1
|
Kim CM, Diez I, Bueichekú E, Ahn S, Montal V, Sepulcre J. Spatiotemporal Correlation between Amyloid and Tau Accumulations Underlies Cognitive Changes in Aging. J Neurosci 2024; 44:e0488232023. [PMID: 38123362 PMCID: PMC10869152 DOI: 10.1523/jneurosci.0488-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 10/03/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023] Open
Abstract
It is poorly known how Aβ and tau accumulations associate at the spatiotemporal level in the in vivo human brain to impact cognitive changes in older adults prior to AD symptoms onset. In this study, we used a graph theory-based spatiotemporal analysis to characterize the cortical patterns of Aβ and tau deposits and their relationship with cognitive changes in the Harvard Aging Brain Study (HABS) cohort. We found that the temporal accumulations of interlinked Aβ and tau pathology display distinctive spatiotemporal correlations associated with early cognitive decline. Notably, we observed that baseline Aβ deposits-Thal amyloid phase Ⅱ-related to future increase of tau deposits, Braak stages Ⅰ-Ⅳ, both displaying linkage to the decline in multi-domain cognitive scores. We also found unimodal tau-to-tau and cognitive impairment associations in broad areas of Braak stages Ⅰ-Ⅳ. The unimodal Aβ-to-Aβ progressions were not associated with cognitive changes. Our results revealed a multifaceted correlation of the spatiotemporal Aβ and tau associations with cognitive decline over time, in which tau-to-tau and tau-Aβ interactions, and not Aβ independently, might be critical contributors to clinical trajectories toward AD in older adults.
Collapse
Affiliation(s)
- Chan-Mi Kim
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown 02129, Massachusetts
| | - Elisenda Bueichekú
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
| | - Sung Ahn
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
| | - Victor Montal
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona, Barcelona 08041, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid 28029, Spain
| | - Jorge Sepulcre
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown 02129, Massachusetts
| |
Collapse
|
2
|
Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
Collapse
Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | |
Collapse
|
3
|
Belasso CJ, Cai Z, Bezgin G, Pascoal T, Stevenson J, Rahmouni N, Tissot C, Lussier F, Rosa-Neto P, Soucy JP, Rivaz H, Benali H. Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum. Front Aging Neurosci 2023; 15:1225816. [PMID: 37920382 PMCID: PMC10619155 DOI: 10.3389/fnagi.2023.1225816] [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: 05/19/2023] [Accepted: 09/26/2023] [Indexed: 11/04/2023] Open
Abstract
Background Alzheimer's disease (AD) diagnosis in its early stages remains difficult with current diagnostic approaches. Though tau neurofibrillary tangles (NFTs) generally follow the stereotypical pattern described by the Braak staging scheme, the network degeneration hypothesis (NDH) has suggested that NFTs spread selectively along functional networks of the brain. To evaluate this, we implemented a Bayesian workflow to develop hierarchical multinomial logistic regression models with increasing levels of complexity of the brain from tau-PET and structural MRI data to investigate whether it is beneficial to incorporate network-level information into an ROI-based predictive model for the presence/absence of AD. Methods This study included data from the Translational Biomarkers in Aging and Dementia (TRIAD) longitudinal cohort from McGill University's Research Centre for Studies in Aging (MCSA). Baseline and 1 year follow-up structural MRI and [18F]MK-6240 tau-PET scans were acquired for 72 cognitive normal (CN), 23 mild cognitive impairment (MCI), and 18 Alzheimer's disease dementia subjects. We constructed the four following hierarchical Bayesian models in order of increasing complexity: (Model 1) a complete-pooling model with observations, (Model 2) a partial-pooling model with observations clustered within ROIs, (Model 3) a partial-pooling model with observations clustered within functional networks, and (Model 4) a partial-pooling model with observations clustered within ROIs that are also clustered within functional brain networks. We then investigated which of the models had better predictive performance given tau-PET or structural MRI data as an input, in the form of a relative annualized rate of change. Results The Bayesian leave-one-out cross-validation (LOO-CV) estimate of the expected log pointwise predictive density (ELPD) results indicated that models 3 and 4 were substantially better than other models for both tau-PET and structural MRI inputs. For tau-PET data, model 3 was slightly better than 4 with an absolute difference in ELPD of 3.10 ± 1.30. For structural MRI data, model 4 was considerably better than other models with an absolute difference in ELPD of 29.83 ± 7.55 relative to model 3, the second-best model. Conclusion Our results suggest that representing the data generating process in terms of a hierarchical model that encompasses both ROI-level and network-level heterogeneity leads to better predictive ability for both tau-PET and structural MRI inputs over all other model iterations.
Collapse
Affiliation(s)
- Clyde J. Belasso
- Department of Electrical and Computer Engineering, Concordia University, Montréal, QC, Canada
- PERFORM Centre, Concordia University, Montréal, QC, Canada
| | - Zhengchen Cai
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada
| | - Gleb Bezgin
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Tharick Pascoal
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada
| | - Firoza Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jean-Paul Soucy
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada
| | - Hassan Rivaz
- Department of Electrical and Computer Engineering, Concordia University, Montréal, QC, Canada
- PERFORM Centre, Concordia University, Montréal, QC, Canada
| | - Habib Benali
- Department of Electrical and Computer Engineering, Concordia University, Montréal, QC, Canada
- PERFORM Centre, Concordia University, Montréal, QC, Canada
| |
Collapse
|
4
|
Yang Z, Chen Y, Hou X, Xu Y, Bai F. Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review. Heliyon 2023; 9:e15389. [PMID: 37101638 PMCID: PMC10123263 DOI: 10.1016/j.heliyon.2023.e15389] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
Collapse
Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing 210008, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
- Correspondence to: 321 Zhongshan Road, Nanjing, 210008, China.
| |
Collapse
|
5
|
Protas H, Ghisays V, Goradia DD, Bauer R, Devadas V, Chen K, Reiman EM, Su Y. Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer's disease continuum. Front Neurosci 2023; 17:1089134. [PMID: 36937677 PMCID: PMC10017746 DOI: 10.3389/fnins.2023.1089134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction Tau PET imaging has emerged as an important tool to detect and monitor tangle burden in vivo in the study of Alzheimer's disease (AD). Previous studies demonstrated the association of tau burden with cognitive decline in probable AD cohorts. This study introduces a novel approach to analyze tau PET data by constructing individualized tau network structure and deriving its graph theory-based measures. We hypothesize that the network- based measures are a measure of the total tau load and the stage through disease. Methods Using tau PET data from the AD Neuroimaging Initiative from 369 participants, we determine the network measures, global efficiency, global strength, and limbic strength, and compare with two regional measures entorhinal and tau composite SUVR, in the ability to differentiate, cognitively unimpaired (CU), MCI and AD. We also investigate the correlation of these network and regional measures and a measure of memory performance, auditory verbal learning test for long-term recall memory (AVLT-LTM). Finally, we determine the stages based on global efficiency and limbic strength using conditional inference trees and compare with Braak staging. Results We demonstrate that the derived network measures are able to differentiate three clinical stages of AD, CU, MCI, and AD. We also demonstrate that these network measures are strongly correlated with memory performance overall. Unlike regional tau measurements, the tau network measures were significantly associated with AVLT-LTM even in cognitively unimpaired individuals. Stages determined from global efficiency and limbic strength, visually resembled Braak staging. Discussion The strong correlations with memory particularly in CU suggest the proposed technique may be used to characterize subtle early tau accumulation. Further investigation is ongoing to examine this technique in a longitudinal setting.
Collapse
Affiliation(s)
- Hillary Protas
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Valentina Ghisays
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Dhruman D. Goradia
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Robert Bauer
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Vivek Devadas
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Neurology, The University of Arizona, Tucson, AZ, United States
- Department of Psychiatry, The University of Arizona, Tucson, AZ, United States
- Department of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Neurology, The University of Arizona, Tucson, AZ, United States
- Department of Psychiatry, The University of Arizona, Tucson, AZ, United States
- Department of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United States
- Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| |
Collapse
|
6
|
Ghasemi-Tarie R, Kiasalari Z, Fakour M, Khorasani M, Keshtkar S, Baluchnejadmojarad T, Roghani M. Nobiletin prevents amyloid β 1-40-induced cognitive impairment via inhibition of neuroinflammation and oxidative/nitrosative stress. Metab Brain Dis 2022; 37:1337-1349. [PMID: 35294678 DOI: 10.1007/s11011-022-00949-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 03/01/2022] [Indexed: 01/17/2023]
Abstract
Alzheimer's disease (AD) is presented as an age-related neurodegenerative disease with multiple cognitive deficits and amyloid β (Aβ) accumulation is the most important involved factor in its development. Nobiletin is a bioflavonoid isolated from citrus fruits peels with anti-inflammatory and anti-oxidative activity as well as anti-dementia property that has shown potency to ameliorate intracellular and extracellular Ab. The aim of the present study was to assess protective effect of nobiletin against Aβ1-40-induced cognitive impairment as a consistent model of AD. After bilateral intrahippocampal (CA1 subfield) injection of Aβ1-40, rats were treated with nobiletin (10 mg/kg/day; p.o.) from stereotaxic surgery day (day 0) till day + 7. Cognition function was evaluated in a battery of behavioral tasks at week 3 with final assessment of hippocampal oxidative stress and inflammation besides Nissl staining and 3-nitrotyrosine (3-NT) immunohistochemistry. Analysis of behavioral data showed notable and significant improvement of alternation in Y maze test, discrimination ratio in novel object recognition task, and step through latency in passive avoidance test in nobiletin-treated Aβ group. Additionally, nobiletin treatment was associated with lower hippocampal levels of MDA and ROS and partial reversal of SOD activity and also improvement of Nrf2 with no significant effect on GSH and catalase. Furthermore, nobiletin attenuated hippocampal neuroinflammation in Aβ group as shown by lower tissue levels of TLR4, NF-kB, and TNFa. Histochemical findings showed that nobiletin prevents CA1 neuronal loss in Nissl staining in addition to its alleviation of 3-nitrotyrosine (3-NT) immunoreactivity as a marker of nitrosative stress. Collectively, these findings indicated neuroprotective and anti-dementia potential of nobiletin that is partly attributed to its anti-oxidative, anti-nitrosative, and anti-inflammatory property associated with proper modulation of TLR4/NF-kB/Nrf2 pathways.
Collapse
Affiliation(s)
| | - Zahra Kiasalari
- Neurophysiology Research Center, Department of Physiology, Shahed University, Tehran, Iran
| | - Marzieh Fakour
- Department of Physiology, School of Medicine, Shahed University, Tehran, Iran.
| | - Maryam Khorasani
- Department of Physiology, School of Medicine, Shahed University, Tehran, Iran
| | - Sedigheh Keshtkar
- Department of Physiology, School of Medicine, Shahed University, Tehran, Iran
| | | | - Mehrdad Roghani
- Neurophysiology Research Center, Department of Physiology, Shahed University, Tehran, Iran.
| |
Collapse
|
7
|
Kim CM, Montal V, Diez I, Orwig W, Sepulcre J. Network interdigitations of Tau and amyloid-beta deposits define cognitive levels in aging. Hum Brain Mapp 2021; 42:2990-3004. [PMID: 33955621 PMCID: PMC8193537 DOI: 10.1002/hbm.25350] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/16/2020] [Accepted: 01/11/2021] [Indexed: 12/18/2022] Open
Abstract
Amyloid‐beta (Aβ) plaques and tau neurofibrillary tangles are pathological hallmarks of Alzheimer's disease (AD); their contribution to neurodegeneration and clinical manifestations are critical in understanding preclinical AD. At present, the mechanisms related to Aβ and tau pathogenesis leading to cognitive decline in older adults remain largely unknown. Here, we examined graph theory‐based positron emission tomography (PET) analytical approaches, within and between tau and Aβ PET modalities, and tested the effects on cognitive changes in cognitively normal older adults (CN). Particularly, we focused on the network interdigitations of Aβ and tau deposits, along with cognitive test scores in CN at both baseline and 2‐year follow‐up (FU). We found highly significant Aβ‐tau network integrations in AD vulnerable areas, as well as significant associations between those Aβ‐tau interdigitations and general cognitive impairment in CN at baseline and FU. Our findings suggest a distinctive contribution of interlinking network relationships between Aβ and tau deposits in heteromodal areas of the human brain. They support a network‐based interaction between Aβ and tau accumulations as a key factor for cognitive deterioration in CN prior to dementia.
Collapse
Affiliation(s)
- Chan-Mi Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Victor Montal
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain.,Centro de Investigacón Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - William Orwig
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | | | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| |
Collapse
|