OFC) might be the core brain region linking insomnia and depression. Here, we used a machine learning approach to differentiate CID patients with depressive symptoms from CID patients without depressive symptoms based on OFC functional connectivity. Seventy patients with CID were recruited and subdivided into CID with high depressive symptom (CID-HD) and low depressive symptom (CID-LD) groups. The OFC functional connectivity (FC) network was constructed using the altered structure of the OFC region as a seed. A linear kernel SVM-based machine learning approach was carried out to classify the CID-HD and CID-LD groups based on OFC FC features. The predict model was further verified in a new cohort of CID group (n = 68). The classification model based on the OFC FC pattern showed a total accuracy of 76.92% (p = 0.0009). The area under the receiver operating characteristic curve of the classification model was 0.84. The OFC functional connectivity with reward network, salience network and default mode network contributed the highest weights to the prediction model. These results were further validated in an independent CID group with high and low depressive symptom (accuracy = 67.9%). These findings provide a potential biomarker for early diagnosis and intervention in CID patients comorbid with depression based on an OFC FC-based machine learning approach.
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Affiliation(s)
- Liang Gong
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Ronghua Xu
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Dan Yang
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Jian Wang
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Xin Ding
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Bei Zhang
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
| | - Xingping Zhang
- Department of General Practice, Chengdu Second People's Hospital, Chengdu, China
| | - Zhengjun Hu
- The Third People's Hospital of Chengdu, Chengdu, China
| | - Chunhua Xi
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, China
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18
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Influence of MRI on Diagnostic Efficacy and Satisfaction of Patients with Alzheimer's Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9038784. [PMID: 34790255 PMCID: PMC8592746 DOI: 10.1155/2021/9038784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/06/2021] [Accepted: 11/01/2021] [Indexed: 01/07/2023]
Abstract
OBJECTIVE To inquire into the influence of magnetic resonance imaging (MRI) on the diagnostic efficacy and satisfaction of patients with Alzheimer's disease (AD). METHODS This study included 42 healthy people (control group) and 66 patients with AD (AD group). The hippocampus volume, temporal sulcus spacing, left-right brain diameter, brain lobe volume, hippocampal height, temporal horn width, lateral fissure width, and degree of leukoaraiosis were all measured using an MRI scan. After diagnosis, the satisfaction of patients in both arms was investigated and the satisfaction degree was recorded. RESULTS Compared with the control group, the left and right hippocampal volumes and hippocampal height of AD patients were smaller, while the temporal sulcus spacing, temporal horn width, lateral fissure width, and left-right brain diameter were remarkably higher. A statistical difference was present in the degree of leukoaraiosis between the two arms. The frontal and temporal lobe volumes of AD patients were notably lower while the volumes of parietal and occipital lobes were similar, versus the control group. The total satisfaction was 83.33% in the control group and 86.36% in the AD group, with no statistical difference between the two arms. CONCLUSIONS MRI can effectively mine the brain information of AD patients with a high patient satisfaction, which has potential value in clinical application.
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Li D, Liu Y, Zeng X, Xiong Z, Yao Y, Liang D, Qu H, Xiang H, Yang Z, Nie L, Wu PY, Wang R. Quantitative Study of the Changes in Cerebral Blood Flow and Iron Deposition During Progression of Alzheimer's Disease. J Alzheimers Dis 2021; 78:439-452. [PMID: 32986675 DOI: 10.3233/jad-200843] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Advanced Alzheimer's disease (AD) has no effective treatment, and identifying early diagnosis markers can provide a time window for treatment. OBJECTIVE To quantify the changes in cerebral blood flow (CBF) and iron deposition during progression of AD. METHODS 94 subjects underwent brain imaging on a 3.0-T MRI scanner with techniques of three-dimensional arterial spin labeling (3D-ASL) and quantitative susceptibility mapping (QSM). The subjects included 22 patients with probable AD, 22 patients with mild cognitive impairment (MCI), 25 patients with subjective cognitive decline (SCD), and 25 normal controls (NC). The CBF and QSM values were obtained using a standardized brain region method based on the Brainnetome Atlas. The differences in CBF and QSM values were analyzed between and within groups using variance analysis and correlation analysis. RESULTS CBF and QSM identified several abnormal brain regions of interest (ROIs) at different stages of AD (p < 0.05). Regionally, the CBF values in several ROIs of the AD and MCI subjects were lower than for NC subjects (p < 0.001). Higher QSM values were observed in the globus pallidus. The CBF and QSM values in multiple ROI were negatively correlated, while the putamen was the common ROI of the three study groups (p < 0.05). The CBF and QSM values in hippocampus were cross-correlated with scale scores during the progression of AD (p < 0.05). CONCLUSION Iron deposition in the basal ganglia and reduction in blood perfusion in multiple regions existed during the progression of AD. The QSM values in putamen can be used as an imaging biomarker for early diagnosis of AD.
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Affiliation(s)
- Dongxue Li
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Yuancheng Liu
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Zhenliang Xiong
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
| | - Yuanrong Yao
- Department of Neurology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Daiyi Liang
- Department of Neurology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Hao Qu
- Department of Neurology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Hui Xiang
- Department of Psychology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Zhenggui Yang
- Department of Psychology, Guizhou Provincial People's Hospital, Guiyang, China
| | | | | | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Key Laboratory of Intelligent Medical Imaging Analysis and Accurate Diagnosis of Guizhou Province, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guiyang, China
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Zhang X, Xue C, Cao X, Yuan Q, Qi W, Xu W, Zhang S, Huang Q. Altered Patterns of Amplitude of Low-Frequency Fluctuations and Fractional Amplitude of Low-Frequency Fluctuations Between Amnestic and Vascular Mild Cognitive Impairment: An ALE-Based Comparative Meta-Analysis. Front Aging Neurosci 2021; 13:711023. [PMID: 34531735 PMCID: PMC8438295 DOI: 10.3389/fnagi.2021.711023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Changes in the amplitude of low-frequency fluctuations (ALFF) and the fractional amplitude of low-frequency fluctuations (fALFF) have provided stronger evidence for the pathophysiology of cognitive impairment. Whether the altered patterns of ALFF and fALFF differ in amnestic cognitive impairment (aMCI) and vascular mild cognitive impairment (vMCI) is largely unknown. The purpose of this study was to explore the ALFF/fALFF changes in the two diseases and to further explore whether they contribute to the diagnosis and differentiation of these diseases. Methods: We searched PubMed, Ovid, and Web of Science databases for articles on studies using the ALFF/fALFF method in patients with aMCI and vMCI. Based on the activation likelihood estimation (ALE) method, connectivity modeling based on coordinate meta-analysis and functional meta-analysis was carried out. Results: Compared with healthy controls (HCs), patients with aMCI showed increased ALFF/fALFF in the bilateral parahippocampal gyrus/hippocampus (PHG/HG), right amygdala, right cerebellum anterior lobe (CAL), left middle temporal gyrus (MTG), left cerebrum temporal lobe sub-gyral, left inferior temporal gyrus (ITG), and left cerebrum limbic lobe uncus. Meanwhile, decreased ALFF/fALFF values were also revealed in the bilateral precuneus (PCUN), bilateral cuneus (CUN), and bilateral posterior cingulate (PC) in patients with aMCI. Compared with HCs, patients with vMCI predominantly showed decreased ALFF/fALFF in the bilateral CUN, left PCUN, left PC, and right cingulate gyrus (CG). Conclusions: The present findings suggest that ALFF and fALFF displayed remarkable altered patterns between aMCI and vMCI when compared with HCs. Thus, the findings of this study may serve as a reliable tool for distinguishing aMCI from vMCI, which may help understand the pathophysiological mechanisms of these diseases.
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Affiliation(s)
- Xulian Zhang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, United States
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shaojun Zhang
- Department of Statistics, University of Florida, Gainesville, FL, United States
| | - Qingling Huang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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21
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Huang W, Li X, Li X, Kang G, Han Y, Shu N. Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients. Front Aging Neurosci 2021; 13:687927. [PMID: 34393757 PMCID: PMC8361326 DOI: 10.3389/fnagi.2021.687927] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/30/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI. METHODS A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation. RESULTS For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks. CONCLUSION White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.
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Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xuanyu Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Biomedical Engineering Institute, Hainan University, Haikou, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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Xu X, Wang T, Li W, Li H, Xu B, Zhang M, Yue L, Wang P, Xiao S. Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline. Front Aging Neurosci 2021; 13:688113. [PMID: 34305568 PMCID: PMC8299728 DOI: 10.3389/fnagi.2021.688113] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Weikai Li
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Hai Li
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Boyan Xu
- Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Min Zhang
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
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23
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Zhang Z, Cui L, Huang Y, Chen Y, Li Y, Guo Q. Changes of Regional Neural Activity Homogeneity in Preclinical Alzheimer's Disease: Compensation and Dysfunction. Front Neurosci 2021; 15:646414. [PMID: 34220418 PMCID: PMC8248345 DOI: 10.3389/fnins.2021.646414] [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] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 05/26/2021] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease and may develop into amnestic mild cognitive impairment (aMCI). Finding suitable biomarkers is the key to accurately identifying SCD. Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies on SCD patients showed functional connectivity disorders. Our goal was to explore whether local neurological homogeneity changes in SCD patients, the relationship between these changes and cognitive function, and similarities of neurological homogeneity changes between SCD and aMCI patients. MATERIALS AND METHODS 37 cases of the healthy control (HC) group, 39 cases of the SCD group, and 28 cases of the aMCI group were included. Participants underwent rs-fMRI examination and a set of neuropsychological test batteries. Regional homogeneity (ReHo) was calculated and compared between groups. ReHo values were extracted from meaningful regions in the SCD group, and the correlation between ReHo values with the performance of neuropsychological tests was analyzed. RESULTS Our results showed significant changes in the ReHo among groups. In the SCD group compared with the HC group, part of the parietal lobe, frontal lobe, and occipital lobe showed decreased ReHo, and the temporal lobe, part of the parietal lobe and the frontal lobe showed increased ReHo. The increased area of ReHo was negatively correlated with the decreased area, and was related to decrease on multiple neuropsychological tests performance. Simultaneously, the changed areas of ReHo in SCD patients are similar to aMCI patients, while aMCI group's neuropsychological test performance was significantly lower than that of the SCD group. CONCLUSION There are significant changes in local neurological homogeneity in SCD patients, and related to the decline of cognitive function. The increase of neurological homogeneity in the temporal lobe and adjacent area is negatively correlated with cognitive function, reflecting compensation for local neural damage. These changes in local neurological homogeneity in SCD patients are similar to aMCI patients, suggesting similar neuropathy in these two stages. However, the aMCI group's cognitive function was significantly worse than that of the SCD group, suggesting that this compensation is limited. In summary, regional neural activity homogeneity may be a potential biomarker for identifying SCD and measuring the disease severity.
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Affiliation(s)
- Zhen Zhang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Liang Cui
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yanlu Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yu Chen
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Yuehua Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
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24
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Chen H, Li W, Sheng X, Ye Q, Zhao H, Xu Y, Bai F. Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study. Eur Radiol 2021; 32:448-459. [PMID: 34109489 DOI: 10.1007/s00330-021-08080-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/13/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD. METHODS One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers. RESULTS We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity. CONCLUSION This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD. KEY POINTS • Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Weikai Li
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Qing Ye
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China. .,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. .,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. .,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
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25
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Ma Z, Jing B, Li Y, Yan H, Li Z, Ma X, Zhuo Z, Wei L, Li H. Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics. J Alzheimers Dis 2021; 73:991-1002. [PMID: 31884464 DOI: 10.3233/jad-190715] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer's disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.
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Affiliation(s)
- Zhe Ma
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Yuxia Li
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Huagang Yan
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Zhaoxia Li
- School of Chinese Medicine, Capital Medical University, Beijing, China
| | - Xiangyu Ma
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Zhizheng Zhuo
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Lijiang Wei
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
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26
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A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiol Aging 2021; 105:205-216. [PMID: 34102381 DOI: 10.1016/j.neurobiolaging.2021.04.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/06/2021] [Accepted: 04/23/2021] [Indexed: 11/22/2022]
Abstract
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.
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27
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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28
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Li T, Wang B, Gao Y, Wang X, Yan T, Xiang J, Niu Y, Liu T, Chen D, Fang B, Xie Y, Funahashi S, Yan T. APOE ε4 and cognitive reserve effects on the functional network in the Alzheimer's disease spectrum. Brain Imaging Behav 2021; 15:758-771. [PMID: 32314201 DOI: 10.1007/s11682-020-00283-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The apolipoprotein E (APOE) ε4 allele is a genetic risk factor for Alzheimer's disease, whereas educational attainments have protective effects against cognitive decline in aging and patients with Alzheimer's disease. We examined the possible effects of years of education and APOE genotype on the topological properties of the functional network in normal aging, mild cognitive impairment and Alzheimer's disease. The years of education showed a significant, negative association with the local efficiency, clustering coefficient and small-worldness of functional networks in APOE ε4 noncarriers but not in ε4 carriers. These associations were mainly observed in normal aging and were reduced in mild cognitive impairment and Alzheimer's disease. Moreover, regions of the inferior frontal gyrus, temporal pole, and cuneus also showed correlations between education and nodal degree. Our findings demonstrated that the protective effects of education persist in APOE ε4 noncarriers but diminish in ε4 carriers. In addition, the protective effects of education were attenuated or reduced in the progression of Alzheimer's disease.
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Affiliation(s)
- Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yuan Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Boyan Fang
- Department of Neurology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shintaro Funahashi
- Advanced research institute of multidisciplinary science, Beijing Institute of Technology, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China.
- Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing, China.
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29
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Dong QY, Li TR, Jiang XY, Wang XN, Han Y, Jiang JH. Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a Han population. ALZHEIMERS RESEARCH & THERAPY 2021; 13:74. [PMID: 33827675 PMCID: PMC8028241 DOI: 10.1186/s13195-021-00811-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/22/2021] [Indexed: 12/16/2022]
Abstract
Introduction Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer’s disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level. Methods This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition. Results Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus. Conclusions These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00811-w.
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Affiliation(s)
- Qiu-Yue Dong
- Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Tao-Ran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xue-Yan Jiang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,German Center for Neurodegenerative Diseases, Clinical Research group, Venusberg Campus 1, Building 99, Bonn, Germany
| | - Xiao-Ni Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China. .,School of Biomedical Engineering, Hainan University, Haikou, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China.
| | - Jie-Hui Jiang
- Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China.
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30
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Yan T, Liu T, Ai J, Shi Z, Zhang J, Pei G, Wu J. Task-induced activation transmitted by structural connectivity is associated with behavioral performance. Brain Struct Funct 2021; 226:1437-1452. [DOI: 10.1007/s00429-021-02249-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/27/2021] [Indexed: 12/18/2022]
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31
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Li T, Liao Z, Mao Y, Hu J, Le D, Pei Y, Sun W, Lin J, Qiu Y, Zhu J, Chen Y, Qi C, Ye X, Su H, Yu E. Temporal dynamic changes of intrinsic brain activity in Alzheimer's disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:63. [PMID: 33553356 PMCID: PMC7859807 DOI: 10.21037/atm-20-7214] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/23/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory impairment. Previous studies have largely focused on alterations of static brain activity occurring in patients with AD. Few studies to date have explored the characteristics of dynamic brain activity in cognitive impairment, and their predictive ability in AD patients. METHODS One hundred and eleven AD patients, 29 MCI patients, and 73 healthy controls (HC) were recruited. The dynamic amplitude of low-frequency fluctuation (dALFF) and the dynamic fraction amplitude of low-frequency fluctuation (dfALFF) were used to assess the temporal variability of local brain activity in patients with AD or mild cognitive impairment (MCI). Pearson's correlation coefficients were calculated between the metrics and subjects' behavioral scores. RESULTS The results of analysis of variance indicated that the AD, MCI, and HC groups showed significant variability of dALFF in the cerebellar posterior and middle temporal lobes. In AD patients, these brain regions had high dALFF variability. Significant dfALFF variability was found between the three groups in the left calcarine cortex and white matter. The AD group showed lower dfALFF than the MCI group in the left calcarine cortex. CONCLUSIONS Compared to HC, AD patients were found to have increased dALFF variability in the cerebellar posterior and temporal lobes. This abnormal pattern may diminish the capacity of the cerebellum and temporal lobes to participate in the cerebrocerebellar circuits and default mode network (DMN), which regulate cognition and emotion in AD. The findings above indicate that the analysis of dALFF and dfALFF based on functional magnetic resonance imaging data may give a new insight into the neurophysiological mechanisms of AD.
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Affiliation(s)
- Ting Li
- Zhejiang Provincial People’s Hospital, Qingdao University, Qingdao, China
| | - Zhengluan Liao
- Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Yanping Mao
- Department of Psychological Medicine, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Jiaojiao Hu
- Department of Psychological Medicine, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Dansheng Le
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yangliu Pei
- Graduate faculty, Bengbu Medical College, Bengbu, China
| | - Wangdi Sun
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jixin Lin
- Department of Internal Medicine, Shengsi County People’s Hospital, Zhoushan, China
| | - Yaju Qiu
- Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Junpeng Zhu
- Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Yan Chen
- Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Chang Qi
- Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Xiangming Ye
- Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Heng Su
- Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, China
| | - Enyan Yu
- Department of Psychological Medicine, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
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Amaefule CO, Dyrba M, Wolfsgruber S, Polcher A, Schneider A, Fliessbach K, Spottke A, Meiberth D, Preis L, Peters O, Incesoy EI, Spruth EJ, Priller J, Altenstein S, Bartels C, Wiltfang J, Janowitz D, Bürger K, Laske C, Munk M, Rudolph J, Glanz W, Dobisch L, Haynes JD, Dechent P, Ertl-Wagner B, Scheffler K, Kilimann I, Düzel E, Metzger CD, Wagner M, Jessen F, Teipel SJ. Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer's disease spectrum. NEUROIMAGE-CLINICAL 2020; 29:102533. [PMID: 33360018 PMCID: PMC7770965 DOI: 10.1016/j.nicl.2020.102533] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 11/24/2020] [Accepted: 12/12/2020] [Indexed: 02/06/2023]
Abstract
Composite scores provide reliable metrics of domain function in multicenter cohort. Visuo-spatial domain composite scores relate to anatomic changes in AD spectrum. Domain scores relate to network-specific resting-state connectivity in AD spectrum.
Background Cognitive decline has been found to be associated with gray matter atrophy and disruption of functional neural networks in Alzheimer’s disease (AD) in structural and functional imaging (fMRI) studies. Most previous studies have used single test scores of cognitive performance among monocentric cohorts. However, cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide a comprehensive description of the structural and functional correlates of the key cognitive domains of AD. Method We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the AD-spectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the association of composite scores with regional atrophy and network-specific functional connectivity among the patient subgroup of SCD, MCI and AD. Result Cognitive performance, atrophy patterns and functional connectivity significantly differed between diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network, patterns of network-specific resting-state functional connectivity were positively associated with distinct cognitive impairments among the patient subgroup in the AD-spectrum. Conclusion Consistent associations between cognitive domain scores and both regional atrophy and network-specific functional connectivity (except for the visual network), support the utility of a multicentric and cognitive domain approach towards explicating the relationship between imaging markers and cognition in the AD-spectrum.
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Affiliation(s)
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | | | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Dix Meiberth
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Lukas Preis
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Goettingen, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Goettingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany
| | - Katharina Bürger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Matthias Munk
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Janna Rudolph
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - John D Haynes
- Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Goettingen, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig Maximilians University, Munich, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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Xu X, Li W, Tao M, Xie Z, Gao X, Yue L, Wang P. Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View. Front Neurosci 2020; 14:577887. [PMID: 33132832 PMCID: PMC7550635 DOI: 10.3389/fnins.2020.577887] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Weikai Li
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China.,Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Mengling Tao
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Zhongfeng Xie
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Xin Gao
- Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
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34
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Wang X, Huang W, Su L, Xing Y, Jessen F, Sun Y, Shu N, Han Y. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener 2020; 15:55. [PMID: 32962744 PMCID: PMC7507636 DOI: 10.1186/s13024-020-00395-3] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/07/2020] [Indexed: 12/15/2022] Open
Abstract
Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.
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Affiliation(s)
- Xiaoqi Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Sino-Britain Centre for Cognition and Ageing Research, Southwest University, Chongqing, China
| | - Yue Xing
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, 50937, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Yu Sun
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. .,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China.
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35
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Chen H, Sheng X, Luo C, Qin R, Ye Q, Zhao H, Xu Y, Bai F. The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline. Transl Neurodegener 2020; 9:21. [PMID: 32460888 PMCID: PMC7254770 DOI: 10.1186/s40035-020-00201-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/20/2020] [Indexed: 01/01/2023] Open
Abstract
Background Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer’s disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD. Methods Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals. Results SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%. Conclusion The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Caimei Luo
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Qing Ye
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China. .,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. .,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. .,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
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Viviano RP, Damoiseaux JS. Functional neuroimaging in subjective cognitive decline: current status and a research path forward. Alzheimers Res Ther 2020; 12:23. [PMID: 32151277 PMCID: PMC7063727 DOI: 10.1186/s13195-020-00591-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/26/2020] [Indexed: 12/20/2022]
Abstract
Subjective cognitive decline is a putative precursor to dementia marked by perceived worsening of cognitive function without overt performance issues on neuropsychological assessment. Although healthy older adults with subjective cognitive decline may function normally, perceived worsening may indicate incipient dementia and predict future deterioration. Therefore, the experience of decline represents a possible entry point for clinical intervention. However, intervention requires a physical manifestation of neuroabnormality to both corroborate incipient dementia and to target clinically. While some individuals with subjective cognitive decline may harbor pathophysiology for specific neurodegenerative disorders, many do not display clear indicators. Thus, disorder-agnostic brain measures could be useful to track the trajectory of decline, and functional neuroimaging in particular may be sensitive to detect incipient dementia and have the ability to track disease-related change when the underlying disease etiology remains unclear. Therefore, in this review, we discuss functional neuroimaging studies of subjective cognitive decline and possible reconciliations to inconsistent findings. We conclude by proposing a functional model where noisy signal propagation and inefficient signal processing across whole-brain networks may lead to the subjective experience of decline and discuss future research directions guided by this model.
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Affiliation(s)
- Raymond P Viviano
- Department of Psychology, Wayne State University, 5057 Woodward Ave. 7th Floor Suite 7908, Detroit, MI, 48201, USA
- Institute of Gerontology, Wayne State University, 87 E. Ferry St., Detroit, MI, 48202, USA
| | - Jessica S Damoiseaux
- Department of Psychology, Wayne State University, 5057 Woodward Ave. 7th Floor Suite 7908, Detroit, MI, 48201, USA.
- Institute of Gerontology, Wayne State University, 87 E. Ferry St., Detroit, MI, 48202, USA.
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