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Xue L, Fu Y, Gao X, Feng G, Qian S, Wei L, Li L, Zhuo C, Zhang H, Tian M. [ 18F]FDG PET integrated with structural MRI for accurate brain age prediction. Eur J Nucl Med Mol Imaging 2024; 51:3617-3629. [PMID: 38839623 DOI: 10.1007/s00259-024-06784-w] [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: 01/29/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.
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Affiliation(s)
- Le Xue
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Gang Feng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Shufang Qian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Ling Wei
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China
| | - Lanlan Li
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China
| | - Cheng Zhuo
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China.
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, Zhejiang, China.
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
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2
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Casanova R, Walker KA, Justice JN, Anderson A, Duggan MR, Cordon J, Barnard RT, Lu L, Hsu FC, Sedaghat S, Prizment A, Kritchevsky SB, Wagenknecht LE, Hughes TM. Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort. GeroScience 2024; 46:3861-3873. [PMID: 38438772 PMCID: PMC11226584 DOI: 10.1007/s11357-024-01112-4] [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: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA.
| | | | - Jamie N Justice
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Andrea Anderson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | | | | | - Ryan T Barnard
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Lingyi Lu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC, USA
| | - Sanaz Sedaghat
- School of Public Health, Oncology and Transplantation, University of Minnesota, Minneapolis, MN, USA
| | - Anna Prizment
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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3
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Herbet G, Duffau H, Mandonnet E. Predictors of cognition after glioma surgery: connectotomy, structure-function phenotype, plasticity. Brain 2024; 147:2621-2635. [PMID: 38573324 DOI: 10.1093/brain/awae093] [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: 12/02/2023] [Revised: 02/19/2024] [Accepted: 03/09/2024] [Indexed: 04/05/2024] Open
Abstract
Determining preoperatively the maximal extent of resection that would preserve cognitive functions is the core challenge of brain tumour surgery. Over the past decade, the methodological framework to achieve this goal has been thoroughly renewed: the population-level topographically-focused voxel-based lesion-symptom mapping has been progressively overshadowed by machine learning (ML) algorithmics, in which the problem is framed as predicting cognitive outcomes in a patient-specific manner from a typically large set of variables. However, the choice of these predictors is of utmost importance, as they should be both informative and parsimonious. In this perspective, we first introduce the concept of connectotomy: instead of parameterizing resection topography through the status (intact/resected) of a huge number of voxels (or parcels) paving the whole brain in the Cartesian 3D-space, the connectotomy models the resection in the connectivity space, by computing a handful number of networks disconnection indices, measuring how the structural connectivity sustaining each network of interest was hit by the resection. This connectivity-informed reduction of dimensionality is a necessary step for efficiently implementing ML tools, given the relatively small number of patient-examples in available training datasets. We further argue that two other major sources of interindividual variability must be considered to improve the accuracy with which outcomes are predicted: the underlying structure-function phenotype and neuroplasticity, for which we provide an in-depth review and propose new ways of determining relevant predictors. We finally discuss the benefits of our approach for precision surgery of glioma.
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Affiliation(s)
- Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Praxiling lab, UMR5267 CNRS & Paul Valéry University, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Institut Universitaire de France, Paris 75000, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier 34090, France
- Department of Medicine, University of Montpellier, Montpellier 34090, France
- Team 'Plasticity of Central Nervous System, Stem Cells and Glial Tumors', U1191 Laboratory, Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM), University of Montpellier, Montpellier 34000, France
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, AP-HP, Paris 75010, France
- Frontlab, CNRS UMR 7225, INSERM U1127, Paris Brain Institute (ICM), Paris 75013, France
- Université de Paris Cité, UFR de médecine, Paris 75005, France
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4
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Arenaza‐Urquijo EM, Boyle R, Casaletto K, Anstey KJ, Vila‐Castelar C, Colverson A, Palpatzis E, Eissman JM, Kheng Siang Ng T, Raghavan S, Akinci M, Vonk JMJ, Machado LS, Zanwar PP, Shrestha HL, Wagner M, Tamburin S, Sohrabi HR, Loi S, Bartrés‐Faz D, Dubal DB, Vemuri P, Okonkwo O, Hohman TJ, Ewers M, Buckley RF. Sex and gender differences in cognitive resilience to aging and Alzheimer's disease. Alzheimers Dement 2024; 20:5695-5719. [PMID: 38967222 PMCID: PMC11350140 DOI: 10.1002/alz.13844] [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: 11/08/2023] [Revised: 03/08/2024] [Accepted: 03/21/2024] [Indexed: 07/06/2024]
Abstract
Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.
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Affiliation(s)
- Eider M. Arenaza‐Urquijo
- Environment and Health Over the Life Course Programme, Climate, Air Pollution, Nature and Urban Health ProgrammeBarcelona Institute for Global Health (ISGlobal)BarcelonaSpain
- University of Pompeu FabraBarcelonaBarcelonaSpain
| | - Rory Boyle
- Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Kaitlin Casaletto
- Department of NeurologyMemory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Kaarin J. Anstey
- University of New South Wales Ageing Futures InstituteSydneyNew South WalesAustralia
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of Psychology, University of New South WalesSidneyNew South WalesAustralia
| | | | - Aaron Colverson
- University of Florida Center for Arts in Medicine Interdisciplinary Research LabUniversity of Florida, Center of Arts in MedicineGainesvilleFloridaUSA
| | - Eleni Palpatzis
- Environment and Health Over the Life Course Programme, Climate, Air Pollution, Nature and Urban Health ProgrammeBarcelona Institute for Global Health (ISGlobal)BarcelonaSpain
- University of Pompeu FabraBarcelonaBarcelonaSpain
| | - Jaclyn M. Eissman
- Vanderbilt Memory and Alzheimer's Center, Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Ted Kheng Siang Ng
- Rush Institute for Healthy Aging and Department of Internal MedicineRush University Medical CenterChicagoIllinoisUSA
| | | | - Muge Akinci
- Environment and Health Over the Life Course Programme, Climate, Air Pollution, Nature and Urban Health ProgrammeBarcelona Institute for Global Health (ISGlobal)BarcelonaSpain
- University of Pompeu FabraBarcelonaBarcelonaSpain
| | - Jet M. J. Vonk
- Department of NeurologyMemory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Luiza S. Machado
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal Do Rio Grande Do Sul, FarroupilhaPorto AlegreBrazil
| | - Preeti P. Zanwar
- Jefferson College of Population Health, Thomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
- The Network on Life Course and Health Dynamics and Disparities, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Maude Wagner
- Rush Alzheimer's Disease Center, Rush University Medical CenterChicagoIllinoisUSA
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement SciencesUniversity of VeronaVeronaItaly
| | - Hamid R. Sohrabi
- Centre for Healthy AgeingHealth Future InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- School of Psychology, Murdoch UniversityMurdochWestern AustraliaAustralia
| | - Samantha Loi
- Neuropsychiatry Centre, Royal Melbourne HospitalParkvilleVictoriaAustralia
- Department of PsychiatryUniversity of MelbourneParkvilleVictoriaAustralia
| | - David Bartrés‐Faz
- Department of MedicineFaculty of Medicine and Health Sciences & Institut de NeurociènciesUniversity of BarcelonaBarcelonaBarcelonaSpain
- Institut d'Investigacions Biomèdiques (IDIBAPS)BarcelonaBarcelonaSpain
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de BarcelonaBadalonaBarcelonaSpain
| | - Dena B. Dubal
- Department of Neurology and Weill Institute of NeurosciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Biomedical and Neurosciences Graduate ProgramsUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Ozioma Okonkwo
- Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's Center, Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Michael Ewers
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig Maximilians Universität (LMU)MunichGermany
- German Center for Neurodegenerative Diseases (DZNE, Munich)MunichGermany
| | - Rachel F. Buckley
- Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
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5
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Yu Y, Cui H, Haas SS, New F, Sanford N, Yu K, Zhan D, Yang G, Gao J, Wei D, Qiu J, Banaj N, Boomsma DI, Breier A, Brodaty H, Buckner RL, Buitelaar JK, Cannon DM, Caseras X, Clark VP, Conrod PJ, Crivello F, Crone EA, Dannlowski U, Davey CG, de Haan L, de Zubicaray GI, Di Giorgio A, Fisch L, Fisher SE, Franke B, Glahn DC, Grotegerd D, Gruber O, Gur RE, Gur RC, Hahn T, Harrison BJ, Hatton S, Hickie IB, Hulshoff Pol HE, Jamieson AJ, Jernigan TL, Jiang J, Kalnin AJ, Kang S, Kochan NA, Kraus A, Lagopoulos J, Lazaro L, McDonald BC, McDonald C, McMahon KL, Mwangi B, Piras F, Rodriguez‐Cruces R, Royer J, Sachdev PS, Satterthwaite TD, Saykin AJ, Schumann G, Sevaggi P, Smoller JW, Soares JC, Spalletta G, Tamnes CK, Trollor JN, Van't Ent D, Vecchio D, Walter H, Wang Y, Weber B, Wen W, Wierenga LM, Williams SCR, Wu M, Zunta‐Soares GB, Bernhardt B, Thompson P, Frangou S, Ge R. Brain-age prediction: Systematic evaluation of site effects, and sample age range and size. Hum Brain Mapp 2024; 45:e26768. [PMID: 38949537 PMCID: PMC11215839 DOI: 10.1002/hbm.26768] [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: 12/19/2023] [Revised: 05/15/2024] [Accepted: 06/10/2024] [Indexed: 07/02/2024] Open
Abstract
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.
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Affiliation(s)
- Yuetong Yu
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Hao‐Qi Cui
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Shalaila S. Haas
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Faye New
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Nicole Sanford
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Kevin Yu
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Denghuang Zhan
- School of Population and Public HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Guoyuan Yang
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life ScienceBeijing Institute of TechnologyBeijingChina
| | - Jia‐Hong Gao
- Center for MRI ResearchPeking UniversityBeijingChina
| | - Dongtao Wei
- School of PsychologySouthwest UniversityChongqingChina
| | - Jiang Qiu
- School of PsychologySouthwest UniversityChongqingChina
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Dorret I. Boomsma
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Alan Breier
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Randy L. Buckner
- Department of Psychology, Center for Brain ScienceHarvard UniversityBostonMassachusettsUSA
- Department of Psychiatry, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan K. Buitelaar
- Department of Cognitive NeuroscienceDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Dara M. Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Xavier Caseras
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical NeurosciencesCardiff UniversityCardiffUK
| | - Vincent P. Clark
- Psychology Clinical Neuroscience Center, Department of PsychologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Patricia J. Conrod
- Department of Psychiatry and AddictionUniversité de Montréal, CHU Ste JustineMontrealQuebecCanada
| | - Fabrice Crivello
- Institut des Maladies NeurodégénérativesUniversité de BordeauxBordeauxFrance
| | - Eveline A. Crone
- Department of Psychology, Faculty of Social SciencesLeiden UniversityLeidenThe Netherlands
- Erasmus School of Social and Behavioral SciencesErasmus University RotterdamRotterdamThe Netherlands
| | - Udo Dannlowski
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | | | - Lieuwe de Haan
- Department of PsychiatryAmsterdam UMCAmsterdamThe Netherlands
| | - Greig I. de Zubicaray
- Faculty of Health, School of Psychology & CounsellingQueensland University of TechnologyBrisbaneQueenslandAustralia
| | | | - Lukas Fisch
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Simon E. Fisher
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Barbara Franke
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
| | - David C. Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tim Hahn
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ben J. Harrison
- Department of PsychiatryThe University of MelbourneMelbourneVictoriaAustralia
| | - Sean Hatton
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Ian B. Hickie
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Hilleke E. Hulshoff Pol
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of PsychologyUtrecht UniversityUtrechtThe Netherlands
- Department of PsychiatryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Alec J. Jamieson
- Department of PsychiatryThe University of MelbourneMelbourneVictoriaAustralia
| | - Terry L. Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and RadiologyUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Andrew J. Kalnin
- Department of RadiologyThe Ohio State University College of MedicineColumbusOhioUSA
| | - Sim Kang
- West Region, Institute of Mental HealthSingaporeSingapore
| | - Nicole A. Kochan
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Anna Kraus
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Jim Lagopoulos
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and PsychologyHospital Clínic, IDIBAPS, CIBERSAM, University of BarcelonaBarcelonaSpain
| | - Brenna C. McDonald
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Katie L. McMahon
- School of Clinical Sciences, Centre for Biomedical TechnologiesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | | | - Jessica Royer
- McConnell Brain Imaging CentreMcGill UniversityMontrealQuebecCanada
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | | | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Gunter Schumann
- Department of PsychiatryCCM, Charite Universitaetsmedizin BerlinBerlinGermany
- Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBIFudan UniversityShanghaiChina
| | - Pierluigi Sevaggi
- Department of Translational Biomedicine and NeuroscienceUniversity of Bari Aldo MoroBariItaly
| | - Jordan W. Smoller
- Department of Psychiatry, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Genomic MedicineMassachusetts General HospitalBostonMassachusettsUSA
- Center for Precision PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Jair C. Soares
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Christian K. Tamnes
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
- Department of Developmental Disability Neuropsychiatry, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Dennis Van't Ent
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin BerlinCorporate Member of FU Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Yang Wang
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition ResearchUniversity of Bonn and University Hospital BonnBonnGermany
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Lara M. Wierenga
- Department of Psychology, Faculty of Social SciencesLeiden UniversityLeidenThe Netherlands
| | - Steven C. R. Williams
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Mon‐Ju Wu
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Giovana B. Zunta‐Soares
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Boris Bernhardt
- McConnell Brain Imaging CentreMcGill UniversityMontrealQuebecCanada
| | - Paul Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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6
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Burmistrov DE, Gudkov SV, Franceschi C, Vedunova MV. Sex as a Determinant of Age-Related Changes in the Brain. Int J Mol Sci 2024; 25:7122. [PMID: 39000227 PMCID: PMC11241365 DOI: 10.3390/ijms25137122] [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: 05/20/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
The notion of notable anatomical, biochemical, and behavioral distinctions within male and female brains has been a contentious topic of interest within the scientific community over several decades. Advancements in neuroimaging and molecular biological techniques have increasingly elucidated common mechanisms characterizing brain aging while also revealing disparities between sexes in these processes. Variations in cognitive functions; susceptibility to and progression of neurodegenerative conditions, notably Alzheimer's and Parkinson's diseases; and notable disparities in life expectancy between sexes, underscore the significance of evaluating aging within the framework of gender differences. This comprehensive review surveys contemporary literature on the restructuring of brain structures and fundamental processes unfolding in the aging brain at cellular and molecular levels, with a focus on gender distinctions. Additionally, the review delves into age-related cognitive alterations, exploring factors influencing the acceleration or deceleration of aging, with particular attention to estrogen's hormonal support of the central nervous system.
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Affiliation(s)
- Dmitriy E. Burmistrov
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilova St., 119991 Moscow, Russia;
| | - Sergey V. Gudkov
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilova St., 119991 Moscow, Russia;
- Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603022 Nizhny Novgorod, Russia
| | - Claudio Franceschi
- Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603022 Nizhny Novgorod, Russia
| | - Maria V. Vedunova
- Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603022 Nizhny Novgorod, Russia
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7
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Weng Y, Murphy CT. Male-specific behavioral and transcriptomic changes in aging C. elegans neurons. iScience 2024; 27:109910. [PMID: 38783998 PMCID: PMC11111838 DOI: 10.1016/j.isci.2024.109910] [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: 12/18/2023] [Revised: 03/20/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Aging is a complex biological process with sexually dimorphic aspects. Although cognitive aging of Caenorhabditis elegans hermaphrodites has been studied, less is known about cognitive decline in males. We found that cognitive aging has both sex-shared and sex-dimorphic characteristics, and we identified neuron-specific age-associated sex-differential targets. In addition to sex-shared neuronal aging genes, males differentially downregulate mitochondrial metabolic genes and upregulate GPCR genes with age, while the X chromosome exhibits increased gene expression in hermaphrodites and altered dosage compensation complex expression with age, indicating possible X chromosome dysregulation that contributes to sexual dimorphism in cognitive aging. Finally, the sex-differentially expressed gene hrg-7, an aspartic-type endopeptidase, regulates male cognitive aging but does not affect hermaphrodites' behaviors. These results suggest that males and hermaphrodites exhibit different age-related neuronal changes. This study will strengthen our understanding of sex-specific vulnerability and resilience and identify pathways to target with treatments that could benefit both sexes.
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Affiliation(s)
- Yifei Weng
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
- LSI Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Coleen T. Murphy
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
- LSI Genomics, Princeton University, Princeton, NJ 08544, USA
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8
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Wittens MMJ, Denissen S, Sima DM, Fransen E, Niemantsverdriet E, Bastin C, Benoit F, Bergmans B, Bier JC, de Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Picard G, Ribbens A, Salmon E, Segers K, Sieben A, Struyfs H, Thiery E, Tournoy J, van Binst AM, Versijpt J, Smeets D, Bjerke M, Nagels G, Engelborghs S. Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study. Alzheimers Res Ther 2024; 16:128. [PMID: 38877568 PMCID: PMC11179390 DOI: 10.1186/s13195-024-01491-y] [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: 04/02/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVES This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. METHODS The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject. RESULTS MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. CONCLUSIONS Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.
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Affiliation(s)
- Mandy M J Wittens
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Stijn Denissen
- icometrix, Leuven, Belgium
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Diana M Sima
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Erik Fransen
- Centre of Medical Genetics, University of Antwerp, and Antwerp University Hospital - UZA, Edegem, Belgium
| | | | - Christine Bastin
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
| | - Florence Benoit
- Geriatrics Department, Brugmann University Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Bruno Bergmans
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Jean-Christophe Bier
- Neurological department H. U. B. - Erasme Hospital - Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Peter Paul de Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Antwerp, 2610, Belgium
- Memory Clinic, Ziekenhuisnetwerk, Antwerp, Belgium
| | - Olivier Deryck
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, 1200, Belgium
- Department of Neurology, Clinique Universitaires Saint-Luc, Brussels, 1200, Belgium
- WELBIO Department, WEL Research Institute, Wavre, 1300, Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc, and Institute of Neuroscience, Université Catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
- Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Memory Clinic - Neurology and Geriatrics Department, CHU Brugmann, Van Gehuchtenplein 4, Brussels, 1020, Belgium
| | - Anne Sieben
- Neuropathology Lab, IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Pathology, Antwerp University Hospital - UZA, Antwerp, Belgium
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Department of Chronic Diseases, Metabolism and Ageing, Geriatric Medicine and Memory Clinic, University Hospitals Leuven and KU Leuven, Louvain, Belgium
| | - Anne-Marie van Binst
- Radiology Department, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Dirk Smeets
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Maria Bjerke
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- Department of Clinical Chemistry, Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- St. Edmund Hall, University of Oxford, Oxford, UK
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium.
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Lopez-Lee C, Torres ERS, Carling G, Gan L. Mechanisms of sex differences in Alzheimer's disease. Neuron 2024; 112:1208-1221. [PMID: 38402606 PMCID: PMC11076015 DOI: 10.1016/j.neuron.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/01/2023] [Accepted: 01/23/2024] [Indexed: 02/27/2024]
Abstract
Alzheimer's disease (AD) and the mechanisms underlying its etiology and progression are complex and multifactorial. The higher AD risk in women may serve as a clue to better understand these complicated processes. In this review, we examine aspects of AD that demonstrate sex-dependent effects and delve into the potential biological mechanisms responsible, compiling findings from advanced technologies such as single-cell RNA sequencing, metabolomics, and multi-omics analyses. We review evidence that sex hormones and sex chromosomes interact with various disease mechanisms during aging, encompassing inflammation, metabolism, and autophagy, leading to unique characteristics in disease progression between men and women.
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Affiliation(s)
- Chloe Lopez-Lee
- Helen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA; Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY, USA
| | - Eileen Ruth S Torres
- Helen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Gillian Carling
- Helen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA; Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA; Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY, USA.
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10
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Horn MD, Forest SC, Saied AA, MacLean AG. Astrocyte expression of aging-associated markers positively correlates with neurodegeneration in the frontal lobe of the rhesus macaque brain. Front Aging Neurosci 2024; 16:1368517. [PMID: 38577492 PMCID: PMC10993697 DOI: 10.3389/fnagi.2024.1368517] [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: 01/10/2024] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction As the population over the age of 65 increases, rates of neurodegenerative disorders and dementias will rise - necessitating further research into the cellular and molecular mechanisms that contribute to brain aging. With the critical importance of astrocytes to neuronal health and functioning, we hypothesized that alterations in astrocyte expression of aging-associated markers p16INK4a (p16) and sirtuin 1 (SIRT1) with age would correlate with increased rates of neurodegeneration, as measured by FluoroJade C (FJC) staining. Methods To test this hypothesis, 19 rhesus macaques at the Tulane National Primate Research Center were selected based on the following criteria: archival FFPE CNS tissue available to use, no noted neuropathology, and an age range of 5-30 years. Tissues were cut at 5 μm and stained for GFAP, p16, SIRT1, and FJC, followed by whole-slide imaging and HALO® image analysis for percentage of marker-positive cells and relative intensity of each stain. Results We found the percentage of p16+ cells increases with age in total cells and astrocytes of the frontal (p = 0.0021, p = 0.0012 respectively) and temporal (p = 0.0226, p = 0.0203 respectively) lobes, as well as the relative intensity of p16 staining (frontal lobe: p = 0.0060; temporal lobe: p = 0.0269). For SIRT1, we found no correlation with age except for an increase in the relative intensity of SIRT1 in the temporal lobe (p = 0.0033). There was an increase in neurodegeneration, as measured by the percentage of FJC+ cells in the frontal lobe with age (p = 0.0057), as well as in the relative intensity of FJC staining in the frontal (p = 0.0030) and parietal (p = 0.0481) lobes. Importantly, increased p16 and SIRT1 expression in astrocytes correlated with increasing neurodegeneration in the frontal lobe (p = 0.0009, p = 0.0095 respectively). Discussion Together, these data suggest that age-associated alterations in astrocytes contribute to neurodegeneration and provide a target for mechanistic studies in the future.
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Affiliation(s)
- Miranda D. Horn
- Brain Institute, Tulane University, New Orleans, LA, United States
| | | | - Ahmad A. Saied
- Tulane National Primate Research Center, Covington, LA, United States
| | - Andrew G. MacLean
- Brain Institute, Tulane University, New Orleans, LA, United States
- Tulane National Primate Research Center, Covington, LA, United States
- Tulane Center for Aging, New Orleans, LA, United States
- Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, LA, United States
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11
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Zarrella JA, Tsurumi A. Genome-wide transcriptome profiling and development of age prediction models in the human brain. Aging (Albany NY) 2024; 16:4075-4094. [PMID: 38428408 PMCID: PMC10968712 DOI: 10.18632/aging.205609] [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: 05/02/2022] [Accepted: 03/28/2023] [Indexed: 03/03/2024]
Abstract
Aging-related transcriptome changes in various regions of the healthy human brain have been explored in previous works, however, a study to develop prediction models for age based on the expression levels of specific panels of transcripts is lacking. Moreover, studies that have assessed sexually dimorphic gene activities in the aging brain have reported discrepant results, suggesting that additional studies would be advantageous. The prefrontal cortex (PFC) region was previously shown to have a particularly large number of significant transcriptome alterations during healthy aging in a study that compared different regions in the human brain. We harmonized neuropathologically normal PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years, and found a large number of differentially regulated transcripts in the old and elderly, compared to young samples overall, and compared female and male-specific expression alterations. We assessed the genes that were associated with age by employing ontology, pathway, and network analyses. Furthermore, we applied various established (least absolute shrinkage and selection operator (Lasso) and Elastic Net (EN)) and recent (eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)) machine learning algorithms to develop accurate prediction models for chronological age and validated them. Studies to further validate these models in other large populations and molecular studies to elucidate the potential mechanisms by which the transcripts identified may be related to aging phenotypes would be advantageous.
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Affiliation(s)
- Joseph A. Zarrella
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Amy Tsurumi
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Shriner's Hospitals for Children-Boston, Boston, MA 02114, USA
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12
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Yildiz C, Medina I. Thermodynamic Analysis to Evaluate the Effect of Diet on Brain Glucose Metabolism: The Case of Fish Oil. Nutrients 2024; 16:631. [PMID: 38474759 DOI: 10.3390/nu16050631] [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: 01/16/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Inefficient glucose metabolism and decreased ATP production in the brain are linked to ageing, cognitive decline, and neurodegenerative diseases (NDDs). This study employed thermodynamic analysis to assess the effect of fish oil supplementation on glucose metabolism in ageing brains. Data from previous studies on glucose metabolism in the aged human brain and grey mouse lemur brains were examined. The results demonstrated that Omega-3 fish oil supplementation in grey mouse lemurs increased entropy generation and decreased Gibbs free energy across all brain regions. Specifically, there was a 47.4% increase in entropy generation and a 47.4 decrease in Gibbs free energy in the whole brain, indicating improved metabolic efficiency. In the human model, looking at the specific brain regions, supplementation with Omega-3 polyunsaturated fatty acids (n-3 PUFAs) reduced the entropy generation difference between elderly and young individuals in the cerebellum and particular parts of the brain cortex, namely the anterior cingulate and occipital lobe, with 100%, 14.29%, and 20% reductions, respectively. The Gibbs free energy difference was reduced only in the anterior cingulate by 60.64%. This research underscores that the application of thermodynamics is a comparable and powerful tool in comprehending the dynamics and metabolic intricacies within the brain.
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Affiliation(s)
- Cennet Yildiz
- Marine Chemistry, Instituto de Investigaciones Marinas CSIC, 36208 Vigo, Spain
- Biothermodynamics, School of Life Sciences, Technische Universität München, 85354 Freising, Germany
| | - Isabel Medina
- Marine Chemistry, Instituto de Investigaciones Marinas CSIC, 36208 Vigo, Spain
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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14
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Millar PR, Gordon BA, Wisch JK, Schultz SA, Benzinger TL, Cruchaga C, Hassenstab JJ, Ibanez L, Karch C, Llibre-Guerra JJ, Morris JC, Perrin RJ, Supnet-Bell C, Xiong C, Allegri RF, Berman SB, Chhatwal JP, Chrem Mendez PA, Day GS, Hofmann A, Ikeuchi T, Jucker M, Lee JH, Levin J, Lopera F, Niimi Y, Sánchez-González VJ, Schofield PR, Sosa-Ortiz AL, Vöglein J, Bateman RJ, Ances BM, McDade EM. Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease. Mol Neurodegener 2023; 18:98. [PMID: 38111006 PMCID: PMC10729487 DOI: 10.1186/s13024-023-00688-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/28/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND "Brain-predicted age" estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. METHODS We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-β-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE ε4 carrier status, sex, and education. RESULTS Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. CONCLUSIONS We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Julie K Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Stephanie A Schultz
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Tammie Ls Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Laura Ibanez
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- NeuroGenomics & Informatics Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | | | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology & Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Chengjie Xiong
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Anna Hofmann
- German Center for Neurodegenerative Diseases (DZNE), 72076, Tübingen, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), 72076, Tübingen, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Yoshiki Niimi
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Bunkyo-Ku, Tokyo, Japan
| | - Victor J Sánchez-González
- Departamento de Clínicas, CUALTOS, Universidad de Guadalajara, Tepatitlán de Morelos, Jalisco, México
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Ana Luisa Sosa-Ortiz
- Instituto Nacional de Neurologia y Neurocirugía MVS, CDMX, Ciudad de México, Mexico
| | - Jonathan Vöglein
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric M McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
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15
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Roger E, Labache L, Hamlin N, Kruse J, Baciu M, Doucet GE. When Age Tips the Balance: a Dual Mechanism Affecting Hemispheric Specialization for Language. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.04.569978. [PMID: 38106059 PMCID: PMC10723284 DOI: 10.1101/2023.12.04.569978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Aging engenders neuroadaptations, generally reducing specificity and selectivity in functional brain responses. Our investigation delves into the functional specialization of brain hemispheres within language-related networks across adulthood. In a cohort of 728 healthy adults spanning ages 18 to 88, we modeled the trajectories of inter-hemispheric asymmetry concerning the principal functional gradient across 37 homotopic regions of interest (hROIs) of an extensive language network, known as the Language-and-Memory Network. Our findings reveal that over two-thirds of Language-and-Memory Network hROIs undergo asymmetry changes with age, falling into two main clusters. The first cluster evolves from left-sided specialization to right-sided tendencies, while the second cluster transitions from right-sided asymmetry to left-hemisphere dominance. These reversed asymmetry shifts manifest around midlife, occurring after age 50, and are associated with poorer language production performance. Our results provide valuable insights into the influence of functional brain asymmetries on language proficiency and present a dynamic perspective on brain plasticity during the typical aging process.
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Affiliation(s)
- Elise Roger
- Institut Universitaire de Gériatrie de Montréal, Communication and Aging Lab, Montreal, Quebec, Canada
- Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France
| | - Loïc Labache
- Department of Psychology, Yale University, New Haven, CT, 06520, US
| | - Noah Hamlin
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 68010, US
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, US
| | - Jordanna Kruse
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 68010, US
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, US
| | - Monica Baciu
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France
| | - Gaelle E. Doucet
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 68010, US
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, US
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Omaha, NE, 68178, US
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16
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Valdes-Hernandez PA, Laffitte Nodarse C, Cole JH, Cruz-Almeida Y. Feasibility of brain age predictions from clinical T1-weighted MRIs. Brain Res Bull 2023; 205:110811. [PMID: 37952679 DOI: 10.1016/j.brainresbull.2023.110811] [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: 05/31/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
An individual's brain predicted age minus chronological age (brain-PAD) obtained from MRIs could become a biomarker of disease in research studies. However, brain age reports from clinical MRIs are scant despite the rich clinical information hospitals provide. Since clinical MRI protocols are meant for specific clinical purposes, performance of brain age predictions on clinical data need to be tested. We explored the feasibility of using DeepBrainNet, a deep network previously trained on research-oriented MRIs, to predict the brain ages of 840 patients who visited 15 facilities of a health system in Florida. Anticipating a strong prediction bias in our clinical sample, we characterized it to propose a covariate model in group-level regressions of brain-PAD (recommended to avoid Type I, II errors), and tested its generalizability, a requirement for meaningful brain age predictions in new single clinical cases. The best bias-related covariate model was scanner-independent and linear in age, while the best method to estimate bias-free brain ages was the inverse of a scanner-independent and quadratic in brain age function. We demonstrated the feasibility to detect sex-related differences in brain-PAD using group-level regression accounting for the selected covariate model. These differences were preserved after bias correction. The Mean-Average Error (MAE) of the predictions in independent data was ∼8 years, 2-3 years greater than reports for research-oriented MRIs using DeepBrainNet, whereas an R2 (assuming no bias) was 0.33 and 0.76 for the uncorrected and corrected brain ages, respectively. DeepBrainNet on clinical populations seems feasible, but more accurate algorithms or transfer-learning retraining is needed.
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Affiliation(s)
- Pedro A Valdes-Hernandez
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - Chavier Laffitte Nodarse
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK; Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Yenisel Cruz-Almeida
- Department of Community Dentistry and Behavioral Science, University of Florida, USA; Pain Research and Intervention Center of Excellence, University of Florida, USA; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, USA; Department of Neuroscience, College of Medicine, University of Florida, USA.
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17
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Zhang F, Chang H, Schaefer SM, Gou J. Biological age and brain age in midlife: relationship to multimorbidity and mental health. Neurobiol Aging 2023; 132:145-153. [PMID: 37804610 PMCID: PMC10803130 DOI: 10.1016/j.neurobiolaging.2023.09.003] [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: 11/04/2022] [Revised: 08/30/2023] [Accepted: 09/07/2023] [Indexed: 10/09/2023]
Abstract
Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.
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Affiliation(s)
- Fengqing Zhang
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA.
| | - Hansoo Chang
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Stacey M Schaefer
- Institute on Aging, University of Wisconsin-Madison, Madison, WI, USA
| | - Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, PA, USA
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18
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Zhang Y, Lu J, Wang M, Zuo C, Jiang J. Influence of Gender on Tau Precipitation in Alzheimer's Disease According to ATN Research Framework. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:565-575. [PMID: 38223687 PMCID: PMC10781910 DOI: 10.1007/s43657-022-00076-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 08/26/2022] [Accepted: 09/01/2022] [Indexed: 01/16/2024]
Abstract
Tau proteins accumulation and their spreading pattern were affected by gender in cognitive impairment patients, especially in the progression of Alzheimer's disease (AD). However, it was unclear whether the gender effects for tau deposition influenced by amyloid deposition. The aim of this study was to investigate gender differences for tau depositions in Aβ positive (A+) subjects. In this study, tau and amyloid positron emission tomography images, structural magnetic resonance imaging images, and demographic information were collected from 179 subjects in Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 63 subjects from Huashan Hospital. Subjects were classified as T+ or T- according to the presence or absence of tau (T) biomarkers. We used two-sample t test and one-way analysis of variance test to analyze the effect of gender with adjusting for age, years of education, and Minimum Mental State Examination. In the ADNI cohort, we found differences in Tau deposition in fusiform gyrus, inferior temporal gyrus, middle temporal gyrus and parahippocampal gyrus between the female T+ (FT+) and male T+ (MT+) groups (p < 0.05). Tau deposition did not differ significantly between female T- (FT-) and male T- (MT-) subjects (p > 0.05). In the Huashan Hospital cohort, there was no difference in Tau deposition between FT+ and MT+ (p > 0.05). The results show that tau depositions significantly increased in females in above brain regions. Our findings suggest that tau deposition is influenced by gender in the A+ subjects. This result has important clinical implications for the development of gender-guided early interventions for patients with both Tau and Amyloid depositions. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00076-9.
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Affiliation(s)
- Ying Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai, 200444 China
| | - Jiaying Lu
- PET Center and National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 201206 China
| | - Min Wang
- School of Communication and Information Engineering, Shanghai University, Shanghai, 200444 China
| | - Chuantao Zuo
- PET Center and National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 201206 China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, 200444 China
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Lee JJ, Earnest T, Ha SM, Bani A, Kothapalli D, Liu P, Sotiras A. Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298396. [PMID: 38116031 PMCID: PMC10729728 DOI: 10.1101/2023.11.10.23298396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease. Highlights ▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.
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He S, Guan Y, Cheng CH, Moore TL, Luebke JI, Killiany RJ, Rosene DL, Koo BB, Ou Y. Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. Front Aging Neurosci 2023; 15:1249415. [PMID: 38020785 PMCID: PMC10646581 DOI: 10.3389/fnagi.2023.1249415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.
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Affiliation(s)
- Sheng He
- Harvard Medical School, Boston Children's Hospital, Boston, MA, United States
| | - Yi Guan
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Tara L. Moore
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Jennifer I. Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Douglas L. Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Yangming Ou
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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21
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Feng L, Ye Z, Mo C, Wang J, Liu S, Gao S, Ke H, Canida TA, Pan Y, van Greevenbroek MM, Houben AJ, Wang K, Hatch KS, Ma Y, Lei DK, Chen C, Mitchell BD, Hong LE, Kochunov P, Chen S, Ma T. Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank. J Hypertens 2023; 41:1811-1820. [PMID: 37682053 PMCID: PMC11083214 DOI: 10.1097/hjh.0000000000003553] [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] [Indexed: 09/09/2023]
Abstract
BACKGROUND Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear. METHODS In this study, we focused on N = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets. RESULTS The hypertension group is on average 0.31 years (95% CI = 0.13-0.49; P < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68-0.95; P < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034-0.71, P = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50-59 and aged 60-69. CONCLUSION High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.
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Affiliation(s)
- Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Chen Mo
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Jingtao Wang
- Department of Hematology, Qilu Hospital of Shandong University
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health
| | - Travis A. Canida
- Department of Mathematics, The College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, USA
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry
| | - Marleen M.J. van Greevenbroek
- Department of Internal Medicine, Maastricht University Medical Centre
- CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Alfons J.H.M. Houben
- Department of Internal Medicine, Maastricht University Medical Centre
- CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Kai Wang
- Department of Internal Medicine, Maastricht University Medical Centre
- CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | | | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry
| | - David K.Y. Lei
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Braxton D. Mitchell
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, USA
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health
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Zelco A, Wapeesittipan P, Joshi A. Insights into Sex and Gender Differences in Brain and Psychopathologies Using Big Data. Life (Basel) 2023; 13:1676. [PMID: 37629533 PMCID: PMC10455614 DOI: 10.3390/life13081676] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/30/2023] [Accepted: 07/15/2023] [Indexed: 08/27/2023] Open
Abstract
The societal implication of sex and gender (SG) differences in brain are profound, as they influence brain development, behavior, and importantly, the presentation, prevalence, and therapeutic response to diseases. Technological advances have enabled speed up identification and characterization of SG differences during development and in psychopathologies. The main aim of this review is to elaborate on new technological advancements, such as genomics, imaging, and emerging biobanks, coupled with bioinformatics analyses of data generated from these technologies have facilitated the identification and characterization of SG differences in the human brain through development and psychopathologies. First, a brief explanation of SG concepts is provided, along with a developmental and evolutionary context. We then describe physiological SG differences in brain activity and function, and in psychopathologies identified through imaging techniques. We further provide an overview of insights into SG differences using genomics, specifically taking advantage of large cohorts and biobanks. We finally emphasize how bioinformatics analyses of big data generated by emerging technologies provides new opportunities to reduce SG disparities in health outcomes, including major challenges.
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Affiliation(s)
| | | | - Anagha Joshi
- Department of Clinical Science, Computational Biology Unit, University of Bergen, 5020 Bergen, Norway; (A.Z.); (P.W.)
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23
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McKay NS, Gordon BA, Hornbeck RC, Dincer A, Flores S, Keefe SJ, Joseph-Mathurin N, Jack CR, Koeppe R, Millar PR, Ances BM, Chen CD, Daniels A, Hobbs DA, Jackson K, Koudelis D, Massoumzadeh P, McCullough A, Nickels ML, Rahmani F, Swisher L, Wang Q, Allegri RF, Berman SB, Brickman AM, Brooks WS, Cash DM, Chhatwal JP, Day GS, Farlow MR, la Fougère C, Fox NC, Fulham M, Ghetti B, Graff-Radford N, Ikeuchi T, Klunk W, Lee JH, Levin J, Martins R, Masters CL, McConathy J, Mori H, Noble JM, Reischl G, Rowe C, Salloway S, Sanchez-Valle R, Schofield PR, Shimada H, Shoji M, Su Y, Suzuki K, Vöglein J, Yakushev I, Cruchaga C, Hassenstab J, Karch C, McDade E, Perrin RJ, Xiong C, Morris JC, Bateman RJ, Benzinger TLS. Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN). Nat Neurosci 2023; 26:1449-1460. [PMID: 37429916 PMCID: PMC10400428 DOI: 10.1038/s41593-023-01359-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.
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Affiliation(s)
| | | | | | - Aylin Dincer
- Washington University in St. Louis, St. Louis, MO, USA
| | - Shaney Flores
- Washington University in St. Louis, St. Louis, MO, USA
| | - Sarah J Keefe
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | | | - Beau M Ances
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - Diana A Hobbs
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | | | | | | | - Laura Swisher
- Washington University in St. Louis, St. Louis, MO, USA
| | - Qing Wang
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - Adam M Brickman
- Columbia University Irving Medical Center, New York, NY, USA
| | - William S Brooks
- Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - David M Cash
- UK Dementia Research Institute at University College London, London, UK
- University College London, London, UK
| | - Jasmeer P Chhatwal
- Massachusetts General and Brigham & Women's Hospitals, Harvard Medical School, Boston, MA, USA
| | | | | | - Christian la Fougère
- Department of Radiology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Nick C Fox
- UK Dementia Research Institute at University College London, London, UK
- University College London, London, UK
| | - Michael Fulham
- Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | | | | | | | | | | | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Ralph Martins
- Edith Cowan University, Joondalup, Western Australia, Australia
| | | | | | | | - James M Noble
- Columbia University Irving Medical Center, New York, NY, USA
| | - Gerald Reischl
- Department of Radiology, University of Tübingen, Tübingen, Germany
| | | | | | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | | | | | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - Igor Yakushev
- School of Medicine, Technical University of Munich, Munich, Germany
| | | | | | - Celeste Karch
- Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - John C Morris
- Washington University in St. Louis, St. Louis, MO, USA
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24
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Gaspar-Silva F, Trigo D, Magalhaes J. Ageing in the brain: mechanisms and rejuvenating strategies. Cell Mol Life Sci 2023; 80:190. [PMID: 37354261 DOI: 10.1007/s00018-023-04832-6] [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: 03/29/2023] [Revised: 05/31/2023] [Accepted: 06/07/2023] [Indexed: 06/26/2023]
Abstract
Ageing is characterized by the progressive loss of cellular homeostasis, leading to an overall decline of the organism's fitness. In the brain, ageing is highly associated with cognitive decline and neurodegenerative diseases. With the rise in life expectancy, characterizing the brain ageing process becomes fundamental for developing therapeutic interventions against the increased incidence of age-related neurodegenerative diseases and to aim for an increase in human life span and, more importantly, health span. In this review, we start by introducing the molecular/cellular hallmarks associated with brain ageing and their impact on brain cell populations. Subsequently, we assess emerging evidence on how systemic ageing translates into brain ageing. Finally, we revisit the mainstream and the novel rejuvenating strategies, discussing the most successful ones in delaying brain ageing and related diseases.
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Affiliation(s)
- Filipa Gaspar-Silva
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-135, Porto, Portugal
| | - Diogo Trigo
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Joana Magalhaes
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-135, Porto, Portugal.
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25
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Zuo N, Hu T, Liu H, Sui J, Liu Y, Jiang T. Different Regional Patterns in Gray Matter-based Age Prediction. Neurosci Bull 2023; 39:984-988. [PMID: 36637790 PMCID: PMC10264318 DOI: 10.1007/s12264-022-01016-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/11/2022] [Indexed: 01/14/2023] Open
Affiliation(s)
- Nianming Zuo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- University of Chinese Academy of Sciences, Beijing, 100190, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Tianyu Hu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Hao Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jing Sui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
- Chinese Institute for Brain Research, Beijing, 102206, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
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26
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Brier MR, Li Z, Ly M, Karim HT, Liang L, Du W, McCarthy JE, Cross AH, Benzinger TLS, Naismith RT, Chahin S. "Brain age" predicts disability accumulation in multiple sclerosis. Ann Clin Transl Neurol 2023; 10:990-1001. [PMID: 37119507 PMCID: PMC10270248 DOI: 10.1002/acn3.51782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/17/2023] [Accepted: 04/10/2023] [Indexed: 05/01/2023] Open
Abstract
OBJECTIVE Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset. METHODS Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. RESULTS MS was associated with advanced predicted brain age cross-sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. INTERPRETATION Brain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.
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Affiliation(s)
- Matthew R. Brier
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Zhuocheng Li
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Maria Ly
- Mallinckrodt Institute of RadiologyWashington University in St. LouisSt LouisMissouriUSA
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Helmet T. Karim
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BioengineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Leda Liang
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - Weixin Du
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - John E. McCarthy
- Department of Mathematics and StatisticsWashington University in St. LouisSt LouisMissouriUSA
| | - Anne H. Cross
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Mallinckrodt Institute of RadiologyWashington University in St. LouisSt LouisMissouriUSA
| | - Robert T. Naismith
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
| | - Salim Chahin
- Department of NeurologyWashington University in St. LouisSt LouisMissouriUSA
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27
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Kadlecova M, Freude K, Haukedal H. Complexity of Sex Differences and Their Impact on Alzheimer's Disease. Biomedicines 2023; 11:biomedicines11051261. [PMID: 37238932 DOI: 10.3390/biomedicines11051261] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 05/28/2023] Open
Abstract
Sex differences are present in brain morphology, sex hormones, aging processes and immune responses. These differences need to be considered for proper modelling of neurological diseases with clear sex differences. This is the case for Alzheimer's disease (AD), a fatal neurodegenerative disorder with two-thirds of cases diagnosed in women. It is becoming clear that there is a complex interplay between the immune system, sex hormones and AD. Microglia are major players in the neuroinflammatory process occurring in AD and have been shown to be directly affected by sex hormones. However, many unanswered questions remain as the importance of including both sexes in research studies has only recently started receiving attention. In this review, we provide a summary of sex differences and their implications in AD, with a focus on microglia action. Furthermore, we discuss current available study models, including emerging complex microfluidic and 3D cellular models and their usefulness for studying hormonal effects in this disease.
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Affiliation(s)
- Marion Kadlecova
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870 C Frederiksberg, Denmark
| | - Kristine Freude
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870 C Frederiksberg, Denmark
| | - Henriette Haukedal
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870 C Frederiksberg, Denmark
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28
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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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29
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Yang H, Fang B, Wang Z, Chen Y, Dong Y. The Timing Sequence and Mechanism of Aging in Endocrine Organs. Cells 2023; 12:cells12070982. [PMID: 37048056 PMCID: PMC10093290 DOI: 10.3390/cells12070982] [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: 01/25/2023] [Revised: 03/15/2023] [Accepted: 03/22/2023] [Indexed: 04/14/2023] Open
Abstract
The world is increasingly aging, and there is an urgent need to find a safe and effective way to delay the aging of the body. It is well known that the endocrine glands are one of the most important organs in the context of aging. Failure of the endocrine glands lead to an abnormal hormonal environment, which in turn leads to many age-related diseases. The aging of endocrine glands is closely linked to oxidative stress, cellular autophagy, genetic damage, and hormone secretion. The first endocrine organ to undergo aging is the pineal gland, at around 6 years old. This is followed in order by the hypothalamus, pituitary gland, adrenal glands, gonads, pancreatic islets, and thyroid gland. This paper summarises the endocrine gland aging-related genes and pathways by bioinformatics analysis. In addition, it systematically summarises the changes in the structure and function of aging endocrine glands as well as the mechanisms of aging. This study will advance research in the field of aging and help in the intervention of age-related diseases.
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Affiliation(s)
- He Yang
- Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, Ministry of Education, China Agricultural University, Beijing 100193, China
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Department of Nutrition and Health, China Agricultural University, Beijing 100193, China
| | - Bing Fang
- Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, Ministry of Education, China Agricultural University, Beijing 100193, China
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Department of Nutrition and Health, China Agricultural University, Beijing 100193, China
| | - Zixu Wang
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Yaoxing Chen
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Yulan Dong
- Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, Ministry of Education, China Agricultural University, Beijing 100193, China
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
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30
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Novakova Martinkova J, Ferretti MT, Ferrari A, Lerch O, Matuskova V, Secnik J, Hort J. Longitudinal progression of choroid plexus enlargement is associated with female sex, cognitive decline and ApoE E4 homozygote status. Front Psychiatry 2023; 14:1039239. [PMID: 36970283 PMCID: PMC10031049 DOI: 10.3389/fpsyt.2023.1039239] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/27/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction Choroid plexus (CP)-related mechanisms have been implicated in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease. In this pilot study, we aimed to elucidate the association between longitudinal changes in CP volume, sex and cognitive impairment. Methods We assessed longitudinal changes in CP volume in a cohort of n = 613 subjects across n = 2,334 datapoints from ADNI 2 and ADNI-GO, belonging to cognitively unimpaired (CN), stable mild cognitive impairment (MCI), clinically diagnosed Alzheimer's disease dementia (AD) or convertor (to either AD or MCI) subgroups. CP volume was automatically segmented and used as a response variable in linear mixed effect models with random intercept clustered by patient identity. Temporal effects of select variables were assessed by interactions and subgroup analyses. Results We found an overall significant increase of CP volume in time (14.92 mm3 per year, 95% confidence interval, CI (11.05, 18.77), p < 0.001). Sex-disaggregated results showed an annual rate of increase 9.48 mm3 in males [95% CI (4.08, 14.87), p < 0.001], and 20.43 mm3 in females [95% CI (14.91, 25.93), p < 0.001], indicating more than double the rate of increase in females, which appeared independent of other temporal variables. The only diagnostic group with a significant CP increase as compared to CN was the convertors group, with an increase of 24.88 mm3/year [95% CI (14, 35.82), p < 0.001]. ApoE exhibited a significant temporal effect, with the E4 homozygote group's CP increasing at more than triple the rate of non-carrier or heterozygote groups [40.72, 95% CI (25.97, 55.46), p < 0.001 vs. 12.52, 95% CI (8.02, 17.02), p < 0.001 for ApoE E4 homozygotes and E4 non-carriers, respectively], and may have modified the diagnostic group relationship. Conclusion Our results contribute to potential mechanisms for sex differences in cognitive impairment with a novel finding of twice the annual choroid plexus enlargement in females and provide putative support for CP-related mechanisms of cognitive deterioration and its relationship to ApoE E4.
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Affiliation(s)
- Julie Novakova Martinkova
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | | | | | - Ondrej Lerch
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Veronika Matuskova
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Juraj Secnik
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
- Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
| | - Jakub Hort
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
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31
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Saravanan M, Xu R, Roby O, Wang Y, Zhu S, Lu A, Du J. Tissue-Specific Sex Difference in Mouse Eye and Brain Metabolome Under Fed and Fasted States. Invest Ophthalmol Vis Sci 2023; 64:18. [PMID: 36892534 PMCID: PMC10010444 DOI: 10.1167/iovs.64.3.18] [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: 01/09/2023] [Accepted: 02/13/2023] [Indexed: 03/10/2023] Open
Abstract
Purpose Visual physiology and various ocular diseases demonstrate sexual dimorphisms; however, how sex influences metabolism in different eye tissues remains undetermined. This study aims to address common and tissue-specific sex differences in metabolism in the retina, RPE, lens, and brain under fed and fasted conditions. Methods After ad libitum fed or being deprived of food for 18 hours, mouse eye tissues (retina, RPE/choroid, and lens), brain, and plasma were harvested for targeted metabolomics. The data were analyzed with both partial least squares-discriminant analysis and volcano plot analysis. Results Among 133 metabolites that cover major metabolic pathways, we found 9 to 45 metabolites that are sex different in different tissues under the fed state and 6 to 18 metabolites under the fasted state. Among these sex-different metabolites, 33 were changed in 2 or more tissues, and 64 were tissue specific. Pantothenic acid, hypotaurine, and 4-hydroxyproline were the top commonly changed metabolites. The lens and the retina had the most tissue-specific, sex-different metabolites enriched in the metabolism of amino acid, nucleotide, lipids, and tricarboxylic acid cycle. The lens and the brain had more similar sex-different metabolites than other ocular tissues. The female RPE and female brain were more sensitive to fasting with more decreased metabolites in amino acid metabolism, tricarboxylic acid cycles, and glycolysis. The plasma had the fewest sex-different metabolites, with very few overlapping changes with tissues. Conclusions Sex has a strong influence on eye and brain metabolism in tissue-specific and metabolic state-specific manners. Our findings may implicate the sexual dimorphisms in eye physiology and susceptibility to ocular diseases.
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Affiliation(s)
- Meghashri Saravanan
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
| | - Rong Xu
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
| | - Olivia Roby
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
| | - Yekai Wang
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
| | - Siyan Zhu
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
| | - Amy Lu
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
| | - Jianhai Du
- Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, United States
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, West Virginia, United States
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Andreou D, Steen NE, Jørgensen KN, Smelror RE, Wedervang-Resell K, Nerland S, Westlye LT, Nærland T, Myhre AM, Joa I, Reitan SMK, Vaaler A, Morken G, Bøen E, Elvsåshagen T, Boye B, Malt UF, Aukrust P, Skrede S, Kroken RA, Johnsen E, Djurovic S, Andreassen OA, Ueland T, Agartz I. Lower circulating neuron-specific enolase concentrations in adults and adolescents with severe mental illness. Psychol Med 2023; 53:1479-1488. [PMID: 35387700 PMCID: PMC10009386 DOI: 10.1017/s0033291721003056] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/05/2021] [Accepted: 07/13/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Both neurodegenerative and neurodevelopmental abnormalities have been suggested to be part of the etiopathology of severe mental illness (SMI). Neuron-specific enolase (NSE), mainly located in the neuronal cytoplasm, may indicate the process as it is upregulated after neuronal injury while a switch from non-neuronal enolase to NSE occurs during neuronal maturation. METHODS We included 1132 adult patients with SMI [schizophrenia (SZ) or bipolar spectrum disorders], 903 adult healthy controls (HC), 32 adolescent patients with SMI and 67 adolescent HC. Plasma NSE concentrations were measured by enzyme immunoassay. For 842 adults and 85 adolescents, we used total grey matter volume (TGMV) based on T1-weighted magnetic resonance images processed in FreeSurfer v6.0. We explored NSE case-control differences in adults and adolescents separately. To investigate whether putative case-control differences in NSE were TGMV-dependent we controlled for TGMV. RESULTS We found significantly lower NSE concentrations in both adult (p < 0.001) and adolescent patients with SMI (p = 0.007) compared to HC. The results remained significant after controlling for TGMV. Among adults, both patients with SZ spectrum (p < 0.001) and bipolar spectrum disorders (p = 0.005) had lower NSE than HC. In both patient subgroups, lower NSE levels were associated with increased symptom severity. Among adults (p < 0.001) and adolescents (p = 0.040), females had lower NSE concentrations than males. CONCLUSION We found lower NSE concentrations in adult and adolescent patients with SMI compared to HC. The results suggest the lack of progressive neuronal injury, and may reflect abnormal neuronal maturation. This provides further support of a neurodevelopmental rather than a neurodegenerative mechanism in SMI.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Runar Elle Smelror
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kirsten Wedervang-Resell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Child and Adolescent Mental Health Research Unit, Division of Mental Health and Addiction, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Terje Nærland
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway
| | - Anne Margrethe Myhre
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Inge Joa
- TIPS – Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway
| | - Solveig Merete Klæbo Reitan
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Arne Vaaler
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Gunnar Morken
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Erlend Bøen
- Psychosomatic and C-L Psychiatry, Adult, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torbjørn Elvsåshagen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Birgitte Boye
- Psychosomatic and C-L Psychiatry, Adult, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Behavioural Medicine, University of Oslo, Oslo, Norway
| | - Ulrik Fredrik Malt
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research Institute of Internal Medicine, Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Silje Skrede
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
| | - Rune Andreas Kroken
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Haukeland University Hospital, Bergen, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Department of Clinical Science, Norwegian Centre for Mental Disorders Research (NORMENT), University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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Baik K, Jeon S, Yang SJ, Na Y, Chung SJ, Yoo HS, Yun M, Lee PH, Sohn YH, Ye BS. Cortical Thickness and Brain Glucose Metabolism in Healthy Aging. J Clin Neurol 2023; 19:138-146. [PMID: 36647225 PMCID: PMC9982173 DOI: 10.3988/jcn.2022.0021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 08/04/2022] [Accepted: 08/07/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND AND PURPOSE We aimed to determine the effect of demographic factors on cortical thickness and brain glucose metabolism in healthy aging subjects. METHODS The following tests were performed on 71 subjects with normal cognition: neurological examination, 3-tesla magnetic resonance imaging, 18F-fluorodeoxyglucose positron-emission tomography, and neuropsychological tests. Cortical thickness and brain metabolism were measured using vertex- and voxelwise analyses, respectively. General linear models (GLMs) were used to determine the effects of age, sex, and education on cortical thickness and brain glucose metabolism. The effects of mean lobar cortical thickness and mean lobar metabolism on neuropsychological test scores were evaluated using GLMs after controlling for age, sex, and education. The intracranial volume (ICV) was further included as a predictor or covariate for the cortical thickness analyses. RESULTS Age was negatively correlated with the mean cortical thickness in all lobes (frontal and parietal lobes, p=0.001; temporal and occipital lobes, p<0.001) and with the mean temporal metabolism (p=0.005). Education was not associated with cortical thickness or brain metabolism in any lobe. Male subjects had a lower mean parietal metabolism than did female subjects (p<0.001), while their mean cortical thicknesses were comparable. ICV was positively correlated with mean cortical thickness in the frontal (p=0.016), temporal (p=0.009), and occipital (p=0.007) lobes. The mean lobar cortical thickness was not associated with cognition scores, while the mean temporal metabolism was positively correlated with verbal memory test scores. CONCLUSIONS Age and sex affect cortical thickness and brain glucose metabolism in different ways. Demographic factors must therefore be considered in analyses of cortical thickness and brain metabolism.
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Affiliation(s)
- Kyoungwon Baik
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seun Jeon
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Soh-Jeong Yang
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Yeona Na
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Han Soo Yoo
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Mijin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Young H. Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
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Goyal MS, Blazey T, Metcalf NV, McAvoy MP, Strain JF, Rahmani M, Durbin TJ, Xiong C, Benzinger TLS, Morris JC, Raichle ME, Vlassenko AG. Brain aerobic glycolysis and resilience in Alzheimer disease. Proc Natl Acad Sci U S A 2023; 120:e2212256120. [PMID: 36745794 PMCID: PMC9963219 DOI: 10.1073/pnas.2212256120] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 01/04/2023] [Indexed: 02/08/2023] Open
Abstract
The distribution of brain aerobic glycolysis (AG) in normal young adults correlates spatially with amyloid-beta (Aβ) deposition in individuals with symptomatic and preclinical Alzheimer disease (AD). Brain AG decreases with age, but the functional significance of this decrease with regard to the development of AD symptomatology is poorly understood. Using PET measurements of regional blood flow, oxygen consumption, and glucose utilization-from which we derive AG-we find that cognitive impairment is strongly associated with loss of the typical youthful pattern of AG. In contrast, amyloid positivity without cognitive impairment was associated with preservation of youthful brain AG, which was even higher than that seen in cognitively unimpaired, amyloid negative adults. Similar findings were not seen for blood flow nor oxygen consumption. Finally, in cognitively unimpaired adults, white matter hyperintensity burden was found to be specifically associated with decreased youthful brain AG. Our results suggest that AG may have a role in the resilience and/or response to early stages of amyloid pathology and that age-related white matter disease may impair this process.
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Affiliation(s)
- Manu S. Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Department of Neurology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO63108
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO63110
| | - Tyler Blazey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
| | - Nicholas V. Metcalf
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
| | - Mark P. McAvoy
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO63108
| | - Jeremy F. Strain
- Department of Neurology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
| | - Maryam Rahmani
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
| | - Tony J. Durbin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO63108
| | - Tammie L.-S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO63108
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO63110
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO63108
| | - Marcus E. Raichle
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Department of Neurology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO63108
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO63110
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO63130
- Department of Psychology & Brain Science, Washington University School of Medicine, St. Louis, MO63130
| | - Andrei G. Vlassenko
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO63110
- Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis, MO63110
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO63108
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35
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Lu J, Wang M, Wu P, Yakushev I, Zhang H, Ziegler S, Jiang J, Förster S, Wang J, Schwaiger M, Rominger A, Huang SC, Liu F, Zuo C, Shi K. Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:50-63. [PMID: 36939769 PMCID: PMC9883378 DOI: 10.1007/s43657-022-00079-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 06/18/2023]
Abstract
Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00079-6.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444 China
- Department of Informatics, Technische Universität München, 80333 Munich, Germany
| | - Ping Wu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Igor Yakushev
- Department of Nuclear Medicine, Technische Universität München, 80333 Munich, Germany
| | - Huiwei Zhang
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital LMU Munich, 80539 Munich, Germany
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444 China
| | - Stefan Förster
- Department of Nuclear Medicine, Technische Universität München, 80333 Munich, Germany
- Department of Nuclear Medicine, Klinikum Bayreuth, 95445, Bayreuth, Germany
| | - Jian Wang
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040 China
| | - Markus Schwaiger
- Klinikum r. d. Isar, Technische Universität München, 95445 Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Sung-Cheng Huang
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, 90095 USA
| | - Fengtao Liu
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040 China
| | - Chuantao Zuo
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Human Phenome Institute, Fudan University, Shanghai, 200433 China
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department of Informatics, Technische Universität München, 80333 Munich, Germany
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36
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Petrican R, Paine AL, Escott-Price V, Shelton KH. Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder. Sci Rep 2023; 13:984. [PMID: 36653486 PMCID: PMC9849214 DOI: 10.1038/s41598-023-28057-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Amy L Paine
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Katherine H Shelton
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
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37
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Millar PR, Gordon BA, Luckett PH, Benzinger TLS, Cruchaga C, Fagan AM, Hassenstab JJ, Perrin RJ, Schindler SE, Allegri RF, Day GS, Farlow MR, Mori H, Nübling G, Bateman RJ, Morris JC, Ances BM. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife 2023; 12:e81869. [PMID: 36607335 PMCID: PMC9988262 DOI: 10.7554/elife.81869] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Brian A Gordon
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
- Department of Neurosurgery, Washington University in St. LouisSt LouisUnited States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. LouisSt LouisUnited States
| | - Anne M Fagan
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Richard J Perrin
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Pathology and Immunology, Washington University in St. LouisSt LouisUnited States
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Ricardo F Allegri
- Department of Cognitive Neurology, Institute for Neurological Research (FLENI)Buenos AiresArgentina
| | - Gregory S Day
- Department of Neurology, Mayo Clinic FloridaJacksonvilleUnited States
| | - Martin R Farlow
- Department of Neurology, Indiana University School of MedicineIndianapolisUnited States
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku UniversityOsakaJapan
| | - Georg Nübling
- Department of Neurology, Ludwig-Maximilians UniversityMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Randall J Bateman
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - John C Morris
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
| | - Beau M Ances
- Department of Neurology, Washington University in St. LouisSt LouisUnited States
- Department of Radiology, Washington University in St. LouisSt LouisUnited States
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38
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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
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Taylor A, Zhang F, Niu X, Heywood A, Stocks J, Feng G, Popuri K, Beg MF, Wang L. Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration. Neuroimage 2022; 263:119621. [PMID: 36089183 PMCID: PMC9995621 DOI: 10.1016/j.neuroimage.2022.119621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/29/2022] [Accepted: 09/07/2022] [Indexed: 11/19/2022] Open
Abstract
Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.
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Affiliation(s)
- Alexei Taylor
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA 19104, USA
| | - Fengqing Zhang
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA 19104, USA.
| | - Xin Niu
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA 19104, USA
| | - Ashley Heywood
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jane Stocks
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Gangyi Feng
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Karteek Popuri
- Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BCE, Canada
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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40
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Liang WS, Goetz LH, Schork NJ. Assessing brain and biological aging trajectories associated with Alzheimer's disease. Front Neurosci 2022; 16:1036102. [PMID: 36389222 PMCID: PMC9650396 DOI: 10.3389/fnins.2022.1036102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
The development of effective treatments to prevent and slow Alzheimer's disease (AD) pathogenesis is needed in order to tackle the steady increase in the global prevalence of AD. This challenge is complicated by the need to identify key health shifts that precede the onset of AD and cognitive decline as these represent windows of opportunity for intervening and preventing disease. Such shifts may be captured through the measurement of biomarkers that reflect the health of the individual, in particular those that reflect brain age and biological age. Brain age biomarkers provide a composite view of the health of the brain based on neuroanatomical analyses, while biological age biomarkers, which encompass the epigenetic clock, provide a measurement of the overall health state of an individual based on DNA methylation analysis. Acceleration of brain and biological ages is associated with changes in cognitive function, as well as neuropathological markers of AD. In this mini-review, we discuss brain age and biological age research in the context of cognitive decline and AD. While more research is needed, studies show that brain and biological aging trajectories are variable across individuals and that such trajectories are non-linear at older ages. Longitudinal monitoring of these biomarkers may be valuable for enabling earlier identification of divergent pathological trajectories toward AD and providing insight into points for intervention.
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Affiliation(s)
- Winnie S. Liang
- NetBio, Inc., Los Angeles, CA, United States
- Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Laura H. Goetz
- NetBio, Inc., Los Angeles, CA, United States
- Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Nicholas J. Schork
- NetBio, Inc., Los Angeles, CA, United States
- Translational Genomics Research Institute, Phoenix, AZ, United States
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41
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Sanford N, Ge R, Antoniades M, Modabbernia A, Haas SS, Whalley HC, Galea L, Popescu SG, Cole JH, Frangou S. Sex differences in predictors and regional patterns of brain age gap estimates. Hum Brain Mapp 2022; 43:4689-4698. [PMID: 35790053 PMCID: PMC9491279 DOI: 10.1002/hbm.25983] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/11/2022] Open
Abstract
The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.
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Affiliation(s)
- Nicole Sanford
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Ruiyang Ge
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Shalaila S. Haas
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Liisa Galea
- Department of Psychology, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | | | - James H. Cole
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Dementia Research Centre, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sophia Frangou
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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42
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Gómez-Ramírez J, Fernández-Blázquez MA, González-Rosa JJ. A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain. Life (Basel) 2022; 12:1586. [PMID: 36295023 PMCID: PMC9656120 DOI: 10.3390/life12101586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 01/25/2023] Open
Abstract
We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires-at least in principle-two MRI scans, one performed at a young age and one at an old age. There is, however, a way to address this problem when having only one MRI scan obtained at an old age. We computed the total brain losses of elderly subjects as a ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 years were assessed. A causal analysis of factors affecting brain atrophy was performed using probabilistic Bayesian modelling and the mathematics of causal inference. We found that both age and sex were causally related to brain atrophy, with women reaching an elderly age with a 1% larger brain volume relative to their intracranial volume than men. How the brain ages and the rationale for sex differences in brain volume losses during the adult lifespan are questions that need to be addressed with causal inference and empirical data. The graphical causal modelling presented here can be instrumental in understanding a puzzling scientific area of study-the biological aging of the brain.
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Affiliation(s)
- Jaime Gómez-Ramírez
- Department of Psychology, University of Cadiz, 11003 Cadiz, Spain
- Institute of Biomedical Research Cadiz (INiBICA), 11009 Cadiz, Spain
| | | | - Javier J. González-Rosa
- Department of Psychology, University of Cadiz, 11003 Cadiz, Spain
- Institute of Biomedical Research Cadiz (INiBICA), 11009 Cadiz, Spain
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43
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Pallier PN, Ferrara M, Romagnolo F, Ferretti MT, Soreq H, Cerase A. Chromosomal and environmental contributions to sex differences in the vulnerability to neurological and neuropsychiatric disorders: Implications for therapeutic interventions. Prog Neurobiol 2022; 219:102353. [PMID: 36100191 DOI: 10.1016/j.pneurobio.2022.102353] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/22/2022] [Accepted: 09/06/2022] [Indexed: 10/14/2022]
Abstract
Neurological and neuropsychiatric disorders affect men and women differently. Multiple sclerosis, Alzheimer's disease, anxiety disorders, depression, meningiomas and late-onset schizophrenia affect women more frequently than men. By contrast, Parkinson's disease, autism spectrum condition, attention-deficit hyperactivity disorder, Tourette's syndrome, amyotrophic lateral sclerosis and early-onset schizophrenia are more prevalent in men. Women have been historically under-recruited or excluded from clinical trials, and most basic research uses male rodent cells or animals as disease models, rarely studying both sexes and factoring sex as a potential source of variation, resulting in a poor understanding of the underlying biological reasons for sex and gender differences in the development of such diseases. Putative pathophysiological contributors include hormones and epigenetics regulators but additional biological and non-biological influences may be at play. We review here the evidence for the underpinning role of the sex chromosome complement, X chromosome inactivation, and environmental and epigenetic regulators in sex differences in the vulnerability to brain disease. We conclude that there is a pressing need for a better understanding of the genetic, epigenetic and environmental mechanisms sustaining sex differences in such diseases, which is critical for developing a precision medicine approach based on sex-tailored prevention and treatment.
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Affiliation(s)
- Patrick N Pallier
- Blizard Institute, Centre for Neuroscience, Surgery and Trauma, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
| | - Maria Ferrara
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy; Department of Psychiatry, Yale University, School of Medicine, New Haven, CT, United States; Women's Brain Project (WBP), Switzerland
| | - Francesca Romagnolo
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | | | - Hermona Soreq
- The Edmond and Lily Safra Center of Brain Science, The Hebrew University of Jerusalem, 9190401, Israel
| | - Andrea Cerase
- EMBL-Rome, Via Ramarini 32, 00015 Monterotondo, RM, Italy; Blizard Institute, Centre for Genomics and Child Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK; Department of Biology, University of Pisa, SS12 Abetone e Brennero 4, 56127 Pisa, Italy.
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44
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Besson P, Rogalski E, Gill NP, Zhang H, Martersteck A, Bandt SK. Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging. Front Aging Neurosci 2022; 14:895535. [PMID: 36081894 PMCID: PMC9445244 DOI: 10.3389/fnagi.2022.895535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.
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Affiliation(s)
- Pierre Besson
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Psychiatry and Behavioral Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nathan P. Gill
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Hui Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Adam Martersteck
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - S. Kathleen Bandt
- Advanced Neuroimaging and Surgical Epilepsy (ANISE) Lab, Northwestern University, Chicago, IL, United States
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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45
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Brinkley TE, Stites SD, Hunsberger HC, Karvonen-Gutierrez CA, Li M, Shaaban CE, Thorpe RJ, Kritchevsky SB. Research Centers Collaborative Network Workshop on Sex and Gender Differences in Aging. Innov Aging 2022; 6:igac055. [PMID: 36267320 PMCID: PMC9579719 DOI: 10.1093/geroni/igac055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Indexed: 02/03/2023] Open
Abstract
Aging affects men and women differently; however, the impact of sex and gender on the aging process is not well understood. Moreover, these 2 concepts are often conflated, which further contributes to a lack of clarity on this important issue. In an effort to better understand the relevance of sex and gender in aging research, the Research Centers Collaborative Network sponsored a 1.5-day conference on sex and gender differences in aging that brought together key thought leaders from the 6 National Institute on Aging center programs. The meeting included sessions on comparing males and females, pathophysiological differences, sex/gender in clinical care, and gender and health in the social context. Presenters from a wide array of disciplines identified opportunities for multidisciplinary research to address current gaps in the field and highlighted the need for a more systematic approach to understanding the how and why of sex/gender differences, as well as the health implications of these differences and the sex/gender biases that affect clinical treatment and outcomes. This article summarizes the proceedings of the workshop and provides several recommendations to move the field forward, such as better data collection tools to assess the intersection of sex and gender in epidemiological research; a life course perspective with attention to fetal/developmental origins and key life stages; innovative animal models to distinguish contributions from sex hormones versus sex chromosomes; and integration of sex/gender into teaching and clinical practice. Ultimately, successful implementation of these recommendations will require thoughtful investigations across the translational spectrum and increased collaborations among those with expertise in sex and gender differences.
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Affiliation(s)
- Tina E Brinkley
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Shana D Stites
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Holly C Hunsberger
- Department of Foundational Science and Humanities, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, USA
| | | | - Mengting Li
- School of Nursing, Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - C Elizabeth Shaaban
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Roland J Thorpe
- Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
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46
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Islam M, Strawn M, Behura SK. Fetal origin of sex‐bias brain aging. FASEB J 2022; 36:e22463. [DOI: 10.1096/fj.202200255rr] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023]
Affiliation(s)
- Maliha Islam
- Division of Animal Sciences University of Missouri Columbia Missouri USA
| | - Monica Strawn
- Division of Animal Sciences University of Missouri Columbia Missouri USA
| | - Susanta K. Behura
- Division of Animal Sciences University of Missouri Columbia Missouri USA
- MU Institute for Data Science and Informatics University of Missouri Columbia Missouri USA
- Interdisciplinary Neuroscience Program University of Missouri Columbia Missouri USA
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47
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Millar PR, Luckett PH, Gordon BA, Benzinger TLS, Schindler SE, Fagan AM, Cruchaga C, Bateman RJ, Allegri R, Jucker M, Lee JH, Mori H, Salloway SP, Yakushev I, Morris JC, Ances BM. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. Neuroimage 2022; 256:119228. [PMID: 35452806 PMCID: PMC9178744 DOI: 10.1016/j.neuroimage.2022.119228] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 12/29/2022] Open
Abstract
"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University, St. Louis, MO 63110, USA.
| | - Patrick H Luckett
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka City University Medical School, Abenoku, Osaka, 545-8585, Japan, Nagaoka Sutoku University
| | | | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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Wan MD, Liu H, Liu XX, Zhang WW, Xiao XW, Zhang SZ, Jiang YL, Zhou H, Liao XX, Zhou YF, Tang BS, Wang JL, Guo JF, Jiao B, Shen L. Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer’s disease. Front Aging Neurosci 2022; 14:906519. [PMID: 35966797 PMCID: PMC9374170 DOI: 10.3389/fnagi.2022.906519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/13/2022] [Indexed: 12/11/2022] Open
Abstract
The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer’s disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aβ1–42, Aβ1–40, Aβ1–42/Aβ1–40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aβ1–42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.
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Affiliation(s)
- Mei-dan Wan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xi-xi Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Wei-wei Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xue-wen Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Si-zhe Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ya-ling Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xin-xin Liao
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Ya-fang Zhou
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Bei-sha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
| | - Jun-Ling Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
| | - Ji-feng Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
| | - Bin Jiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Bin Jiao,
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China
- *Correspondence: Lu Shen,
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Acharya D, Mukherjea A, Cao J, Ruesch A, Schmitt S, Yang J, Smith MA, Kainerstorfer JM. Non-Invasive Spectroscopy for Measuring Cerebral Tissue Oxygenation and Metabolism as a Function of Cerebral Perfusion Pressure. Metabolites 2022; 12:metabo12070667. [PMID: 35888791 PMCID: PMC9323243 DOI: 10.3390/metabo12070667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) measure cerebral hemodynamics, which in turn can be used to assess the cerebral metabolic rate of oxygen (CMRO2) and cerebral autoregulation (CA). However, current mathematical models for CMRO2 estimation make assumptions that break down for cerebral perfusion pressure (CPP)-induced changes in CA. Here, we performed preclinical experiments with controlled changes in CPP while simultaneously measuring NIRS and DCS at rest. We observed changes in arterial oxygen saturation (~10%) and arterial blood volume (~50%) with CPP, two variables often assumed to be constant in CMRO2 estimations. Hence, we propose a general mathematical model that accounts for these variations when estimating CMRO2 and validate its use for CA monitoring on our experimental data. We observed significant changes in the various oxygenation parameters, including the coupling ratio (CMRO2/blood flow) between regions of autoregulation and dysregulation. Our work provides an appropriate model and preliminary experimental evidence for the use of NIRS- and DCS-based tissue oxygenation and metabolism metrics for non-invasive diagnosis of CA health in CPP-altering neuropathologies.
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Affiliation(s)
- Deepshikha Acharya
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
| | - Ankita Mukherjea
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
| | - Jiaming Cao
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
| | - Alexander Ruesch
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Samantha Schmitt
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Jason Yang
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
| | - Matthew A. Smith
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Jana M. Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (D.A.); (A.M.); (J.C.); (S.S.); (J.Y.); (M.A.S.)
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
- Correspondence:
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50
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Lee J, Burkett BJ, Min HK, Senjem ML, Lundt ES, Botha H, Graff-Radford J, Barnard LR, Gunter JL, Schwarz CG, Kantarci K, Knopman DS, Boeve BF, Lowe VJ, Petersen RC, Jack CR, Jones DT. Deep learning-based brain age prediction in normal aging and dementia. NATURE AGING 2022; 2:412-424. [PMID: 37118071 PMCID: PMC10154042 DOI: 10.1038/s43587-022-00219-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 03/29/2022] [Indexed: 11/08/2022]
Abstract
Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.
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Affiliation(s)
- Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Emily S Lundt
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.
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