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Huang L, Fu Y, Zhang Y, Hu H, Ma L, Ge Y, Zhao Y, Zhang Y, Chen S, Feng J, Cheng W, Tan L, Yu J. Identifying modifiable factors associated with neuroimaging markers of brain health. CNS Neurosci Ther 2024; 30:e70057. [PMID: 39404063 PMCID: PMC11474882 DOI: 10.1111/cns.70057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/20/2024] [Accepted: 09/07/2024] [Indexed: 10/19/2024] Open
Abstract
AIMS Brain structural alterations begin long before the presentation of brain disorders; therefore, we aimed to systematically investigate a wide range of influencing factors on neuroimaging markers of brain health. METHODS Utilizing data from 30,651 participants from the UK Biobank, we explored associations between 218 modifiable factors and neuroimaging markers of brain health. We conducted an exposome-wide association study using the least absolute shrinkage and selection operator (LASSO) technique. Restricted cubic splines (RCS) were further employed to estimate potential nonlinear correlations. Weighted standardized scores for neuroimaging markers were computed based on the estimates for individual factors. Finally, stratum-specific analyses were performed to examine differences in factors affecting brain health at different ages. RESULTS The identified factors related to neuroimaging markers of brain health fell into six domains, including systematic diseases, lifestyle factors, personality traits, social support, anthropometric indicators, and biochemical markers. The explained variance percentage of neuroimaging markers by weighted standardized scores ranged from 0.5% to 7%. Notably, associations between systematic diseases and neuroimaging markers were stronger in older individuals than in younger ones. CONCLUSION This study identified a series of factors related to neuroimaging markers of brain health. Targeting the identified factors might help in formulating effective strategies for maintaining brain health.
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Affiliation(s)
- Liang‐Yu Huang
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Yan Fu
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Yi Zhang
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - He‐Ying Hu
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Ling‐Zhi Ma
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Yi‐Jun Ge
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Yong‐Li Zhao
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Ya‐Ru Zhang
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Shi‐Dong Chen
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jian‐Feng Feng
- Institute of Science and Technology for Brain‐Inspired Intelligence, Fudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University), Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
| | - Wei Cheng
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
- Institute of Science and Technology for Brain‐Inspired Intelligence, Fudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University), Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
| | - Lan Tan
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Jin‐Tai Yu
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
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Tian F, Wang Y, Qian ZM, Ran S, Zhang Z, Wang C, McMillin SE, Chavan NR, Lin H. Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia. Brain 2024:awae257. [PMID: 39324695 DOI: 10.1093/brain/awae257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/18/2024] [Accepted: 07/18/2024] [Indexed: 09/27/2024] Open
Abstract
Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.
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Affiliation(s)
- Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuhua Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Shanshan Ran
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | | | - Niraj R Chavan
- Department of Obstetrics, Gynecology and Women's Health, School of Medicine Saint Louis University, Saint Louis, MO 63117, USA
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
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3
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Morais-Ribeiro R, Almeida FC, Coelho A, Oliveira TG. Differential atrophy along the longitudinal hippocampal axis in Alzheimer's disease. Eur J Neurosci 2024; 59:3376-3388. [PMID: 38654447 DOI: 10.1111/ejn.16361] [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: 02/04/2023] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age and sex match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r = -.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r = .25). Overall, in AD, the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.
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Affiliation(s)
- Rafaela Morais-Ribeiro
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Francisco C Almeida
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Department of Neuroradiology, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Tiago Gil Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Division of Neuroradiology, Hospital de Braga, Braga, Portugal
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4
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Lan F, Roquet D, Dalton MA, El-Omar H, Ahmed RM, Piguet O, Irish M. Exploring graded profiles of hippocampal atrophy along the anterior-posterior axis in semantic dementia and Alzheimer's disease. Neurobiol Aging 2024; 135:70-78. [PMID: 38232501 DOI: 10.1016/j.neurobiolaging.2024.01.004] [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: 07/13/2023] [Revised: 01/07/2024] [Accepted: 01/09/2024] [Indexed: 01/19/2024]
Abstract
Mounting evidence indicates marked hippocampal degeneration in semantic dementia (SD) however, the spatial distribution of hippocampal atrophy profiles in this syndrome remains unclear. Using a recently developed parcellation approach, we extracted hippocampal volumes from four distinct subregions running from anterior to posterior along the longitudinal axis (anterior, intermediate rostral, intermediate caudal, and posterior). Volumetric differences in hippocampal subregions were compared between 21 SD, 24 matched Alzheimer's disease (AD), and 27 healthy older Control participants. Despite comparable overall hippocampal volume loss, SD and AD groups diverged in terms of the magnitude of atrophy along the anterior-posterior axis of the hippocampus. Global hippocampal atrophy was observed in AD, with no discernible gradation or lateralisation. In contrast, SD patients displayed graded bilateral hippocampal atrophy, most pronounced on the left-hand side, and concentrated in anterior relative to posterior subregions. Finally, we found preliminary evidence that disease-specific vulnerability along the anterior-posterior axis of the hippocampus was associated with canonical clinical features of these syndromes.
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Affiliation(s)
- Fang Lan
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Daniel Roquet
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Marshall A Dalton
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Hashim El-Omar
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia
| | - Rebekah M Ahmed
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; Memory and Cognition Clinic, Department of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Olivier Piguet
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia
| | - Muireann Irish
- The University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia; The University of Sydney, School of Psychology, Sydney, New South Wales, Australia.
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5
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Fan X, Liu Y, Li S, Yang Y, Zhao Y, Li W, Hao J, Xu Z, Zhang B, Liu W, Zhang S. Comprehensive landscape-style investigation of the molecular mechanism of acupuncture at ST36 single acupoint on different systemic diseases. Heliyon 2024; 10:e26270. [PMID: 38375243 PMCID: PMC10875596 DOI: 10.1016/j.heliyon.2024.e26270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/21/2024] Open
Abstract
The principle of acupoint stimulation efficacy is based on traditional meridian theory. However, the molecular mechanisms underlying the therapeutic effects of acupoints in treating diseases remain unclear in modern scientific understanding. In this study, we selected the ST36 acupoint for investigation and summarized all relevant literature from the PubMed database over the past 10 years. The results indicate that stimulation of ST36 single acupoints has therapeutic effects mainly in models of respiratory, neurological, digestive, endocrine and immune system diseases. And it can affect the inflammatory state, oxidative stress, respiratory mucus secretion, intestinal flora, immune cell function, neurotransmitter transmission, hormone secretion, the network of Interstitial Cells of Cajal (ICC) and glucose metabolism of the organism in these pathological states. Among them, acupuncture at the ST36 single point has the most prominent function in regulating the inflammatory state, which can mainly affect the activation of MAPK signaling pathway and drive the "molecular-cellular" mode involving macrophages, T-lymphocytes, mast cells (MCs) and neuroglial cells as the core to trigger the molecular level changes of the acupuncture point locally or in the target organ tissues, thereby establishing a multi-system, multi-target, multi-level molecular regulating mechanism. This article provides a comprehensive summary and discussion of the molecular mechanisms and effects of acupuncture at the ST36 acupoint, laying the groundwork for future in-depth research on acupuncture point theory.
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Affiliation(s)
- Xiaojing Fan
- The Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300250, China
| | - Yunlong Liu
- Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Shanshan Li
- Research Center of Experimental Acupuncture Science, Tianjin University of Chinese Medicine, Tianjin, 301617, China
| | - Yongrui Yang
- Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Yinghui Zhao
- Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Wenxi Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Jiaxin Hao
- Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Zhifang Xu
- Research Center of Experimental Acupuncture Science, Tianjin University of Chinese Medicine, Tianjin, 301617, China
| | - Bo Zhang
- Department of Automation, Tsinghua University, Institute for TCM-X, Beijing, 100084, China
| | - Wei Liu
- The First Affiliated Hospital of Hebei University of Chinese Medicine, Hebei Province Hospital of Chinese Medicine, Hebei Shijiazhuang, 050011, China
| | - Suzhao Zhang
- The First Affiliated Hospital of Hebei University of Chinese Medicine, Hebei Province Hospital of Chinese Medicine, Hebei Shijiazhuang, 050011, China
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6
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Yu H, Ding Y, Wei Y, Dyrba M, Wang D, Kang X, Xu W, Zhao K, Liu Y. Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell-type. Hum Brain Mapp 2023; 44:6364-6374. [PMID: 37846762 PMCID: PMC10681645 DOI: 10.1002/hbm.26512] [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: 05/18/2023] [Revised: 09/10/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most prevalent forms of dementia in older individuals. Convergent evidence suggests structural connectome abnormalities in specific brain regions are linked to AD progression. The biological basis underpinnings of these connectome changes, however, have remained elusive. We utilized an individual regional mean connectivity strength (RMCS) derived from a regional radiomics similarity network to capture altered morphological connectivity in 1654 participants (605 normal controls, 766 mild cognitive impairment [MCI], and 283 AD). Then, we also explored the biological basis behind these morphological changes through gene enrichment analysis and cell-specific analysis. We found that RMCS probes of the hippocampus and medial temporal lobe were significantly altered in AD and MCI, with these differences being spatially related to the expression of AD-risk genes. In addition, gene enrichment analysis revealed that the modulation of chemical synaptic transmission is the most relevant biological process associated with the altered RMCS in AD. Notably, neuronal cells were found to be the most pertinent cells in the altered RMCS. Our findings shed light on understanding the biological basis of structural connectome changes in AD, which may ultimately lead to more effective diagnostic and therapeutic strategies for this devastating disease.
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Affiliation(s)
- Huiying Yu
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Yanhui Ding
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
| | - Dong Wang
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Xiaopeng Kang
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Weizhi Xu
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Kun Zhao
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Yong Liu
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
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Zhao K, Chen P, Alexander-Bloch A, Wei Y, Dyrba M, Yang F, Kang X, Wang D, Fan D, Ye S, Tang Y, Yao H, Zhou B, Lu J, Yu C, Wang P, Liao Z, Chen Y, Huang L, Zhang X, Han Y, Li S, Liu Y. A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study. EClinicalMedicine 2023; 65:102276. [PMID: 37954904 PMCID: PMC10632687 DOI: 10.1016/j.eclinm.2023.102276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 11/14/2023] Open
Abstract
Background Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Methods Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. Findings IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p < 1 × 10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ (HR = 3.78 [95% CI: 2.63∼5.43], p = 2.13 × 10-14) and CSF Tau (HR = 3.77 [95% CI: 2.64∼5.39], p = 9.53 × 10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ (beta = -0.26, p = 4.40 × 10-9) and CSF Tau (beta = 0.12, p = 1.02 × 10-5). Interpretation Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials. Funding Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.
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Affiliation(s)
- Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, USA
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Martin Dyrba
- German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Fan Yang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, Beijing, China
- Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China
- Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Diseases, Beijing, China
| | - Shan Ye
- Department of Neurology, Peking University Third Hospital, Beijing, China
- Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China
- Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Diseases, Beijing, China
| | - Yi Tang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhengluan Liao
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Yan Chen
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Longjian Huang
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- National Clinical Research Centre for Geriatric Disorders, Beijing, China
- Centre of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China
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8
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Karat BG, DeKraker J, Hussain U, Köhler S, Khan AR. Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI. Hum Brain Mapp 2023; 44:5485-5503. [PMID: 37615057 PMCID: PMC10543110 DOI: 10.1002/hbm.26461] [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: 04/25/2023] [Revised: 06/12/2023] [Accepted: 08/06/2023] [Indexed: 08/25/2023] Open
Abstract
The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current magnetic resonance based neuroimaging techniques. In this work, we used diffusion magnetic resonance imaging (dMRI) and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the neurite orientation dispersion and density imaging (NODDI) model optimized for grey matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the cornu ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibres, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitectonic differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures were separable across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fibre pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo dMRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.
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Affiliation(s)
- Bradley G. Karat
- Robarts Research Institute, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonOntarioCanada
- Neuroscience Graduate ProgramUniversity of Western OntarioLondonOntarioCanada
| | - Jordan DeKraker
- Robarts Research Institute, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonOntarioCanada
- Montreal Neurological InstituteMcGill UniversityMontrealQuebecCanada
| | | | - Stefan Köhler
- Department of PsychologyUniversity of Western OntarioLondonOntarioCanada
| | - Ali R. Khan
- Robarts Research Institute, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonOntarioCanada
- Western Institute for NeuroscienceUniversity of Western OntarioLondonOntarioCanada
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Balajoo SM, Eickhoff SB, Masouleh SK, Plachti A, Waite L, Saberi A, Bahri MA, Bastin C, Salmon E, Hoffstaedter F, Palomero-Gallagher N, Genon S. Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles. Alzheimers Dement 2023; 19:4787-4804. [PMID: 37014937 PMCID: PMC10698199 DOI: 10.1002/alz.13056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 04/06/2023]
Abstract
INTRODUCTION Hippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD). METHODS We characterized the spatial patterns of hippocampus differentiation based on brain co-metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging. RESULTS The hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co-metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object-centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior-posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved. DISCUSSION Future studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.
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Affiliation(s)
- Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Shahrzad Kharabian Masouleh
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Laura Waite
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Amin Saberi
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM‑1), Research Centre Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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Plachti A, Latzman RD, Balajoo SM, Hoffstaedter F, Madsen KS, Baare W, Siebner HR, Eickhoff SB, Genon S. Hippocampal anterior- posterior shift in childhood and adolescence. Prog Neurobiol 2023; 225:102447. [PMID: 36967075 PMCID: PMC10185869 DOI: 10.1016/j.pneurobio.2023.102447] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023]
Abstract
Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.
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Affiliation(s)
- Anna Plachti
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Robert D Latzman
- Data Sciences Institute, Takeda Pharmaceutical, Cambridge, MA, USA
| | | | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Radiography, Department of Technology, University College Copenhagen, Copenhagen, Denmark
| | - William Baare
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium.
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11
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Vik A, Kociński M, Rye I, Lundervold AJ, Lundervold AS. Functional activity level reported by an informant is an early predictor of Alzheimer's disease. BMC Geriatr 2023; 23:205. [PMID: 37003981 PMCID: PMC10067216 DOI: 10.1186/s12877-023-03849-7] [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: 04/27/2022] [Accepted: 02/24/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer's disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. METHODS Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. RESULTS The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. CONCLUSION The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders.
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Affiliation(s)
- Alexandra Vik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marek Kociński
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ingrid Rye
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Alexander S Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway.
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12
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Zhang L, Hu X, Hu Y, Tang M, Qiu H, Zhu Z, Gao Y, Li H, Kuang W, Ji W. Structural covariance network of the hippocampus-amygdala complex in medication-naïve patients with first-episode major depressive disorder. PSYCHORADIOLOGY 2022; 2:190-198. [PMID: 38665275 PMCID: PMC10917195 DOI: 10.1093/psyrad/kkac023] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/05/2022] [Accepted: 12/14/2022] [Indexed: 04/28/2024]
Abstract
Background The hippocampus and amygdala are densely interconnected structures that work together in multiple affective and cognitive processes that are important to the etiology of major depressive disorder (MDD). Each of these structures consists of several heterogeneous subfields. We aim to explore the topologic properties of the volume-based intrinsic network within the hippocampus-amygdala complex in medication-naïve patients with first-episode MDD. Methods High-resolution T1-weighted magnetic resonance imaging scans were acquired from 123 first-episode, medication-naïve, and noncomorbid MDD patients and 81 age-, sex-, and education level-matched healthy control participants (HCs). The structural covariance network (SCN) was constructed for each group using the volumes of the hippocampal subfields and amygdala subregions; the weights of the edges were defined by the partial correlation coefficients between each pair of subfields/subregions, controlled for age, sex, education level, and intracranial volume. The global and nodal graph metrics were calculated and compared between groups. Results Compared with HCs, the SCN within the hippocampus-amygdala complex in patients with MDD showed a shortened mean characteristic path length, reduced modularity, and reduced small-worldness index. At the nodal level, the left hippocampal tail showed increased measures of centrality, segregation, and integration, while nodes in the left amygdala showed decreased measures of centrality, segregation, and integration in patients with MDD compared with HCs. Conclusion Our results provide the first evidence of atypical topologic characteristics within the hippocampus-amygdala complex in patients with MDD using structure network analysis. It provides more delineate mechanism of those two structures that underlying neuropathologic process in MDD.
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Affiliation(s)
- Lianqing Zhang
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Xinyue Hu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Yongbo Hu
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Mengyue Tang
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Hui Qiu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Ziyu Zhu
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Yingxue Gao
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Hailong Li
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Weidong Ji
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science and Affiliated Mental Health Center, East China Normal University, Shanghai 200335, China
- Child Psychiatry, Shanghai Changning Mental Health Center, Shanghai 200335, China
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13
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de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, Yushkevich PA, Wolk DA. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci 2022; 42:2131-2141. [PMID: 35086906 PMCID: PMC8916768 DOI: 10.1523/jneurosci.0949-21.2021] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/21/2022] Open
Abstract
The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.
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Affiliation(s)
- Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Université de Caen Normandie, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique (UMRS) Unité 1237, Caen 14000, France
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Diagnostic Radiology, Lund University, Lund 22185, Sweden
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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Genon S, Bernhardt BC, La Joie R, Amunts K, Eickhoff SB. The many dimensions of human hippocampal organization and (dys)function. Trends Neurosci 2021; 44:977-989. [PMID: 34756460 DOI: 10.1016/j.tins.2021.10.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/06/2021] [Accepted: 10/05/2021] [Indexed: 11/19/2022]
Abstract
The internal organization of hippocampal formation has been studied for more than a century. Although early accounts emphasized its subfields along the medial-lateral axis, findings in recent decades have highlighted also the anterior-to-posterior (i.e., longitudinal) axis as a key contributor to this brain region's functional organization. Hence, understanding of hippocampal function likely demands characterizing both medial-to-lateral and anterior-to-posterior axes, an approach that has been concretized by recent advances in in vivo parcellation and gradient mapping techniques. Following a short historical overview, we review the evidence provided by these approaches in brain-mapping studies, as well as the perspectives they open for addressing the behavioral relevance of the interacting organizational axes in healthy and clinical populations.
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Affiliation(s)
- Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
| | | | - Renaud La Joie
- Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, Structural and Functional Organisation of the Brain (INM-1), Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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