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Tang J, Cao Z, Lei M, Yu Q, Mai Y, Xu J, Liao W, Ruan Y, Shi L, Yang L, Liu J. Heterogeneity of cerebral atrophic rate in mild cognitive impairment and its interactive association with proteins related to microglia activity on longitudinal cognitive changes. Arch Gerontol Geriatr 2024; 127:105582. [PMID: 39079281 DOI: 10.1016/j.archger.2024.105582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 07/09/2024] [Accepted: 07/15/2024] [Indexed: 09/15/2024]
Abstract
BACKGROUND Heterogeneity of cerebral atrophic rate commonly exists in mild cognitive impairment (MCI), which may be associated with microglia-involved neuropathology and have an influence on cognitive outcomes. OBJECTIVE We aim to explore the heterogeneity of cerebral atrophic rate among MCI and its association with plasma proteins related to microglia activity, with further investigation of their interaction effects on long-term cognition. SUBJECTS A total of 630 MCI subjects in the ADNI database were included, of which 260 subjects were available with baseline data on plasma proteins. METHODS Group-based multi-trajectory modeling (GBMT) was used to identify the latent classes with heterogeneous cerebral atrophic rates. Associations between latent classes and plasma proteins related to microglia activity were investigated with generalized linear models. Linear mixed effect models (LME) were implemented to explore the interaction effects between proteins related to microglia activity and identified latent classes on longitudinal cognitive changes. RESULTS Two latent classes were identified and labeled as the slow-atrophy class and the fast-atrophy class. Associations were found between such heterogeneity of atrophic rates and plasma proteins related to microglia activity, especially AXL receptor tyrosine kinase (AXL), CD40 antigen (CD40), and tumor necrosis factor receptor-like 2 (TNF-R2). Interaction effects on longitudinal cognitive changes showed that higher CD40 was associated with faster cognitive decline in the slow-atrophy class and higher AXL or TNF-R2 was associated with slower cognitive decline in the fast-atrophy class. CONCLUSIONS Heterogeneity of atrophic rates at the MCI stage is associated with several plasma proteins related to microglia activity, which show either protective or adverse effects on long-term cognition depending on the variability of atrophic rates.
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Affiliation(s)
- Jingyi Tang
- Department of Neurology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou City, Guangdong Province, MN 510120, China
| | - Zhiyu Cao
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, No.250 East Changgang Road, Guangzhou City, Guangdong Province, MN 510260, China
| | - Ming Lei
- Department of Neurology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou City, Guangdong Province, MN 510120, China
| | - Qun Yu
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, No.250 East Changgang Road, Guangzhou City, Guangdong Province, MN 510260, China
| | - Yingren Mai
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, No.250 East Changgang Road, Guangzhou City, Guangdong Province, MN 510260, China
| | - Jiaxin Xu
- Department of Neurology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou City, Guangdong Province, MN 510120, China
| | - Wang Liao
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, No.250 East Changgang Road, Guangzhou City, Guangdong Province, MN 510260, China
| | - Yuting Ruan
- Department of Rehabilitation, The Second Affiliated Hospital of Guangzhou Medical University, No.250 East Changgang Road, Guangzhou City, Guangdong Province, MN 510260, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen City, Guangdong Province, MN 518000, China; Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, MN 999077, China
| | - Lianhong Yang
- Department of Neurology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou City, Guangdong Province, MN 510120, China.
| | - Jun Liu
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, No.250 East Changgang Road, Guangzhou City, Guangdong Province, MN 510260, China.
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TLS, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga AW, O'Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Nick Bryan R, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Brain aging patterns in a large and diverse cohort of 49,482 individuals. Nat Med 2024:10.1038/s41591-024-03144-x. [PMID: 39147830 DOI: 10.1038/s41591-024-03144-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 06/20/2024] [Indexed: 08/17/2024]
Abstract
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- GE Healthcare, Bellevue, WA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- Site Rostock/Greifswald, German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Data Science & Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Mark A Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sid O'Bryant
- Institute for Translational Research University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Mallar M Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Dept of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, Baltimore, MD, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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3
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Soldan A, Wang J, Pettigrew C, Davatzikos C, Erus G, Hohman TJ, Dumitrescu L, Bilgel M, Resnick SM, Rivera-Rivera LA, Langhough R, Johnson SC, Benzinger T, Morris JC, Laws SM, Fripp J, Masters CL, Albert MS. Alzheimer's disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults. Brain Commun 2024; 6:fcae276. [PMID: 39229494 PMCID: PMC11369827 DOI: 10.1093/braincomms/fcae276] [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: 12/05/2023] [Revised: 06/25/2024] [Accepted: 08/12/2024] [Indexed: 09/05/2024] Open
Abstract
Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE-ɛ4, APOE-ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE-ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE-ɛ4 with atrophy were reduced among those with higher education (P < 0.04) and younger baseline ages (P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (P = 0.035) and the hippocampus (P = 0.014), independent of APOE-ɛ4 status. APOE-ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities (P = 0.014). Additionally, there was an APOE-ɛ4 × AD-PRS interaction in relation to white matter hyperintensities (P = 0.038), with greater increases in white matter hyperintensities among APOE-ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ɛ4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ɛ2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation.
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Affiliation(s)
- Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jiangxia Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Logan Dumitrescu
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, Baltimore, MD 21224, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, Baltimore, MD 21224, USA
| | - Leonardo A Rivera-Rivera
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA
| | - Rebecca Langhough
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA
| | - Tammie Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, WA 6027, Australia
| | - Jurgen Fripp
- Australian E-Health Research Centre, CSIRO Health & Biosecurity, Herston, QLD 4029, Australia
| | - Colin L Masters
- The Florey Institute, University of Melbourne, Parkville, VIC 3052, Australia
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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4
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Nguyen S, McEvoy LK, Espeland MA, Whitsel EA, Lu A, Horvath S, Manson JE, Rapp SR, Shadyab AH. Associations of Epigenetic Age Estimators With Cognitive Function Trajectories in the Women's Health Initiative Memory Study. Neurology 2024; 103:e209534. [PMID: 38857479 PMCID: PMC11226313 DOI: 10.1212/wnl.0000000000209534] [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: 01/04/2024] [Accepted: 04/05/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Epigenetic age estimators indicating faster/slower biological aging vs chronological age independently associate with several age-related outcomes; however, longitudinal associations with cognitive function are understudied. We examined associations of epigenetic age estimators with cognitive function measured annually. METHODS This longitudinal study consisted of older women enrolled in the Women's Health Initiative Memory Study with DNA methylation (DNAm) collected at baseline (1995-1998) from 3 ancillary studies and were followed up to 13 years. Global cognitive function was measured annually by Modified Mini-Mental State Examination (3MS; baseline-2007) and by modified Telephone Interview for Cognitive Status (TICS-m, 2008-2021). We calculated 5 epigenetic age estimators: extrinsic AgeAccel, intrinsic AgeAccel, AgeAccelPheno, AgeAccelGrim2, Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), and AgeAccelGrim2 components (DNA-based plasma protein surrogates). We estimated longitudinal epigenetic age estimator-cognitive function associations using linear mixed-effects models containing age, education, race or ethnicity, and subsequently alcohol, smoking, body mass index, and comorbidities. We examined effect modification by APOE ε4 carriage. RESULTS A total of 795 participants were enrolled. The mean baseline age was 70.8 ± 4 years (10.7% Black, 3.9% Hispanic or Latina, 85.4% White), A 1-SD (0.12) increment in DunedinPACE associated with faster annual declines in TICS-m scores in minimally adjusted (β = -0.118, 95% CI -0.202 to -0.034; p = 0.0006) and fully adjusted (β = -0.123, 95% CI -0.211 to -0.036; p = 0.006) models. AgeAccelPheno associated with faster annual declines in TICS-m with minimal adjustment (β = -0.091, 95% CI -0.176 to -0.006; p = 0.035) but not with full adjustment. No other epigenetic age estimators associated with changes in 3MS or TICS-m. Higher values of DNAm-based surrogates of growth differentiation factor 15, beta-2 microglobulin, Cystatin C, tissue inhibitor metalloproteinase 1, and adrenomedullin associated with faster annual declines in 3MS and TICS-m. Higher DNAm log A1c associated with faster annual declines in TICS-m only. DunedinPACE associated with faster annual declines in 3MS among APOE ε4 carriers but not among noncarriers (p-interaction = 0.020). DISCUSSION Higher DunedinPACE associated with faster declines in TICS-m and 3MS scores among APOE ε4 carriers. DunedinPACE may help identify older women at risk of future cognitive decline. Limitations include the ancillary studies that collected epigenetic data not designed to study epigenetics and cognitive function. We examined epigenetic age estimators with global cognitive function and not specific cognitive domains. Findings may not generalize to men and more diverse populations.
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Affiliation(s)
- Steve Nguyen
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Linda K McEvoy
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Mark A Espeland
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Eric A Whitsel
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Ake Lu
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Steve Horvath
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Joann E Manson
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Stephen R Rapp
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
| | - Aladdin H Shadyab
- From the Division of Epidemiology (S.N., L.K.M., A.H.S.), Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla; Kaiser Permanente Washington Health Research Institute (L.K.M.), Seattle, WA; Departments of Internal Medicine and Biostatistics and Data Science (M.A.E.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Epidemiology (E.A.W.), Gillings School of Global Public Health; Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill; Altos Labs (A.L., S.H.), San Diego, CA; Department of Epidemiology (S.H.), UCLA Fielding School of Public Health, Los Angeles, CA; Division of Preventive Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry & Behavioral Medicine (S.R.R.), Wake Forest School of Medicine, Winston-Salem, NC; and Division of Geriatrics, Gerontology, and Palliative Care (A.H.S.), Department of Medicine, University of California, San Diego, La Jolla
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5
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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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6
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Wang M, Wei M, Wang L, Song J, Rominger A, Shi K, Jiang J. Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum. Brain Sci 2024; 14:575. [PMID: 38928575 PMCID: PMC11201453 DOI: 10.3390/brainsci14060575] [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: 05/02/2024] [Revised: 05/22/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
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Affiliation(s)
- Min Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China
| | - Luyao Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Jun Song
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Computer Aided Medical Procedures, School of Computation, Information and Technology, Technical University of Munich, 85748 Munich, Germany
| | - Jiehui Jiang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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7
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Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024; 81:456-467. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
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Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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8
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Rodrigues EA, Christie GJ, Cosco T, Farzan F, Sixsmith A, Moreno S. A Subtype Perspective on Cognitive Trajectories in Healthy Aging. Brain Sci 2024; 14:351. [PMID: 38672003 PMCID: PMC11048421 DOI: 10.3390/brainsci14040351] [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: 02/17/2024] [Revised: 03/25/2024] [Accepted: 03/30/2024] [Indexed: 04/28/2024] Open
Abstract
Cognitive aging is a complex and dynamic process characterized by changes due to genetics and environmental factors, including lifestyle choices and environmental exposure, which contribute to the heterogeneity observed in cognitive outcomes. This heterogeneity is particularly pronounced among older adults, with some individuals maintaining stable cognitive function while others experience complex, non-linear changes, making it difficult to identify meaningful decline accurately. Current research methods range from population-level modeling to individual-specific assessments. In this work, we review these methodologies and propose that population subtyping should be considered as a viable alternative. This approach relies on early individual-specific detection methods that can lead to an improved understanding of changes in individual cognitive trajectories. The improved understanding of cognitive trajectories through population subtyping can lead to the identification of meaningful changes and the determination of timely, effective interventions. This approach can aid in informing policy decisions and in developing targeted interventions that promote cognitive health, ultimately contributing to a more personalized understanding of the aging process within society and reducing the burden on healthcare systems.
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Affiliation(s)
- Emma A. Rodrigues
- School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | | | - Theodore Cosco
- Department of Gerontology, Simon Fraser University, Vancouver, BC V6B 5K3, Canada
| | - Faranak Farzan
- School of Mechatronics and Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Andrew Sixsmith
- Department of Gerontology, Simon Fraser University, Vancouver, BC V6B 5K3, Canada
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
- Circle Innovation, Simon Fraser University, Surrey, BC V3T 0A3, Canada
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9
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Vipin A, Lee BTK, Kumar D, Soo SA, Leow YJ, Ghildiyal S, Lee FPHE, Hilal S, Kandiah N. The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes. Alzheimers Res Ther 2024; 16:40. [PMID: 38368378 PMCID: PMC10874041 DOI: 10.1186/s13195-024-01410-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 02/05/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. METHODS Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. RESULTS Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). CONCLUSIONS Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.
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Affiliation(s)
- Ashwati Vipin
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore
| | - Bernett Teck Kwong Lee
- Centre for Biomedical Informatics, 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Dilip Kumar
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore
| | - See Ann Soo
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore
| | - Yi Jin Leow
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore
| | - Smriti Ghildiyal
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore
| | - Faith Phemie Hui En Lee
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore
| | - Saima Hilal
- Saw Swee Hock School of Public Health, Tahir Foundation Building, 12 Science Drive 2, National University of Singapore and National University Health System, Singapore, 117549, Singapore
- Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Nagaendran Kandiah
- Dementia Research Centre (Singapore), 11 Mandalay Road, Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore, 308232, Singapore.
- Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
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10
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Clark AL, Thomas KR, Ortega N, Haley AP, Duarte A, O'Bryant S. Empirically derived psychosocial-behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS-HD: Associations with AD biomarkers and cognitive outcomes. Alzheimers Dement 2024; 20:1360-1373. [PMID: 37990803 PMCID: PMC10917046 DOI: 10.1002/alz.13544] [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: 07/25/2023] [Revised: 09/21/2023] [Accepted: 10/14/2023] [Indexed: 11/23/2023]
Abstract
INTRODUCTION Identification of psychosocial-behavioral phenotypes to understand within-group heterogeneity in risk and resiliency to Alzheimer's disease (AD) within Black/African American and Hispanic/Latino older adults is essential for the implementation of precision health approaches. METHODS A cluster analysis was performed on baseline measures of socioeconomic resources (annual income, social support, occupational complexity) and psychiatric distress (chronic stress, depression, anxiety) for 1220 racially/ethnically minoritized adults enrolled in the Health and Aging Brain Study-Health Disparities (HABS-HD). Analyses of covariance adjusting for sociodemographic factors examined phenotype differences in cognition and plasma AD biomarkers. RESULTS The cluster analysis identified (1) Low Resource/High Distress (n = 256); (2) High Resource/Low Distress (n = 485); and (3) Low Resource/Low Distress (n = 479) phenotypes. The Low Resource/High Distress phenotype displayed poorer cognition and higher plasma neurofilament light chain; differences between the High Resource/Low Distress and Low Resource/Low Distress phenotypes were minimal. DISCUSSION The identification of psychosocial-behavioral phenotypes within racially/ethnically minoritized older adults is crucial to the development of targeted AD prevention and intervention efforts.
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Affiliation(s)
- Alexandra L. Clark
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
- Research ServiceVA San Diego Healthcare SystemSan DiegoCaliforniaUSA
| | - Kelsey R. Thomas
- Research ServiceVA San Diego Healthcare SystemSan DiegoCaliforniaUSA
- Department of PsychiatryUniversity of California San Diego Medical SchoolLa JollaCaliforniaUSA
| | - Nazareth Ortega
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
| | - Andreana P. Haley
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
| | - Audrey Duarte
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
| | - Sid O'Bryant
- Institute for Translational ResearchUniversity of North Texas Health Science CenterFort WorthTexasUSA
- Department of Family MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
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11
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Liu L, Lin L, Sun S, Wu S. Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset. Bioengineering (Basel) 2024; 11:124. [PMID: 38391610 PMCID: PMC10886122 DOI: 10.3390/bioengineering11020124] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.
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Affiliation(s)
- Lingyu Liu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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12
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Chen P, Zhang S, Zhao K, Kang X, Rittman T, Liu Y. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review. Brain Res 2024; 1823:148675. [PMID: 37979603 DOI: 10.1016/j.brainres.2023.148675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Shirui Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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13
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Honnorat N, Seshadri S, Killiany R, Blangero J, Glahn DC, Fox P, Habes M. Riemannian frameworks for the harmonization of resting-state functional MRI scans. Med Image Anal 2024; 91:103043. [PMID: 38029722 PMCID: PMC11157681 DOI: 10.1016/j.media.2023.103043] [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] [Revised: 11/03/2023] [Accepted: 11/24/2023] [Indexed: 12/01/2023]
Abstract
Magnetic Resonance Imaging provides unprecedented images of the brain. Unfortunately, scanners and acquisition protocols can significantly impact MRI scans. The development of statistical methods able to reduce this variability without altering the relevant information in the scans, often coined harmonization methods, has been the topic of an increasing research effort supported by the recent growth of publicly available neuroimaging data sets and new possibilities for combining them to achieve greater statistical power. In this work, we focus on the challenges specifically raised by the harmonization of resting-state functional MRI scans. We propose to harmonize resting-state fMRI scans by reducing the impact of covariates such as scanner differences and scanning protocols on their associated functional connectomes and then propagating the changes back to the rs-fMRI time series. We use Riemannian geometric frameworks to preserve the mathematical properties of functional connectomes during their harmonization, and we demonstrate how state-of-the-art harmonization methods can be embedded within these frameworks to reduce covariates effects while preserving the relevant clinical information associated with aging or brain disorders. During our experiments, a large set of synthetic data was generated and processed to compare eighty variants of the proposed approach. The framework achieving the best harmonization was then applied to three low-dimensional data sets made of 712 sets of fMRI time series provided by the ABIDE consortium and two high-dimensional data sets obtained by processing 1527 rs-fMRI scans provided by the Human Connectome Project, the Framingham Heart Study and the Genetics of Brain Structure and Function study. These experiments established that our new framework could successfully harmonize low-dimensional connectomes and voxelwise functional time series and confirmed the need for preserving connectomes properties during their harmonization.
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Affiliation(s)
- Nicolas Honnorat
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Sudha Seshadri
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ron Killiany
- Center for Biomedical Imaging, Boston University Medical School, Boston, MA, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Fox
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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14
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Lay-Yee R, Hariri AR, Knodt AR, Barrett-Young A, Matthews T, Milne BJ. Social isolation from childhood to mid-adulthood: is there an association with older brain age? Psychol Med 2023; 53:7874-7882. [PMID: 37485695 PMCID: PMC10755222 DOI: 10.1017/s0033291723001964] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/19/2023] [Accepted: 06/23/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Older brain age - as estimated from structural MRI data - is known to be associated with detrimental mental and physical health outcomes in older adults. Social isolation, which has similar detrimental effects on health, may be associated with accelerated brain aging though little is known about how different trajectories of social isolation across the life course moderate this association. We examined the associations between social isolation trajectories from age 5 to age 38 and brain age assessed at age 45. METHODS We previously created a typology of social isolation based on onset during the life course and persistence into adulthood, using group-based trajectory analysis of longitudinal data from a New Zealand birth cohort. The typology comprises four groups: 'never-isolated', 'adult-only', 'child-only', and persistent 'child-adult' isolation. A brain age gap estimate (brainAGE) - the difference between predicted age from structural MRI date and chronological age - was derived at age 45. We undertook analyses of brainAGE with trajectory group as the predictor, adjusting for sex, family socio-economic status, and a range of familial and child-behavioral factors. RESULTS Older brain age in mid-adulthood was associated with trajectories of social isolation after adjustment for family and child confounders, particularly for the 'adult-only' group compared to the 'never-isolated' group. CONCLUSIONS Although our findings are associational, they indicate that preventing social isolation, particularly in mid-adulthood, may help to avert accelerated brain aging associated with negative health outcomes later in life.
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Affiliation(s)
- Roy Lay-Yee
- Centre of Methods and Policy Application in the Social Sciences, and School of Social Sciences, Faculty of Arts, University of Auckland, Auckland, New Zealand
| | - Ahmad R. Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Annchen R. Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | | | - Timothy Matthews
- Department of Social Genetic & Developmental Psychiatry, Institute of Psychiatry, King's College London, London, UK
| | - Barry J. Milne
- Centre of Methods and Policy Application in the Social Sciences, and School of Social Sciences, Faculty of Arts, University of Auckland, Auckland, New Zealand
- Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand
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15
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Gao J, Liu J, Xu Y, Peng D, Wang Z. Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease. Front Neurosci 2023; 17:1222751. [PMID: 37457008 PMCID: PMC10347411 DOI: 10.3389/fnins.2023.1222751] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI). Methods In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD. Results The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age. Discussion Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.
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Affiliation(s)
| | | | | | | | - Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
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16
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Habes M, Jacobson AM, Braffett BH, Rashid T, Ryan CM, Shou H, Cui Y, Davatzikos C, Luchsinger JA, Biessels GJ, Bebu I, Gubitosi-Klug RA, Bryan RN, Nasrallah IM. Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes. JAMA Netw Open 2023; 6:e2316182. [PMID: 37261829 PMCID: PMC10236234 DOI: 10.1001/jamanetworkopen.2023.16182] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/09/2023] [Indexed: 06/02/2023] Open
Abstract
Importance Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging. Objective To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices. Design, Setting, and Participants This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT/EDIC. Data analyses were performed between July 2020 and April 2022. Exposure T1D diagnosis. Main Outcomes and Measures Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy. Results This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%] were older than 65 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: β, 6.16; SE, 0.71; control participants: β, 1.04; SE, 0.04; P < .001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (β, -0.04; SE, 0.01; P < .001), but not among controls. Conclusions and Relevance The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.
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Affiliation(s)
- Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Alan M. Jacobson
- NYU Long Island School of Medicine, NYU Langone Hospital-Long Island, Mineola, New York
| | | | - Tanweer Rashid
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Geert J. Biessels
- Department of Neurology, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ionut Bebu
- George Washington University, Biostatistics Center, Rockville, Maryland
| | - Rose A. Gubitosi-Klug
- Case Western Reserve University School of Medicine, Rainbow Babies and Children's Hospital, Cleveland, Ohio
| | - R. Nick Bryan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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17
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Fountain-Zaragoza S, Liu H, Benitez A. Functional Network Alterations Associated with Cognition in Pre-Clinical Alzheimer's Disease. Brain Connect 2023; 13:275-286. [PMID: 36606679 PMCID: PMC10280291 DOI: 10.1089/brain.2022.0032] [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: 01/07/2023] Open
Abstract
Objective: Accumulation of cerebral amyloid-β (Aβ) is a risk factor for cognitive decline and defining feature of Alzheimer's disease (AD). Aβ is implicated in brain network disruption, but the extent to which these changes correspond with observable cognitive deficits in pre-clinical AD has not been tested. This study utilized individual-specific functional parcellations to sensitively evaluate the relationship between network connectivity and cognition in adults with and without Aβ deposition. Participants and Methods: Cognitively unimpaired adults ages 45-85 completed amyloid positron emission tomography, resting-state-functional magnetic resonance imaging (fMRI), and neuropsychological tests of episodic memory and executive function (EF). Participants in the upper tertile of mean standard uptake value ratio were considered Aβ+ (n = 50) while others were Aβ- (n = 99). Individualized functional network parcellations were generated from resting-state fMRI data. We examined the effects of group, network, and group-by-network interactions on memory and EF. Results: We observed several interactions such that within the Aβ+ group, preserved network integrity (i.e., greater connectivity within specific networks) was associated with better cognition, whereas network desegregation (i.e., greater connectivity between relative to within networks) was associated with worse cognition. This dissociation was most apparent for cognitive networks (frontoparietal, dorsal and ventral attention, limbic, and default mode), with connectivity relating to EF in the Aβ+ group specifically. Conclusions: Using an innovative approach to constructing individual-specified resting-state functional connectomes, we were able to detect differences in brain-cognition associations in pre-clinical AD. Our findings provide novel insight into specific functional network alterations occurring in the presence of Aβ that relate to cognitive function in asymptomatic individuals. Impact statement Elevated cerebral amyloid-β is a biomarker of pre-clinical Alzheimer's disease (AD). Associations between amyloidosis, functional network disruption, and cognitive impairment are evident in the later stages of AD, but these effects have not been substantiated in pre-clinical AD. Using individual-specific parcellations that maximally localize functional networks, we identify network alterations that relate to cognition in pre-clinical AD that have not been previously reported. We demonstrate that these effects localize to networks implicated in cognition. Our findings suggest that there may be subtle, amyloid-related alterations in the functional connectome that are detectable in pre-clinical AD, with potential implications for cognition in asymptomatic individuals.
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Affiliation(s)
- Stephanie Fountain-Zaragoza
- Department of Health Sciences and Research, Medical University of South Carolina, Charleston, South Carolina, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Andreana Benitez
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neurology, and Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
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18
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Nyberg L, Andersson M, Lundquist A, Baaré WFC, Bartrés-Faz D, Bertram L, Boraxbekk CJ, Brandmaier AM, Demnitz N, Drevon CA, Duezel S, Ebmeier KP, Ghisletta P, Henson R, Jensen DEA, Kievit RA, Knights E, Kühn S, Lindenberger U, Plachti A, Pudas S, Roe JM, Madsen KS, Solé-Padullés C, Sommerer Y, Suri S, Zsoldos E, Fjell AM, Walhovd KB. Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates. Cereb Cortex 2023; 33:5075-5081. [PMID: 36197324 PMCID: PMC10151879 DOI: 10.1093/cercor/bhac400] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.
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Affiliation(s)
- Lars Nyberg
- Department of Radiation Sciences (Radiology), Umeå University, 901 87 Umeå, Sweden
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Micael Andersson
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
| | - Anders Lundquist
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
- Department of Statistics, USBE, Umeå University, Umeå S-90187, Sweden
| | - William F C Baaré
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institut de Neurociències, Universitat de Barcelona, and Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Lars Bertram
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, 23562 Lübeck, Germany
| | - Carl-Johan Boraxbekk
- Department of Radiation Sciences (Radiology), Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
- Faculty of Medical and Health Sciences, Institute for Clinical Medicine, University of Copenhagen, 2400 Copenhagen, Denmark
- Department of Neurology, Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital - Bispebjerg and Frederiksberg, 2400 Copenhagen, Denmark
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
- MSB Medical School Berlin, 14197 Berlin, Germany
- Max Plank UCL Centre for Computational Psychiatry and Ageing Research, 14195 Berlin, Germany, and London, UK
| | - Naiara Demnitz
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
| | - Christian A Drevon
- Vitas AS, Science Park, 0349 Oslo, Norway
- Department of Nutrition, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, 0317 Oslo Norway
| | - Sandra Duezel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
| | - Paolo Ghisletta
- Faculty of Psychology and Educational Sciences, University of Geneva, 1204 Geneva, Switzerland
- UniDistance Suisse, 3900 Brig, Switzerland
- Swiss National Centre of Competence in Research LIVES, University of Geneva, 1204 Geneva, Switzerland
| | - Richard Henson
- Medical Research Council Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 7EF, England
| | - Daria E A Jensen
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, OX3 9DU Oxford, UK
| | - Rogier A Kievit
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 GD Nijmegen, The Netherlands
| | - Ethan Knights
- Medical Research Council Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 7EF, England
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development & Clinic for Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
- Max Plank UCL Centre for Computational Psychiatry and Ageing Research, 14195 Berlin, Germany, and London, UK
| | - Anna Plachti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
| | - Sara Pudas
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, 2650 Copenhagen, Denmark
- Radiography, Department of Technology, University College Copenhagen, 2200 Copenhagen N, Denmark
| | - Cristina Solé-Padullés
- Department of Medicine, Faculty of Medicine and Health Sciences, Institut de Neurociències, Universitat de Barcelona, and Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Yasmine Sommerer
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, 23562 Lübeck, Germany
| | - Sana Suri
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 GD Nijmegen, The Netherlands
| | - Enikő Zsoldos
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 GD Nijmegen, The Netherlands
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Center for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0373 Oslo, Norway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Center for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0373 Oslo, Norway
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19
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Fingelkurts AA, Fingelkurts AA. Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications. Brain Sci 2023; 13:520. [PMID: 36979330 PMCID: PMC10046544 DOI: 10.3390/brainsci13030520] [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: 02/20/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan. METHODS AND OBJECTIVE Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months). RESULTS We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately. CONCLUSIONS Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.
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20
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Dintica CS, Habes M, Erus G, Vittinghoff E, Davatzikos C, Nasrallah IM, Launer LJ, Sidney S, Yaffe K. Elevated blood pressure is associated with advanced brain aging in mid-life: A 30-year follow-up of The CARDIA Study. Alzheimers Dement 2023; 19:924-932. [PMID: 35779250 PMCID: PMC9806185 DOI: 10.1002/alz.12725] [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: 01/31/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear. METHODS We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses. RESULTS Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively). CONCLUSION Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.
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Affiliation(s)
| | - Mohamad Habes
- University of Pennsylvania, Philadelphia, PA
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Guray Erus
- University of Pennsylvania, Philadelphia, PA
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21
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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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Wrigglesworth J, Ryan J, Ward PGD, Woods RL, Storey E, Egan GF, Murray A, Espinoza SE, Shah RC, Trevaks RE, Ward SA, Harding IH. Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function. Front Aging Neurosci 2023; 14:1063721. [PMID: 36688169 PMCID: PMC9846261 DOI: 10.3389/fnagi.2022.1063721] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/29/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction Neuroimaging-based 'brain age' can identify individuals with 'advanced' or 'resilient' brain aging. Brain-predicted age difference (brain-PAD) is predictive of cognitive and physical health outcomes. However, it is unknown how individual health and lifestyle factors may modify the relationship between brain-PAD and future cognitive or functional performance. We aimed to identify health-related subgroups of older individuals with resilient or advanced brain-PAD, and determine if membership in these subgroups is differentially associated with changes in cognition and frailty over three to five years. Methods Brain-PAD was predicted from T1-weighted images acquired from 326 community-dwelling older adults (73.8 ± 3.6 years, 42.3% female), recruited from the larger ASPREE (ASPirin in Reducing Events in the Elderly) trial. Participants were grouped as having resilient (n=159) or advanced (n=167) brain-PAD, and latent class analysis (LCA) was performed using a set of cognitive, lifestyle, and health measures. We examined associations of class membership with longitudinal change in cognitive function and frailty deficit accumulation index (FI) using linear mixed models adjusted for age, sex and education. Results Subgroups of resilient and advanced brain aging were comparable in all characteristics before LCA. Two typically similar latent classes were identified for both subgroups of brain agers: class 1 were characterized by low prevalence of obesity and better physical health and class 2 by poor cardiometabolic, physical and cognitive health. Among resilient brain agers, class 1 was associated with a decrease in cognition, and class 2 with an increase over 5 years, though was a small effect that was equivalent to a 0.04 standard deviation difference per year. No significant class distinctions were evident with FI. For advanced brain agers, there was no evidence of an association between class membership and changes in cognition or FI. Conclusion These results demonstrate that the relationship between brain age and cognitive trajectories may be influenced by other health-related factors. In particular, people with age-resilient brains had different trajectories of cognitive change depending on their cognitive and physical health status at baseline. Future predictive models of aging outcomes will likely be aided by considering the mediating or synergistic influence of multiple lifestyle and health indices alongside brain age.
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Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Phillip G. D. Ward
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Vic, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Gary F. Egan
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Vic, Australia
| | - Anne Murray
- Hennepin Healthcare and Berman Center for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, MN, United States
- Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, United States
| | - Sara E. Espinoza
- Division of Geriatrics, Gerontology & Palliative Medicine, Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, Houston, TX, United States
- Geriatric Research, Education & Clinical Center, South Texas Veterans Health Care System, San Antonio, TX, United States
| | - Raj C. Shah
- Department of Family & Preventive Medicine and the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Ruth E. Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Stephanie A. Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
- Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Ian H. Harding
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
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23
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Dintica CS, Habes M, Erus G, Simone T, Schreiner P, Yaffe K. Long-term depressive symptoms and midlife brain age. J Affect Disord 2023; 320:436-441. [PMID: 36202300 PMCID: PMC10115134 DOI: 10.1016/j.jad.2022.09.164] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Evidence suggests that depression may be a risk factor for dementia in older adults, but the link between depressive symptoms and brain health earlier in life is less understood. Our aim was to investigate the association between long-term depressive symptoms in young to mid-adulthood and a measure of brain age derived from structural MRI. METHODS From the Coronary Artery Risk Development in Young Adults study, we identified 649 participants (age 23-36 at baseline) with brain MRI and cognitive testing. Long-term depressive symptoms were measured with the Center for Epidemiological Studies Depression scale (CESD) six times across 25 years and analyzed as time-weighted averages (TWA). Brain age was derived using previously validated high dimensional neuroimaging pattern analysis, quantifying individual differences in age-related atrophy. Elevated depressive symptoms were defined as CES-D ≥16. Linear regression was used to test the association between TWA depressive symptoms, brain aging, and cognition. RESULTS Each standard deviation (5-points) increment in TWA depression symptoms over 25 years was associated with one-year greater brain age (β: 1.14, 95 % confidence interval [CI]: 0.57 to 1.71). Participants with elevated TWA depressive symptoms had on average a 3-year greater brain age (β: 2.75, 95 % CI: 0.43 to 5.08). Moreover, elevated depressive symptoms were associated with higher odds of poor cognitive function in midlife (OR: 3.30, 95 % CI: 1.37 to 7.97). LIMITATIONS Brain age was assessed at one time, limiting our ability to evaluate the temporality of depressive symptoms and brain aging. CONCLUSIONS Elevated depressive symptoms in early adulthood may have implications for brain health as early as in midlife.
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Affiliation(s)
| | - Mohamad Habes
- University of Pennsylvania, Philadelphia, PA, USA; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA.
| | - Guray Erus
- University of Pennsylvania, Philadelphia, PA, USA.
| | - Tamar Simone
- Northern California Institute for Research and Education, San Francisco, CA, USA.
| | - Pamela Schreiner
- Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA.
| | - Kristine Yaffe
- University of California, San Francisco, California, CA, USA; VA Medical Center, San Francisco, CA, USA.
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Resting-state functional connectivity does not predict individual differences in the effects of emotion on memory. Sci Rep 2022; 12:14481. [PMID: 36008438 PMCID: PMC9411155 DOI: 10.1038/s41598-022-18543-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 08/16/2022] [Indexed: 11/28/2022] Open
Abstract
Emotion-laden events and objects are typically better remembered than neutral ones. This is usually explained by stronger functional coupling in the brain evoked by emotional content. However, most research on this issue has focused on functional connectivity evoked during or after learning. The effect of an individual’s functional connectivity at rest is unknown. Our pre-registered study addresses this issue by analysing a large database, the Cambridge Centre for Ageing and Neuroscience, which includes resting-state data and emotional memory scores from 303 participants aged 18–87 years. We applied regularised regression to select the relevant connections and replicated previous findings that whole-brain resting-state functional connectivity can predict age and intelligence in younger adults. However, whole-brain functional connectivity predicted neither an emotional enhancement effect (i.e., the degree to which emotionally positive or negative events are remembered better than neutral events) nor a positivity bias effect (i.e., the degree to which emotionally positive events are remembered better than negative events), failing to support our pre-registered hypotheses. These results imply a small or no association between individual differences in functional connectivity at rest and emotional memory, and support recent notions that resting-state functional connectivity is not always useful in predicting individual differences in behavioural measures.
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25
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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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Zuroff L, Wisse LEM, Glenn T, Xie SX, Nasrallah IM, Habes M, Dubroff J, de Flores R, Xie L, Yushkevich P, Doshi J, Davatsikos C, Shaw LM, Tropea TF, Chen-Plotkin AS, Wolk DA, Das S, Mechanic-Hamilton D. Self- and Partner-Reported Subjective Memory Complaints: Association with Objective Cognitive Impairment and Risk of Decline. J Alzheimers Dis Rep 2022; 6:411-430. [PMID: 36072364 PMCID: PMC9397901 DOI: 10.3233/adr-220013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2022] [Indexed: 11/15/2022] Open
Abstract
Background Episodic memory decline is a hallmark of Alzheimer's disease (AD). Subjective memory complaints (SMCs) may represent one of the earliest signs of impending cognitive decline. The degree to which self- or partner-reported SMCs predict cognitive change remains unclear. Objective We aimed to evaluate the relationship between self- and partner-reported SMCs, objective cognitive performance, AD biomarkers, and risk of future decline in a well-characterized longitudinal memory center cohort. We also evaluated whether study partner characteristics influence reports of SMCs. Methods 758 participants and 690 study partners were recruited from the Penn Alzheimer's Disease Research Center Clinical Core. Participants included those with Normal Cognition, Mild Cognitive Impairment, and AD. SMCs were measured using the Prospective and Retrospective Memory Questionnaire (PRMQ), and were evaluated for their association with cognition, genetic, plasma, and neuroimaging biomarkers of AD, cognitive and functional decline, and diagnostic progression over an average of four years. Results We found that partner-reported SMCs were more consistent with cognitive test performance and increasing symptom severity than self-reported SMCs. Partner-reported SMCs showed stronger correlations with AD-associated brain atrophy, plasma biomarkers of neurodegeneration, and longitudinal cognitive and functional decline. A 10-point increase on baseline PRMQ increased the annual risk of diagnostic progression by approximately 70%. Study partner demographics and relationship to participants influenced reports of SMCs in AD participants only. Conclusion Partner-reported SMCs, using the PRMQ, have a stronger relationship with the neuroanatomic and cognitive changes associated with AD than patient-reported SMCs. Further work is needed to evaluate whether SMCs could be used to screen for future decline.
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Affiliation(s)
- Leah Zuroff
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Trevor Glenn
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Jacob Dubroff
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- Université de Caen Normandie, INSERM UMRS U1237, Caen, France
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatsikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas F. Tropea
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alice S. Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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de Dieu Uwisengeyimana J, Nguchu BA, Wang Y, Zhang D, Liu Y, Jiang Z, Wang X, Qiu B. Longitudinal resting-state functional connectivity and regional brain atrophy-based biomarkers of preclinical cognitive impairment in healthy old adults. Aging Clin Exp Res 2022; 34:1303-1313. [PMID: 35023051 DOI: 10.1007/s40520-021-02067-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 12/27/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Intervention against age-related neurodegenerative diseases may be difficult once extensive structural and functional deteriorations have already occurred in the brain. AIM Investigating 6-year longitudinal changes and implications of regional brain atrophy and functional connectivity in the triple-network model as biomarkers of preclinical cognitive impairment in healthy aging. METHODS We acquired longitudinal cognitive scores and magnetic resonance imaging (MRI) data from 74 healthy old adults. Resting-state functional MRI (rs-fMRI) analysis was conducted using FSL6.0.1 to examine functional connectivity changes and regional brain morphometries were quantified using FreeSurfer5.3. Finally, we cross-validated and compared two support vector machine (SVM) regression models to predict future 6-year cognition score from the baseline regional brain atrophy and resting-state functional connectivity (rs-FC) measures. RESULTS After a 6-year follow-up, our results (P < 0.05-corrected) indicated significant connectivity reduction within all the three brain networks, significant differences in regional brain volumes and cortical thickness. We also observed significant improvement in episodic memory and significant decline in executive functions. Finally, comparing the two models, we observed that regional brain atrophy predictors were more efficient in approximating future 6-year cognitive scores (R = 0.756, P < 0.0001) than rs-FC predictors (R = 0.6, P < 0.0001). CONCLUSION This study used longitudinal data to keep subject variability low and to increase the validity of the results. We demonstrated significant changes in structural and functional MRI over 6 years. Our findings present a potential neuroimaging-based biomarker to detect cognitive impairment and prevent risks of neurodegenerative diseases in healthy old adults.
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Affiliation(s)
- Jean de Dieu Uwisengeyimana
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China
- Department of Electrical and Electronics Engineering, College of Science and Technology, University of Rwanda, Kigali, Rwanda
| | - Benedictor Alexander Nguchu
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Yaming Wang
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Du Zhang
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Yanpeng Liu
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Zhoufan Jiang
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Xiaoxiao Wang
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China.
| | - Bensheng Qiu
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China.
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Merenstein JL, Bennett IJ. Bridging patterns of neurocognitive aging across the older adult lifespan. Neurosci Biobehav Rev 2022; 135:104594. [PMID: 35227712 PMCID: PMC9888009 DOI: 10.1016/j.neubiorev.2022.104594] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/27/2022] [Accepted: 02/23/2022] [Indexed: 02/02/2023]
Abstract
Magnetic resonance imaging (MRI) studies of brain and neurocognitive aging rarely include oldest-old adults (ages 80 +). But predictions of neurocognitive aging theories derived from MRI findings in younger-old adults (ages ~55-80) may not generalize into advanced age, particularly given the increased prevalence of cognitive impairment/dementia in the oldest-old. Here, we reviewed the MRI literature in oldest-old adults and interpreted findings within the context of regional variation, compensation, brain maintenance, and reserve theories. Structural MRI studies revealed regional variation in brain aging as larger age effects on medial temporal and posterior regions for oldest-old than younger-old adults. They also revealed that brain maintenance explained preserved cognitive functioning into the tenth decade of life. Very few functional MRI studies examined compensatory activity in oldest-old adults who perform as well as younger groups, although there was evidence that higher brain reserve in oldest-old adults may mediate effects of brain aging on cognition. Despite some continuity, different cognitive and neural profiles across the older adult lifespan should be addressed in modern neurocognitive aging theories.
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29
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Zheng Y, Habes M, Gonzales M, Pomponio R, Nasrallah I, Khan S, Vaughan DE, Davatzikos C, Seshadri S, Launer L, Sorond F, Sedaghat S, Wainwright D, Baccarelli A, Sidney S, Bryan N, Greenland P, Lloyd-Jones D, Yaffe K, Hou L. Mid-life epigenetic age, neuroimaging brain age, and cognitive function: coronary artery risk development in young adults (CARDIA) study. Aging (Albany NY) 2022; 14:1691-1712. [PMID: 35220276 PMCID: PMC8908939 DOI: 10.18632/aging.203918] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 02/08/2022] [Indexed: 11/25/2022]
Abstract
The proportion of aging populations affected by dementia is increasing. There is an urgent need to identify biological aging markers in mid-life before symptoms of age-related dementia present for early intervention to delay the cognitive decline and the onset of dementia. In this cohort study involving 1,676 healthy participants (mean age 40) with up to 15 years of follow up, we evaluated the associations between cognitive function and two classes of novel biological aging markers: blood-based epigenetic aging and neuroimaging-based brain aging. Both accelerated epigenetic aging and brain aging were prospectively associated with worse cognitive outcomes. Specifically, every year faster epigenetic or brain aging was on average associated with 0.19-0.28 higher (worse) Stroop score, 0.04-0.05 lower (worse) RAVLT score, and 0.23-0.45 lower (worse) DSST (all false-discovery-rate-adjusted p <0.05). While epigenetic aging is a more stable biomarker with strong long-term predictive performance for cognitive function, brain aging biomarker may change more dynamically in temporal association with cognitive decline. The combined model using epigenetic and brain aging markers achieved the highest accuracy (AUC: 0.68, p<0.001) in predicting global cognitive function status. Accelerated epigenetic age and brain age at midlife may aid timely identification of individuals at risk for accelerated cognitive decline and promote the development of interventions to preserve optimal functioning across the lifespan.
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Affiliation(s)
- Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mohamad Habes
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mitzi Gonzales
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Raymond Pomponio
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sadiya Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Douglas E. Vaughan
- Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sudha Seshadri
- Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Lenore Launer
- Laboratory of Epidemiology and Population Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Farzaneh Sorond
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Sanaz Sedaghat
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Derek Wainwright
- Departments of Neurological Surgery, Medicine-Hematology and Oncology, Microbiology-Immunology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - Stephen Sidney
- Kaiser Permanente Division of Research, Oakland, CA 94612, USA
| | - Nick Bryan
- Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Kristine Yaffe
- Departments of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
- Department of Neurology University of California, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, CA 94143, USA
- San Francisco VA Medical Center, San Francisco, CA 94143, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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30
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Sisakhti M, Shafaghi L, Batouli SAH. The Volumetric Changes of the Pineal Gland with Age: An Atlas-based Structural Analysis. Exp Aging Res 2022; 48:474-504. [DOI: 10.1080/0361073x.2022.2033593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Minoo Sisakhti
- Department of Cognitive Psychology, Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Lida Shafaghi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Department of Computational Cognition, Humanlab Technologies, Vancouver, Canada
| | - Seyed Amir Hossein Batouli
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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31
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Ramduny J, Bastiani M, Huedepohl R, Sotiropoulos SN, Chechlacz M. The Association Between Inadequate Sleep and Accelerated Brain Ageing. Neurobiol Aging 2022; 114:1-14. [PMID: 35344818 PMCID: PMC9084918 DOI: 10.1016/j.neurobiolaging.2022.02.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/23/2021] [Accepted: 02/14/2022] [Indexed: 01/18/2023]
Affiliation(s)
- Jivesh Ramduny
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; School of Psychology, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; National Institute for Health Research (NIHR), Nottingham Biomedical Research Centre, Queen's Medical Centre, Nottingham, UK
| | - Robin Huedepohl
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; National Institute for Health Research (NIHR), Nottingham Biomedical Research Centre, Queen's Medical Centre, Nottingham, UK.
| | - Magdalena Chechlacz
- School of Psychology, University of Birmingham, Birmingham, UK; Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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32
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Schindler LS, Subramaniapillai S, Barth C, van der Meer D, Pedersen ML, Kaufmann T, Maximov II, Linge J, Leinhard OD, Beck D, Gurholt TP, Voldsbekk I, Suri S, Ebmeier KP, Draganski B, Andreassen OA, Westlye LT, de Lange AMG. Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. Neuroimage Clin 2022; 36:103239. [PMID: 36451350 PMCID: PMC9668664 DOI: 10.1016/j.nicl.2022.103239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
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Affiliation(s)
- Louise S Schindler
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Jennifer Linge
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P Gurholt
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Dept. of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
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Drouin SM, McFall GP, Potvin O, Bellec P, Masellis M, Duchesne S, Dixon RA. Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes. J Alzheimers Dis 2022; 88:97-115. [PMID: 35570482 PMCID: PMC9277685 DOI: 10.3233/jad-215289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses. OBJECTIVE To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers. METHODS We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index. CONCLUSION Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.
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Affiliation(s)
- Shannon M. Drouin
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - G. Peggy McFall
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Pierre Bellec
- Département de Psychologie, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Simon Duchesne
- CERVO Brain Research Centre, Quebec, QC, Canada
- Radiology and Nuclear Medicine Department, Université Laval, Quebec, QC, Canada
| | - Roger A. Dixon
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
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Kokošová V, Filip P, Kec D, Baláž M. Bidirectional Association Between Sleep and Brain Atrophy in Aging. Front Aging Neurosci 2021; 13:726662. [PMID: 34955805 PMCID: PMC8693777 DOI: 10.3389/fnagi.2021.726662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 10/29/2021] [Indexed: 11/23/2022] Open
Abstract
Human brain aging is characterized by the gradual deterioration of its function and structure, affected by the interplay of a multitude of causal factors. The sleep, a periodically repeating state of reversible unconsciousness characterized by distinct electrical brain activity, is crucial for maintaining brain homeostasis. Indeed, insufficient sleep was associated with accelerated brain atrophy and impaired brain functional connectivity. Concurrently, alteration of sleep-related transient electrical events in senescence was correlated with structural and functional deterioration of brain regions responsible for their generation, implying the interconnectedness of sleep and brain structure. This review discusses currently available data on the link between human brain aging and sleep derived from various neuroimaging and neurophysiological methods. We advocate the notion of a mutual relationship between the sleep structure and age-related alterations of functional and structural brain integrity, pointing out the position of high-quality sleep as a potent preventive factor of early brain aging and neurodegeneration. However, further studies are needed to reveal the causality of the relationship between sleep and brain aging.
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Affiliation(s)
- Viktória Kokošová
- Department of Neurology, Faculty of Medicine, University Hospital Brno and Masaryk University, Brno, Czechia
| | - Pavel Filip
- Department of Neurology, First Faculty of Medicine, General University Hospital Prague and Charles University, Prague, Czechia.,Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States
| | - David Kec
- Department of Neurology, Faculty of Medicine, University Hospital Brno and Masaryk University, Brno, Czechia
| | - Marek Baláž
- First Department of Neurology, Faculty of Medicine, University Hospital of St. Anne and Masaryk University, Brno, Czechia
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35
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Bocancea DI, van Loenhoud AC, Groot C, Barkhof F, van der Flier WM, Ossenkoppele R. Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods: A Systematic Review and Meta-analysis. Neurology 2021; 97:474-488. [PMID: 34266918 PMCID: PMC8448552 DOI: 10.1212/wnl.0000000000012499] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 06/14/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND OBJECTIVE There is a lack of consensus on how to optimally define and measure resistance and resilience in brain and cognitive aging. Residual methods use residuals from regression analysis to quantify the capacity to avoid (resistance) or cope (resilience) "better or worse than expected" given a certain level of risk or cerebral damage. We reviewed the rapidly growing literature on residual methods in the context of aging and Alzheimer disease (AD) and performed meta-analyses to investigate associations of residual method-based resilience and resistance measures with longitudinal cognitive and clinical outcomes. METHODS A systematic literature search of PubMed and Web of Science databases (consulted until March 2020) and subsequent screening led to 54 studies fulfilling eligibility criteria, including 10 studies suitable for the meta-analyses. RESULTS We identified articles using residual methods aimed at quantifying resistance (n = 33), cognitive resilience (n = 23), and brain resilience (n = 2). Critical examination of the literature revealed that there is considerable methodologic variability in how the residual measures were derived and validated. Despite methodologic differences across studies, meta-analytic assessments showed significant associations of levels of resistance (hazard ratio [HR] [95% confidence interval (CI)] 1.12 [1.07-1.17]; p < 0.0001) and levels of resilience (HR [95% CI] 0.46 [0.32-0.68]; p < 0.001) with risk of progression to dementia/AD. Resilience was also associated with rate of cognitive decline (β [95% CI] 0.05 [0.01-0.08]; p < 0.01). DISCUSSION This review and meta-analysis supports the usefulness of residual methods as appropriate measures of resilience and resistance, as they capture clinically meaningful information in aging and AD. More rigorous methodologic standardization is needed to increase comparability across studies and, ultimately, application in clinical practice.
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Affiliation(s)
- Diana I Bocancea
- From the Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience (D.I.B., A.C.v.L., C.G., W.M.v.d.F., R.O.), and Department of Radiology and Nuclear Medicine (F.B.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; Department of Epidemiology and Biostatistics (W.M.v.d.F.), VU University Medical Center, Amsterdam, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Anna C van Loenhoud
- From the Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience (D.I.B., A.C.v.L., C.G., W.M.v.d.F., R.O.), and Department of Radiology and Nuclear Medicine (F.B.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; Department of Epidemiology and Biostatistics (W.M.v.d.F.), VU University Medical Center, Amsterdam, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Colin Groot
- From the Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience (D.I.B., A.C.v.L., C.G., W.M.v.d.F., R.O.), and Department of Radiology and Nuclear Medicine (F.B.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; Department of Epidemiology and Biostatistics (W.M.v.d.F.), VU University Medical Center, Amsterdam, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Frederik Barkhof
- From the Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience (D.I.B., A.C.v.L., C.G., W.M.v.d.F., R.O.), and Department of Radiology and Nuclear Medicine (F.B.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; Department of Epidemiology and Biostatistics (W.M.v.d.F.), VU University Medical Center, Amsterdam, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Wiesje M van der Flier
- From the Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience (D.I.B., A.C.v.L., C.G., W.M.v.d.F., R.O.), and Department of Radiology and Nuclear Medicine (F.B.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; Department of Epidemiology and Biostatistics (W.M.v.d.F.), VU University Medical Center, Amsterdam, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Rik Ossenkoppele
- From the Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience (D.I.B., A.C.v.L., C.G., W.M.v.d.F., R.O.), and Department of Radiology and Nuclear Medicine (F.B.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK; Department of Epidemiology and Biostatistics (W.M.v.d.F.), VU University Medical Center, Amsterdam, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
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Voldsbekk I, Barth C, Maximov II, Kaufmann T, Beck D, Richard G, Moberget T, Westlye LT, de Lange AG. A history of previous childbirths is linked to women's white matter brain age in midlife and older age. Hum Brain Mapp 2021; 42:4372-4386. [PMID: 34118094 PMCID: PMC8356991 DOI: 10.1002/hbm.25553] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/12/2021] [Accepted: 05/27/2021] [Indexed: 02/06/2023] Open
Abstract
Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter and white matter ageing trajectories later in life. Utilising global and regional brain age prediction based on multi-shell diffusion-weighted imaging data, we investigated the association between previous childbirths and white matter brain age in 8,895 women in the UK Biobank cohort (age range = 54-81 years). The results showed that number of previous childbirths was negatively associated with white matter brain age, potentially indicating a protective effect of parity on white matter later in life. Both global white matter and grey matter brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global white matter association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain white matter characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence women's white matter trajectories across the lifespan.
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Affiliation(s)
- Irene Voldsbekk
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Claudia Barth
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of Psychiatry and PsychotherapyUniversity of TübingenTübingenGermany
| | - Dani Beck
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- Sunnaas Rehabilitation Hospital HTOsloNorway
| | - Genevieve Richard
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Torgeir Moberget
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
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37
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Zangrossi A, Montemurro S, Altoè G, Mondini S. Heterogeneity and Factorial Structure in Alzheimer's Disease: A Cognitive Perspective. J Alzheimers Dis 2021; 83:1341-1351. [PMID: 34420975 DOI: 10.3233/jad-210719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) patients show heterogeneous cognitive profiles which suggest the existence of cognitive subgroups. A deeper comprehension of this heterogeneity could contribute to move toward a precision medicine perspective. OBJECTIVE In this study, we aimed 1) to investigate AD cognitive heterogeneity as a product of the combination of within- (factors) and between-patients (sub-phenotypes) components, and 2) to promote its assessment in clinical practice by defining a small set of critical tests for this purpose. METHODS We performed factor mixture analysis (FMA) on neurocognitive assessment results of N = 230 patients with a clinical diagnosis of AD. This technique allowed to investigate the structure of cognitive heterogeneity in this sample and to characterize the core features of cognitive sub-phenotypes. Subsequently, we performed a tests selection based on logistic regression to highlight the best tests to detect AD patients in our sample. Finally, the accuracy of the same tests in the discrimination of sub-phenotypes was evaluated. RESULTS FMA revealed a structure characterized by five latent factors and four groups, which were identifiable by means of a few cognitive tests and were mainly characterized by memory deficits with visuospatial difficulties ("Visuospatial AD"), typical AD cognitive pattern ("Typical AD"), less impaired memory ("Mild AD"), and language/praxis deficits with relatively spared memory ("Nonamnestic AD"). CONCLUSION The structure of cognitive heterogeneity in our sample of AD patients, as studied by FMA, could be summarized by four sub-phenotypes with distinct cognitive characteristics easily identifiable in clinical practice. Clinical implications under the precision medicine framework are discussed.
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Affiliation(s)
- Andrea Zangrossi
- Department of Neuroscience, University of Padua, Padua, Italy.,Padova Neuroscience Center (PNC), University of Padua, Padua, Italy
| | | | - Gianmarco Altoè
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
| | - Sara Mondini
- Department of Philosophy, Sociology, Pedagogy and Applied Psychology, University of Padua, Padua, Italy.,Human Inspired Technology Research Centre, University of Padua, Padua, Italy
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Speiser JL, Callahan KE, Houston DK, Fanning J, Gill TM, Guralnik JM, Newman AB, Pahor M, Rejeski WJ, Miller ME. Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. J Gerontol A Biol Sci Med Sci 2021; 76:647-654. [PMID: 32498077 DOI: 10.1093/gerona/glaa138] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. METHOD We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. RESULTS Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). CONCLUSIONS Machine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.
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Affiliation(s)
- Jaime Lynn Speiser
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Kathryn E Callahan
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Denise K Houston
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jason Fanning
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Jack M Guralnik
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore
| | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania
| | - Marco Pahor
- Department of Aging and Geriatric Research, University of Florida, Gainesville
| | - W Jack Rejeski
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina
| | - Michael E Miller
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
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39
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de Lange AMG, Kaufmann T, Quintana DS, Winterton A, Andreassen OA, Westlye LT, Ebmeier KP. Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study. Behav Brain Res 2021; 414:113510. [PMID: 34358570 DOI: 10.1016/j.bbr.2021.113510] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 07/09/2021] [Accepted: 07/31/2021] [Indexed: 01/12/2023]
Abstract
Loneliness is linked to increased risk for Alzheimer's disease, but little is known about factors potentially contributing to adverse brain health in lonely individuals. In this study, we used data from 24,867 UK Biobank participants to investigate risk factors related to loneliness and estimated brain age based on neuroimaging data. The results showed that on average, individuals who self-reported loneliness on a single yes/no item scored higher on neuroticism, depression, social isolation, and socioeconomic deprivation, performed less physical activity, and had higher BMI compared to individuals who did not report loneliness. In line with studies pointing to a genetic overlap of loneliness with neuroticism and depression, permutation feature importance ranked these factors as the most important for classifying lonely vs. not lonely individuals (ROC AUC = 0.83). While strongly linked to loneliness, neuroticism and depression were not associated with brain age estimates. Conversely, objective social isolation showed a main effect on brain age, and individuals reporting both loneliness and social isolation showed higher brain age relative to controls - as part of a prominent risk profile with elevated scores on socioeconomic deprivation and unhealthy lifestyle behaviours, in addition to neuroticism and depression. While longitudinal studies are required to determine causality, this finding may indicate that the combination of social isolation and a genetic predisposition for loneliness involves a risk for adverse brain health. Importantly, the results underline the complexity in associations between loneliness and adverse health outcomes, where observed risks likely depend on a combination of interlinked variables including genetic as well as social, behavioural, physical, and socioeconomic factors.
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Affiliation(s)
- Ann-Marie G de Lange
- Department of Psychiatry, University of Oxford, Oxford, UK; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Tübingen Center for Mental Health, Dept. of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Daniel S Quintana
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Adriano Winterton
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Schulz M, Malherbe C, Cheng B, Thomalla G, Schlemm E. Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature. BMC Med 2021; 19:103. [PMID: 33947394 PMCID: PMC8097883 DOI: 10.1186/s12916-021-01962-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Cerebral small vessel disease (CSVD) is a common neurological disease present in the ageing population that is associated with an increased risk of dementia and stroke. Damage to white matter tracts compromises the substrate for interneuronal connectivity. Analysing resting-state functional magnetic resonance imaging (fMRI) can reveal dysfunctional patterns of brain connectivity and contribute to explaining the pathophysiology of clinical phenotypes in CSVD. MATERIALS AND METHODS This systematic review provides an overview of methods and results of recent resting-state functional MRI studies in patients with CSVD. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, a systematic search of the literature was performed. RESULTS Of 493 studies that were screened, 44 reports were identified that investigated resting-state fMRI connectivity in the context of cerebral small vessel disease. The risk of bias and heterogeneity of results were moderate to high. Patterns associated with CSVD included disturbed connectivity within and between intrinsic brain networks, in particular the default mode, dorsal attention, frontoparietal control, and salience networks; decoupling of neuronal activity along an anterior-posterior axis; and increases in functional connectivity in the early stage of the disease. CONCLUSION The recent literature provides further evidence for a functional disconnection model of cognitive impairment in CSVD. We suggest that the salience network might play a hitherto underappreciated role in this model. Low quality of evidence and the lack of preregistered multi-centre studies remain challenges to be overcome in the future.
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Affiliation(s)
- Maximilian Schulz
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Caroline Malherbe
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Eckhard Schlemm
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
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41
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Anatürk M, Kaufmann T, Cole JH, Suri S, Griffanti L, Zsoldos E, Filippini N, Singh‐Manoux A, Kivimäki M, Westlye LT, Ebmeier KP, de Lange AG. Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging. Hum Brain Mapp 2021; 42:1626-1640. [PMID: 33314530 PMCID: PMC7978127 DOI: 10.1002/hbm.25316] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 11/27/2020] [Accepted: 12/01/2020] [Indexed: 12/17/2022] Open
Abstract
The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no "gold standard" for measuring these constructs. Using machine-learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34-82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
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Affiliation(s)
- Melis Anatürk
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
| | - Sana Suri
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Ludovica Griffanti
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Enikő Zsoldos
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Nicola Filippini
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Archana Singh‐Manoux
- Epidemiology of Ageing and Neurodegenerative diseasesUniversité de Paris, INSERM U1153ParisFrance
- Department of Epidemiology and Public HealthUniversity College LondonLondonUK
| | - Mika Kivimäki
- Department of Epidemiology and Public HealthUniversity College LondonLondonUK
| | - Lars T. Westlye
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | | | - Ann‐Marie G. de Lange
- Department of PsychiatryUniversity of OxfordOxfordUK
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
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Diersch N, Valdes-Herrera JP, Tempelmann C, Wolbers T. Increased Hippocampal Excitability and Altered Learning Dynamics Mediate Cognitive Mapping Deficits in Human Aging. J Neurosci 2021; 41:3204-3221. [PMID: 33648956 PMCID: PMC8026345 DOI: 10.1523/jneurosci.0528-20.2021] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 11/28/2022] Open
Abstract
Learning the spatial layout of a novel environment is associated with dynamic activity changes in the hippocampus and in medial parietal areas. With advancing age, the ability to learn spatial environments deteriorates substantially but the underlying neural mechanisms are not well understood. Here, we report findings from a behavioral and a fMRI experiment where healthy human older and younger adults of either sex performed a spatial learning task in a photorealistic virtual environment (VE). We modeled individual learning states using a Bayesian state-space model and found that activity in retrosplenial cortex (RSC)/parieto-occipital sulcus (POS) and anterior hippocampus did not change systematically as a function learning in older compared with younger adults across repeated episodes in the environment. Moreover, effective connectivity analyses revealed that the age-related learning deficits were linked to an increase in hippocampal excitability. Together, these results provide novel insights into how human aging affects computations in the brain's navigation system, highlighting the critical role of the hippocampus.SIGNIFICANCE STATEMENT Key structures of the brain's navigation circuit are particularly vulnerable to the deleterious consequences of aging, and declines in spatial navigation are among the earliest indicators for a progression from healthy aging to neurodegenerative diseases. Our study is among the first to provide a mechanistic account about how physiological changes in the aging brain affect the formation of spatial knowledge. We show that neural activity in the aging hippocampus and medial parietal areas is decoupled from individual learning states across repeated episodes in a novel spatial environment. Importantly, we find that increased excitability of the anterior hippocampus might constitute a potential neural mechanism for cognitive mapping deficits in old age.
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Affiliation(s)
- Nadine Diersch
- Aging and Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - Jose P Valdes-Herrera
- Aging and Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - Claus Tempelmann
- Department of Neurology, Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
| | - Thomas Wolbers
- Aging and Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
- Department of Neurology, Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- Center for Behavioural Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
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Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacology 2021; 46:911-919. [PMID: 33495545 PMCID: PMC8115107 DOI: 10.1038/s41386-021-00967-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 01/08/2021] [Indexed: 01/30/2023]
Abstract
Multiple structural and functional neuroimaging measures vary over the course of the lifespan and can be used to predict chronological age. Accelerated brain aging, as quantified by deviations in the MRI-based predicted age with respect to chronological age, is associated with risk for neurodegenerative conditions, bipolar disorder, and mortality. Whether age-related changes in resting-state functional connectivity are accelerated in major depressive disorder (MDD) is unknown, and, if so, it is unclear if these changes contribute to specific cognitive weaknesses that often occur in MDD. Here, we delineated age-related functional connectivity changes in a large sample of normal control subjects and tested whether brain aging is accelerated in MDD. Furthermore, we tested whether accelerated brain aging predicts individual differences in cognitive function. We trained a support vector regression model predicting age using resting-state functional connectivity in 710 healthy adults aged 18-89. We applied this model trained on normal aging subjects to a sample of actively depressed MDD participants (n = 109). The difference between predicted brain age and chronological age was 2.11 years greater (p = 0.015) in MDD patients compared to control participants. An older MDD brain age was significantly associated with increased impulsivity and, in males, increased depressive severity. Unexpectedly, accelerated brain aging was also associated with increased placebo response in a sham-controlled trial of high-frequency repetitive transcranial magnetic stimulation targeting the dorsomedial prefrontal cortex. Our results indicate that MDD is associated with accelerated brain aging, and that accelerated aging is selectively associated with greater impulsivity and depression severity.
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Dunås T, Wåhlin A, Nyberg L, Boraxbekk CJ. Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance. Cereb Cortex 2021; 31:3393-3407. [PMID: 33690853 PMCID: PMC8196254 DOI: 10.1093/cercor/bhab019] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/20/2021] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years of education. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regression method did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, current cognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years prior to imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physical fitness throughout life contributes to brain maintenance and preserved cognitive ability.
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Affiliation(s)
- Tora Dunås
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Centre for Demographic and Ageing Research (CEDAR), Umeå University, S-901 87 Umeå, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Umeå University, S-901 87 Umeå, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Umeå University, S-901 87 Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, S-901 87 Umeå, Sweden
| | - Carl-Johan Boraxbekk
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Umeå University, S-901 87 Umeå, Sweden.,Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark.,Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital Bispebjerg, DK-2400 Copenhagen, Denmark
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45
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Zhang B, Lin L, Wu S, Al-Masqari ZHMA. Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern. Brain Sci 2021; 11:brainsci11020278. [PMID: 33672406 PMCID: PMC7926857 DOI: 10.3390/brainsci11020278] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/19/2021] [Accepted: 02/20/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer’s disease (AD) is a disease of a heterogeneous nature, which can be disentangled by exploring the characteristics of each AD subtype in the brain structure, neuropathology, and cognition. In this study, a total of 192 AD and 228 cognitively normal (CN) subjects were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Based on the cortical thickness patterns, the mixture of experts method (MOE) was applied to the implicit model spectrum of transforms lined with each AD subtype, then their neuropsychological and neuropathological characteristics were analyzed. Furthermore, the piecewise linear classifiers composed of each AD subtype and CN were resolved, and each subtype was comprehensively explained. The following four distinct AD subtypes were discovered: bilateral parietal, frontal, and temporal atrophy AD subtype (occipital sparing AD subtype (OSAD), 29.2%), left temporal dominant atrophy AD subtype (LTAD, 22.4%), minimal atrophy AD subtype (MAD, 16.1%), and diffuse atrophy AD subtype (DAD, 32.3%). These four subtypes display their own characteristics in atrophy pattern, cognition, and neuropathology. Compared with the previous studies, our study found that some AD subjects showed obvious asymmetrical atrophy in left lateral temporal-parietal cortex, OSAD presented the worst cerebrospinal fluid levels, and MAD had the highest proportions of APOE ε4 and APOE ε2. The subtype characteristics were further revealed from the aspect of the model, making it easier for clinicians to understand. The results offer an effective support for individual diagnosis and prognosis.
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Affiliation(s)
- Baiwen Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
- Correspondence: ; Tel.: +86-10-6739-1610
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Zakarea H. M. A. Al-Masqari
- Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China; (B.Z.); (S.W.); (Z.H.M.A.A.-M.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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46
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Bittner N, Jockwitz C, Franke K, Gaser C, Moebus S, Bayen UJ, Amunts K, Caspers S. When your brain looks older than expected: combined lifestyle risk and BrainAGE. Brain Struct Funct 2021; 226:621-645. [PMID: 33423086 PMCID: PMC7981332 DOI: 10.1007/s00429-020-02184-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 11/24/2020] [Indexed: 12/25/2022]
Abstract
Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.
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Affiliation(s)
- Nora Bittner
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstr. 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany
| | - Christiane Jockwitz
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstr. 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany
| | - Katja Franke
- Structural Brain Mapping Group, University Hospital Jena, 07743, Jena, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, University Hospital Jena, 07743, Jena, Germany
| | - Susanne Moebus
- Institute of Urban Public Health, University of Duisburg-Essen, 45122, Essen, Germany
| | - Ute J Bayen
- Mathematical and Cognitive Psychology, Institute for Experimental Psychology, Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany.,Cecile and Oskar Vogt Institute for Brain Research, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany.,JARA-BRAIN, Juelich-Aachen Research Alliance, 52425, Jülich, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstr. 1, 40225, Düsseldorf, Germany. .,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany. .,JARA-BRAIN, Juelich-Aachen Research Alliance, 52425, Jülich, Germany.
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47
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Yu GZ, Ly M, Karim HT, Muppidi N, Aizenstein HJ, Ibinson JW. Accelerated brain aging in chronic low back pain. Brain Res 2021; 1755:147263. [PMID: 33422525 DOI: 10.1016/j.brainres.2020.147263] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/20/2020] [Accepted: 12/20/2020] [Indexed: 12/15/2022]
Abstract
Chronic low back pain (CLBP) is a leading cause of disability and is associated with neurodegenerative changes in brain structure. These changes lead to impairments in cognitive function and are consistent with those seen in aging, suggesting an accelerated aging pattern. In this study we assessed this using machine-learning estimated brain age (BA) as a holistic metric of morphometric changes associated with aging. Structural imaging data from 31 non-depressed CLBP patients and 32 healthy controls from the Pain and Interoception Imaging Network were included. Using our previously developed algorithm, we estimated BA per individual based on grey matter density. We then conducted multivariable linear modeling for effects of group, chronological age, and their interaction on BA. We also performed two voxel-wise analyses comparing grey matter density between CLBP and control individuals and the association between gray matter density and BA. There was an interaction between CLBP and greater chronological age on BA such that the discrepancy in BA between healthy and CLBP individuals was greater for older individuals. In CLBP individuals, BA was not associated with sex, current level of pain, duration of CLBP, or mild to moderate depressive symptoms. CLBP individuals had lower cerebellar grey matter density compared to healthy individuals. Brain age was associated with lower gray matter density in numerous brain regions. CLBP was associated with greater BA, which was more profound in later life. BA as a holistic metric was sensitive to differences in gray matter density in numerous regions which eluded direct comparison between groups.
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Affiliation(s)
- Gary Z Yu
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maria Ly
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nishita Muppidi
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Howard J Aizenstein
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - James W Ibinson
- Department of Anesthesiology, University of Pittsburgh, Pittsburgh, PA, USA.
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48
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Batouli SAH, Sisakhti M, Haghshenas S, Dehghani H, Sachdev P, Ekhtiari H, Kochan N, Wen W, Leemans A, Kohanpour M, Oghabian MA. Iranian Brain Imaging Database: A Neuropsychiatric Database of Healthy Brain. Basic Clin Neurosci 2021; 12:115-132. [PMID: 33995934 PMCID: PMC8114860 DOI: 10.32598/bcn.12.1.1774.2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 06/19/2019] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases with similar goals are available. However, they were not done on an Iranian population, and the battery of their tests (e.g. cognitive tests) is selected based on their specific questions and needs. METHODS The IBID will include 300 participants (50% female) in the age range of 20 to 70 years old, with an equal number of participants (#60) in each age decade. It comprises a battery of cognitive, lifestyle, medical, and mental health tests, in addition to several Magnetic Resonance Imaging (MRI) protocols. Each participant completes the assessments on two referral days. RESULTS The study currently has a cross-sectional design, but longitudinal assessments are considered for the future phases of the study. Here, details of the methodology and the initial results of assessing the first 152 participants of the study are provided. CONCLUSION IBID is established to enable research into human brain function, to aid clinicians in disease diagnosis research, and also to unite the Iranian researchers with interests in the brain.
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Affiliation(s)
- Seyed Amir Hossein Batouli
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Minoo Sisakhti
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Shirin Haghshenas
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Dehghani
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | | | - Nicole Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mohsen Kohanpour
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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49
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de Lange AMG, Anatürk M, Suri S, Kaufmann T, Cole JH, Griffanti L, Zsoldos E, Jensen DEA, Filippini N, Singh-Manoux A, Kivimäki M, Westlye LT, Ebmeier KP. Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. Neuroimage 2020; 222:117292. [PMID: 32835819 PMCID: PMC8121758 DOI: 10.1016/j.neuroimage.2020.117292] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022] Open
Abstract
Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.
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Affiliation(s)
- Ann-Marie G de Lange
- Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Melis Anatürk
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ludovica Griffanti
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Enikő Zsoldos
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Daria E A Jensen
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Nicola Filippini
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Archana Singh-Manoux
- Epidemiology of Ageing and Neurodegenerative Diseases, Universit de Paris, INSERM U1153, Paris France; Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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50
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Ferré P, Jarret J, Brambati S, Bellec P, Joanette Y. Functional Connectivity of Successful Picture-Naming: Age-Specific Organization and the Effect of Engaging in Stimulating Activities. Front Aging Neurosci 2020; 12:535770. [PMID: 33250759 PMCID: PMC7674930 DOI: 10.3389/fnagi.2020.535770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 10/05/2020] [Indexed: 11/13/2022] Open
Abstract
Aging is a lifelong process that starts at birth. Throughout the course of their life, individuals are exposed to various levels of stimulating activities. A higher level of engagement in such activities is suspected to protect against the normal course of cognitive aging or the cognitive manifestations of age-related brain diseases. However, the exact mechanism underlying such protective action remains unclear. The concept of the neurocognitive reserve was introduced to refer to the hypothesis that engagement in stimulating activities shapes brain structure and function, thus indirectly allowing for better preserved cognitive abilities. Although it is known that word production is among the best-preserved cognitive abilities in aging, the underlying neurofunctional mechanisms that allow this relative preservation are still unknown, and it is still unclear how engagement in stimulating activities affects these processes. The objective of this study is to describe the brain functional connectivity patterns associated with picture-naming abilities in younger and older adults with varying levels of engagement in stimulating activities, as a proxy for neurocognitive reserve. A mediation analysis was applied to determine whether the association between reserve proxies and naming accuracy is dependent on task FC. Results show that naming accuracy depends on the posterior cingulate cortex (PCC) functional decoupling in both younger and older adults but through different pathways. While high-performing older adults rely on the asynchronization of this area from motor speech regions’ activity, the best-performing younger adults rely on the functional decoupling with language-related regions. Mediation analysis reveals that the PCC decoupling mediates the relationship between the level of engagement in stimulating activities and naming accuracy in younger adults, but not in older adults. These findings suggest that reserve-related mechanisms may be more critical for naming in early adult life, while older adults’ neurofunctional organization may benefit more from a lifetime of acquired knowledge.
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Affiliation(s)
- Perrine Ferré
- Centre de Recherche de l’institut de Gériatrie de l’Université de Montréal (CRIUGM), Montréal, QC, Canada
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada
- *Correspondence: Perrine Ferré
| | - Julien Jarret
- Centre de Recherche de l’institut de Gériatrie de l’Université de Montréal (CRIUGM), Montréal, QC, Canada
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada
| | - Simona Brambati
- Centre de Recherche de l’institut de Gériatrie de l’Université de Montréal (CRIUGM), Montréal, QC, Canada
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada
| | - Pierre Bellec
- Centre de Recherche de l’institut de Gériatrie de l’Université de Montréal (CRIUGM), Montréal, QC, Canada
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada
| | - Yves Joanette
- Centre de Recherche de l’institut de Gériatrie de l’Université de Montréal (CRIUGM), Montréal, QC, Canada
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada
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