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Liu L, Sun S, Kang W, Wu S, Lin L. A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis. Rev Neurosci 2024; 35:121-139. [PMID: 37419866 DOI: 10.1515/revneuro-2023-0033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/18/2023] [Indexed: 07/09/2023]
Abstract
Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.
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Affiliation(s)
- Lingyu Liu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Wenjie Kang
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shuicai Wu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Lan Lin
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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2
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Singh SP, Gupta S, Rajapakse JC. Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:371-381. [PMID: 38633564 PMCID: PMC11023626 DOI: 10.1109/jtehm.2024.3366504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 11/17/2023] [Accepted: 02/12/2024] [Indexed: 04/19/2024]
Abstract
Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer. The number of trainable parameters and the training time is significantly reduced compared to feedforward networks. We demonstrate superior performance of this architecture in encoding the structural connectome implicated in Alzheimer's disease (AD) and Parkinson's disease (PD) from DTI brain scans. For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. We show that the proposed architecture reduces 45.1% and 47.1% of the trainable parameters compared to a feedforward DNN with an increase in accuracy by 2.6 % and 3.1% for cognitively normal (CN) vs AD and CN vs PD classification, respectively. We also show that the proposed RFE method leads to a further increase in accuracy by 2.1% and 4% for CN vs AD and CN vs PD classification, while removing approximately 90% to 95% irrelevant features. Furthermore, we argue that the biomarkers (i.e., key brain regions and connections) identified are consistent with previous literature. We show that relevancy score-based methods can yield high discriminative power and are suitable for brain decoding. We also show that the proposed approach led to a reduction in the number of trainable network parameters, an increase in classification accuracy, and a detection of brain connections and regions that were consistent with earlier studies.
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Affiliation(s)
- Satya P. Singh
- Division of Electronics and Communication EngineeringNetaji Subhas University of TechnologyDwarkaNew Delhi110078India
| | - Sukrit Gupta
- Department of Computer Science and EngineeringIndian Institute of Technology RoparRupnagarPunjab140001India
| | - Jagath C. Rajapakse
- School of Computer Science and EngineeringNanyang Technological UniversityNanyangSingapore639798
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3
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Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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4
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Altered structural and functional homotopic connectivity associated with the progression from mild cognitive impairment to Alzheimer's disease. Psychiatry Res 2023; 319:115000. [PMID: 36502711 DOI: 10.1016/j.psychres.2022.115000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/28/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022]
Abstract
The progressive mild cognitive impairment (pMCI) is associated with an increased risk of Alzheimer's disease (AD). Many studies have reported the disrupted brain alteration during the imminent conversion from pMCI to AD. However, the subtle difference of structural and functional of inter-hemispheric between pMCI and stable mild cognitive impairment (sMCI) remains unknown. In the present study, we scanned the multimodal magnetic resonance imaging of 38 sMCI, 26 pMCI, and 50 healthy controls (HC) and assessed the cognitive function. The voxel-mirrored homotopic connectivity (VMHC) and volume of corpus callosum were calculated. A structural equation modeling (SEM) was established to determine the relationships between the corpus callosum, the inter-hemispheric connectivity, and cognitive assessment. Compared to sMCI, pMCI exhibited decreased VMHC in insular and thalamus, and reduced volume of corpus callosum. SEM results showed that decreased inter-hemispheric connectivity was directly associated with cognitive impairment and corpus callosum atrophy, and corpus callosum atrophy indirectly caused cognitive impairment by mediating inter-hemispheric connectivity in pMCI. In conclusion, the destruction of homotopic connectivity is related to cognitive impairment, and the corpus callosum atrophy partially mediates the association between the homotopic connectivity and cognitive impairment in pMCI.
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Breuninger TA, Wawro N, Freuer D, Reitmeier S, Artati A, Grallert H, Adamski J, Meisinger C, Peters A, Haller D, Linseisen J. Fecal Bile Acids and Neutral Sterols Are Associated with Latent Microbial Subgroups in the Human Gut. Metabolites 2022; 12:metabo12090846. [PMID: 36144250 PMCID: PMC9504437 DOI: 10.3390/metabo12090846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/28/2022] Open
Abstract
Bile acids, neutral sterols, and the gut microbiome are intricately intertwined and each affects human health and metabolism. However, much is still unknown about this relationship. This analysis included 1280 participants of the KORA FF4 study. Fecal metabolites (primary and secondary bile acids, plant and animal sterols) were analyzed using a metabolomics approach. Dirichlet regression models were used to evaluate associations between the metabolites and twenty microbial subgroups that were previously identified using latent Dirichlet allocation. Significant associations were identified between 12 of 17 primary and secondary bile acids and several of the microbial subgroups. Three subgroups showed largely positive significant associations with bile acids, and six subgroups showed mostly inverse associations with fecal bile acids. We identified a trend where microbial subgroups that were previously associated with “healthy” factors were here inversely associated with fecal bile acid levels. Conversely, subgroups that were previously associated with “unhealthy” factors were positively associated with fecal bile acid levels. These results indicate that further research is necessary regarding bile acids and microbiota composition, particularly in relation to metabolic health.
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Affiliation(s)
- Taylor A. Breuninger
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
- Correspondence:
| | - Nina Wawro
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Dennis Freuer
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
| | - Sandra Reitmeier
- Chair of Nutrition and Immunology, Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL—Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Anna Artati
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Metabolomics and Proteomics Core, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Harald Grallert
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Christa Meisinger
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
| | - Annette Peters
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Dirk Haller
- Chair of Nutrition and Immunology, Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL—Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Jakob Linseisen
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
- ZIEL—Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
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Chen P, Yao H, Tijms BM, Wang P, Wang D, Song C, Yang H, Zhang Z, Zhao K, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhang X, Jiang T, Zhou Y, Lu J, Han Y, Liu B, Zhou B, Liu Y. Four Distinct Subtypes of Alzheimer's Disease Based on Resting-State Connectivity Biomarkers. Biol Psychiatry 2022; 93:759-769. [PMID: 36137824 DOI: 10.1016/j.biopsych.2022.06.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/19/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. METHODS We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. RESULTS Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. CONCLUSIONS These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC - Location VUmc, The Netherlands
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Kun Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Yida Qu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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Jellinger KA. Pathobiological Subtypes of Alzheimer Disease. Dement Geriatr Cogn Disord 2021; 49:321-333. [PMID: 33429401 DOI: 10.1159/000508625] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 05/11/2020] [Indexed: 11/19/2022] Open
Abstract
Alzheimer disease (AD), the most common form of dementia, is a heterogenous disorder with various pathobiological subtypes. In addition to the 4 major subtypes based on the distribution of tau pathology and brain atrophy (typical, limbic predominant, hippocampal sparing, and minimal atrophy [MA]), several other clinical variants showing distinct regional patterns of tau burden have been identified: nonamnestic, corticobasal syndromal, primary progressive aphasia, posterior cortical atrophy, behavioral/dysexecutive, and mild dementia variants. Among the subtypes, differences were found in age at onset, sex distribution, cognitive status, disease duration, APOE genotype, and biomarker levels. The patterns of key network destructions parallel the tau and atrophy patterns of the AD subgroups essentially. Interruption of key networks, in particular the default-mode network that is responsible for cognitive decline, is consistent in hetero-genous AD groups. AD pathology is often associated with co-pathologies: cerebrovascular lesions, Lewy pathology, and TDP-43 proteinopathies. These mixed pathologies essentially influence the clinical picture of AD and may accel-erate disease progression. Unraveling the heterogeneity among the AD spectrum entities is important for opening a window to pathogenic mechanisms affecting the brain and enabling precision medicine approaches as a basis for developing preventive and ultimately successful disease-modifying therapies for AD.
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Zhang B, Lin L, Wu S. A Review of Brain Atrophy Subtypes Definition and Analysis for Alzheimer’s Disease Heterogeneity Studies. J Alzheimers Dis 2021; 80:1339-1352. [DOI: 10.3233/jad-201274] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Alzheimer’s disease (AD) is a heterogeneous disease with different subtypes. Studying AD subtypes from brain structure, neuropathology, and cognition are of great importance for AD heterogeneity research. Starting from the study of constructing AD subtypes based on the features of T1-weighted structural magnetic resonance imaging, this paper introduces the major connections between the subtype definition and analysis strategies, including brain region-based subtype definition, and their demographic, neuropathological, and neuropsychological characteristics. The advantages and existing problems are analyzed, and reasonable improvement schemes are prospected. Overall, this review offers a more comprehensive view in the field of atrophy subtype in AD, along with their advantages, challenges, and future prospects, and provide a basis for improving individualized AD diagnosis.
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Affiliation(s)
- Baiwen Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Lan Lin
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
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9
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Breuninger TA, Wawro N, Breuninger J, Reitmeier S, Clavel T, Six-Merker J, Pestoni G, Rohrmann S, Rathmann W, Peters A, Grallert H, Meisinger C, Haller D, Linseisen J. Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation. MICROBIOME 2021; 9:61. [PMID: 33726846 PMCID: PMC7967986 DOI: 10.1186/s40168-020-00969-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/06/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND The gut microbiome impacts human health through various mechanisms and is involved in the development of a range of non-communicable diseases. Diet is a well-known factor influencing microbe-host interaction in health and disease. However, very few findings are based on large-scale analysis using population-based studies. Our aim was to investigate the cross-sectional relationship between habitual dietary intake and gut microbiota structure in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study. RESULTS Fecal microbiota was analyzed using 16S rRNA gene amplicon sequencing. Latent Dirichlet allocation (LDA) was applied to samples from 1992 participants to identify 20 microbial subgroups within the study population. Each participant's gut microbiota was subsequently described by a unique composition of these 20 subgroups. Associations between habitual dietary intake, assessed via repeated 24-h food lists and a Food Frequency Questionnaire, and the 20 subgroups, as well as between prevalence of metabolic diseases/risk factors and the subgroups, were assessed with multivariate-adjusted Dirichlet regression models. After adjustment for multiple testing, eight of 20 microbial subgroups were significantly associated with habitual diet, while nine of 20 microbial subgroups were associated with the prevalence of one or more metabolic diseases/risk factors. Subgroups 5 (Faecalibacterium, Lachnospiracea incertae sedis, Gemmiger, Roseburia) and 14 (Coprococcus, Bacteroides, Faecalibacterium, Ruminococcus) were particularly strongly associated with diet. For example, participants with a high probability for subgroup 5 were characterized by a higher Alternate Healthy Eating Index and Mediterranean Diet Score and a higher intake of food items such as fruits, vegetables, legumes, and whole grains, while participants with prevalent type 2 diabetes mellitus were characterized by a lower probability for subgroup 5. CONCLUSIONS The associations between habitual diet, metabolic diseases, and microbial subgroups identified in this analysis not only expand upon current knowledge of diet-microbiota-disease relationships, but also indicate the possibility of certain microbial groups to be modulated by dietary intervention, with the potential of impacting human health. Additionally, LDA appears to be a powerful tool for interpreting latent structures of the human gut microbiota. However, the subgroups and associations observed in this analysis need to be replicated in further studies. Video abstract.
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Affiliation(s)
- Taylor A. Breuninger
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
| | - Nina Wawro
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
| | | | - Sandra Reitmeier
- Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Thomas Clavel
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
- Functional Microbiome Research Group, Institute of Medical Microbiology, RWTH University Hospital, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Julia Six-Merker
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Giulia Pestoni
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
| | - Sabine Rohrmann
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, Deutsches Diabetes-Zentrum (DDZ), Auf’m Hennekamp 65, 40225 Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Christa Meisinger
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
| | - Dirk Haller
- Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Jakob Linseisen
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
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Martín-Signes M, Cano-Melle C, Chica AB. Fronto-parietal networks underlie the interaction between executive control and conscious perception: Evidence from TMS and DWI. Cortex 2020; 134:1-15. [PMID: 33248337 DOI: 10.1016/j.cortex.2020.09.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 06/19/2020] [Accepted: 09/29/2020] [Indexed: 01/21/2023]
Abstract
The executive control network is involved in novel situations or those in which prepotent responses need to be overridden. Previous studies have demonstrated that when control is exerted, conscious perception is impaired, and this effect is related to the functional connectivity of fronto-parietal regions. In the present study, we explored the causal involvement of one of the nodes of this fronto-parietal network (the right Supplementary Motor Area, SMA) in the interaction between executive control and conscious perception. Participants performed a dual task in which they responded to a Stroop task while detecting the presence/absence of a near-threshold Gabor stimulus. Concurrently, transcranial magnetic stimulation (TMS) was applied over the right SMA or a control site (vertex; Experiment 1). As a further control, the right Frontal Eye Field (FEF) was stimulated in Experiment 2. Diffusion-weighted imaging (DWI) tractography was used to isolate the three branches of the superior longitudinal fasciculus (SLF I, II and III), and the frontal aslant tract (FAT), and to explore if TMS effects were related to their micro- and macrostructural characteristics. Results demonstrated reduced perceptual sensitivity on incongruent as compared to congruent Stroop trials. A causal role of the right SMA on the modulation of perceptual sensitivity by executive control was only demonstrated when the microstructure of the right SLF III or the left FAT were taken into account. The volume of the right SLF III was also related to the modulation of response criterion by executive control when the right FEF was stimulated. These results add evidence in favor of shared neural correlates for attention and conscious perception in fronto-parietal regions and highlight the role of white matter in TMS effects.
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Affiliation(s)
- Mar Martín-Signes
- Department of Experimental Psychology, and Mind, Brain, and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain.
| | - Cristina Cano-Melle
- Department of Experimental Psychology, and Mind, Brain, and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
| | - Ana B Chica
- Department of Experimental Psychology, and Mind, Brain, and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
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Habes M, Grothe MJ, Tunc B, McMillan C, Wolk DA, Davatzikos C. Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods. Biol Psychiatry 2020; 88:70-82. [PMID: 32201044 PMCID: PMC7305953 DOI: 10.1016/j.biopsych.2020.01.016] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Revised: 11/30/2019] [Accepted: 01/21/2020] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.
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Affiliation(s)
- Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Memory Center, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany,Wallenberg Center for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Birkan Tunc
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Corey McMillan
- Department of Neurology and Penn FTD Center, University of Pennsylvania, Philadelphia, USA
| | - David A. Wolk
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, Philadelphia, USA
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12
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Ferreira D, Nordberg A, Westman E. Biological subtypes of Alzheimer disease: A systematic review and meta-analysis. Neurology 2020; 94:436-448. [PMID: 32047067 PMCID: PMC7238917 DOI: 10.1212/wnl.0000000000009058] [Citation(s) in RCA: 183] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 12/17/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To test the hypothesis that distinct subtypes of Alzheimer disease (AD) exist and underlie the heterogeneity within AD, we conducted a systematic review and meta-analysis on AD subtype studies based on postmortem and neuroimaging data. METHODS EMBASE, PubMed, and Web of Science databases were consulted until July 2019. RESULTS Neuropathology and neuroimaging studies have consistently identified 3 subtypes of AD based on the distribution of tau-related pathology and regional brain atrophy: typical, limbic-predominant, and hippocampal-sparing AD. A fourth subtype, minimal atrophy AD, has been identified in several neuroimaging studies. Typical AD displays tau-related pathology and atrophy both in hippocampus and association cortex and has a pooled frequency of 55%. Limbic-predominant, hippocampal-sparing, and minimal atrophy AD had a pooled frequency of 21%, 17%, and 15%, respectively. Between-subtype differences were found in age at onset, age at assessment, sex distribution, years of education, global cognitive status, disease duration, APOE ε4 genotype, and CSF biomarker levels. CONCLUSION We identified 2 core dimensions of heterogeneity: typicality and severity. We propose that these 2 dimensions determine individuals' belonging to one of the AD subtypes based on the combination of protective factors, risk factors, and concomitant non-AD brain pathologies. This model is envisioned to aid with framing hypotheses, study design, interpretation of results, and understanding mechanisms in future subtype studies. Our model can be used along the A/T/N classification scheme for AD biomarkers. Unraveling the heterogeneity within AD is critical for implementing precision medicine approaches and for ultimately developing successful disease-modifying drugs for AD.
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Affiliation(s)
- Daniel Ferreira
- From the Division of Clinical Geriatrics (D.F., A.N., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; Theme Aging (A.N.), Karolinska University Hospital, Huddinge, Sweden; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Agneta Nordberg
- From the Division of Clinical Geriatrics (D.F., A.N., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; Theme Aging (A.N.), Karolinska University Hospital, Huddinge, Sweden; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Eric Westman
- From the Division of Clinical Geriatrics (D.F., A.N., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; Theme Aging (A.N.), Karolinska University Hospital, Huddinge, Sweden; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Luo C, Li M, Qin R, Chen H, Yang D, Huang L, Liu R, Xu Y, Bai F, Zhao H. White Matter Microstructural Damage as an Early Sign of Subjective Cognitive Decline. Front Aging Neurosci 2020; 11:378. [PMID: 32047428 PMCID: PMC6997435 DOI: 10.3389/fnagi.2019.00378] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 12/23/2019] [Indexed: 01/21/2023] Open
Abstract
Background and Objective: Subjective cognitive decline (SCD) is considered a preclinical state of Alzheimer's disease (AD) and may represent a more advanced preclinical status than amnestic mild cognitive impairment (aMCI). Our aim was to explore changes in the white matter (WM) microstructure and their correlation with cognitive function in these AD-spectrum patients. Methods: Diffusion tensor images from 43 individuals with normal cognition (NC), 38 SCD patients, and 36 aMCI patients were compared using an atlas-based segmentation strategy. The correlation between diffusion parameters and cognitive function was further analyzed. Results: The anatomical pattern of WM impairment was generally similar between SCD and aMCI patients. However, aMCI patients showed significantly lower fractional anisotropy (i.e., corpus callosum forceps major and forceps minor) and increased mean diffusivity [i.e., bilateral anterior thalamic radiation (ATR), left corticospinal tract (CST), forceps minor, left cingulum (cingulate gyrus), left cingulum hippocampus, and left inferior fronto-occipital fasciculus (IFO)] in some tracts than did SCD subjects, indicating a disruption in WM microstructural integrity in the aMCI. Individuals with microstructural disruption in forceps minor, left cingulum (cingulate gyrus), and left cingulum hippocampus tracts performed worse in general cognition and memory function tests, as indicated by line regression analysis. Conclusion: SCD individuals had extensive WM microstructural damage in a pattern similar to that seen in aMCI, although presenting a cognitive performance comparable with that of cognitively healthy individuals. Our results suggest that WM integrity might precede objectively measurable memory decline and may be a potential early biomarker for AD.
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Affiliation(s)
- Caimei Luo
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Mengchun Li
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Dan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Lili Huang
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Renyuan Liu
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Nanjing University Medical School, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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