publication . Article . 2020

Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging

Neth, Bryan J.; Graff-Radford, Jonathan; Mielke, Michelle M.; Przybelski, Scott A.; Lesnick, Timothy G.; Schwarz, Christopher G.; Reid, Robert I.; Senjem, Matthew L.; Lowe, Val J.; Machulda, Mary M.; ...
Open Access
  • Published: 09 Jan 2020 Journal: Frontiers in Aging Neuroscience, volume 11 (eissn: 1663-4365, Copyright policy)
  • Publisher: Frontiers Media SA
Abstract
Background Brain reserve can be defined as the individual variation in the brain structural characteristics that later in life are likely to modulate cognitive performance. Late midlife represents a point in aging where some structural brain imaging changes have become manifest but the effects of cognitive aging are minimal, and thus may represent an ideal opportunity to determine the relationship between risk factors and brain imaging biomarkers of reserve. Objective We aimed to assess neuroimaging measures from multiple modalities to broaden our understanding of brain reserve, and the late midlife risk factors that may make the brain vulnerable to age related ...
Subjects
free text keywords: Ageing, Cognitive Neuroscience, Cognitive reserve, Population, education.field_of_study, education, Fractional anisotropy, Developmental psychology, Psychology, Cognition, Posterior cingulate, Effects of sleep deprivation on cognitive performance, Corpus callosum, Clinical psychology, Neuroimaging, Neuroscience, Original Research, brain reserve, cognitive aging, multimodal imaging, resilience, dynamic
Funded by
NIH| ALZHEIMERS DISEASE PATIENT REGISTRY
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2U01AG006786-04
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| EXTRAMURAL RESEARCH FACILITIES IMPROVEMENT PROGRAM
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1C06RR018898-01
  • Funding stream: NATIONAL CENTER FOR RESEARCH RESOURCES
,
NIH| Development, Validation, and Application of an Imaging based CVD Scale
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01NS097495-02
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
,
NIH| Multimorbidity and Aging: Rochester Epidemiology Project
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG034676-52
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Exceptional Aging: Identifying Modifiers of Alzheimer's Disease Trajectories
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG056366-02
  • Funding stream: NATIONAL INSTITUTE ON AGING
Communities
Neuroinformatics
61 references, page 1 of 5

Arenaza-Urquijo E. M.Landeau B.La Joie R.Mevel K.Mezenge F.Perrotin A. (2013). Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. Neuroimage 83 450–457. 10.1016/j.neuroimage.2013.06.053 23796547 [OpenAIRE] [PubMed] [DOI]

Arenaza-Urquijo E. M.Vemuri P. (2018). Resistance vs resilience to Alzheimer disease: clarifying terminology for preclinical studies. Neurology 90 695–703. 10.1212/WNL.0000000000005303 29592885 [OpenAIRE] [PubMed] [DOI]

Beck A. T.Steer R. A.Brown G. K. (1996). Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation 490–498.

Bozzali M.Falini A.Franceschi M.Cercignani M.Zuffi M.Scotti G. (2002). White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J. Neurol. Neurosurg. Psychia try 72 742–746.12023417 [OpenAIRE] [PubMed]

Byers A. L.Yaffe K. (2011). Depression and risk of developing dementia. Nat. Rev. Neurol. 7 323–331. 10.1038/nrneurol.2011.60 21537355 [OpenAIRE] [PubMed] [DOI]

Calle E. E.Thun M. J.Petrelli J. M.Rodriguez C.Heath C. W.Jr. (1999). Body-mass index and mortality in a prospective cohort of US adults. N. England J. Med. 341 1097–1105. 10511607 [OpenAIRE] [PubMed]

Chêne G.Beiser A.Au R.Preis S. R.Wolf P. A.Dufouil C. (2015). Gender and incidence of dementia in the framingham heart study from mid-adult life. Alzheimers Dement. 11 310–320. 10.1016/j.jalz.2013.10.005 24418058 [OpenAIRE] [PubMed] [DOI]

Chua T. C.Wen W.Slavin M. J.Sachdev P. S. (2008). Diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease: a review. Curr. Opin. Neurol. 21 83–92. 10.1097/WCO.0b013e3282f4594b 18180656 [PubMed] [DOI]

Clare L.Wu Y.-T.Teale J. C.Macleod C.Matthews F.Brayne C. (2017). Potentially modifiable lifestyle factors, cognitive reserve, and cognitive function in later life: a cross-sectional study. PLoS Med. 14:e1002259. 10.1371/journal.pmed.1002259 28323829 [OpenAIRE] [PubMed] [DOI]

Coresh J.Selvin E.Stevens L. A.Manzi J.Kusek J. W.Eggers P. (2007). Prevalence of chronic kidney disease in the United States. JAMA 298 2038–2047. 17986697 [OpenAIRE] [PubMed]

Craft S. (2009). The role of metabolic disorders in Alzheimer Disease and vascular dementia. Arch. Neurol. 66 300–305. 10.1001/archneurol.2009.27 19273747 [OpenAIRE] [PubMed] [DOI]

Crooks V. C.Lubben J.Petitti D. B.Little D.Chiu V. (2008). Social network, cognitive function, and dementia incidence among elderly women. Am. J. Public Health 98 1221–1227. 10.2105/AJPH.2007.115923 18511731 [OpenAIRE] [PubMed] [DOI]

Cunnane S.Nugent S.Roy M.Courchesne-Loyer A.Croteau E.Tremblay S. (2011). Brain fuel metabolism, aging, and Alzheimer’s disease. Nutrition 27 3–20.21035308 [OpenAIRE] [PubMed]

Debette S.Seshadri S.Beiser A.Au R.Himali J.Palumbo C. (2011). Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77 461–468. 10.1212/WNL.0b013e318227b227 21810696 [OpenAIRE] [PubMed] [DOI]

Ewers M.Insel P. S.Stern Y.Weiner M. W.Alzheimer’s Disease Neuroimaging Intitative. (2013). Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease. Neurology 80 1194–1201. 10.1212/WNL.0b013e31828970c2 23486873 [OpenAIRE] [PubMed] [DOI]

61 references, page 1 of 5
Abstract
Background Brain reserve can be defined as the individual variation in the brain structural characteristics that later in life are likely to modulate cognitive performance. Late midlife represents a point in aging where some structural brain imaging changes have become manifest but the effects of cognitive aging are minimal, and thus may represent an ideal opportunity to determine the relationship between risk factors and brain imaging biomarkers of reserve. Objective We aimed to assess neuroimaging measures from multiple modalities to broaden our understanding of brain reserve, and the late midlife risk factors that may make the brain vulnerable to age related ...
Subjects
free text keywords: Ageing, Cognitive Neuroscience, Cognitive reserve, Population, education.field_of_study, education, Fractional anisotropy, Developmental psychology, Psychology, Cognition, Posterior cingulate, Effects of sleep deprivation on cognitive performance, Corpus callosum, Clinical psychology, Neuroimaging, Neuroscience, Original Research, brain reserve, cognitive aging, multimodal imaging, resilience, dynamic
Funded by
NIH| ALZHEIMERS DISEASE PATIENT REGISTRY
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2U01AG006786-04
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| EXTRAMURAL RESEARCH FACILITIES IMPROVEMENT PROGRAM
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1C06RR018898-01
  • Funding stream: NATIONAL CENTER FOR RESEARCH RESOURCES
,
NIH| Development, Validation, and Application of an Imaging based CVD Scale
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01NS097495-02
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
,
NIH| Multimorbidity and Aging: Rochester Epidemiology Project
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG034676-52
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Exceptional Aging: Identifying Modifiers of Alzheimer's Disease Trajectories
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG056366-02
  • Funding stream: NATIONAL INSTITUTE ON AGING
Communities
Neuroinformatics
61 references, page 1 of 5

Arenaza-Urquijo E. M.Landeau B.La Joie R.Mevel K.Mezenge F.Perrotin A. (2013). Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. Neuroimage 83 450–457. 10.1016/j.neuroimage.2013.06.053 23796547 [OpenAIRE] [PubMed] [DOI]

Arenaza-Urquijo E. M.Vemuri P. (2018). Resistance vs resilience to Alzheimer disease: clarifying terminology for preclinical studies. Neurology 90 695–703. 10.1212/WNL.0000000000005303 29592885 [OpenAIRE] [PubMed] [DOI]

Beck A. T.Steer R. A.Brown G. K. (1996). Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation 490–498.

Bozzali M.Falini A.Franceschi M.Cercignani M.Zuffi M.Scotti G. (2002). White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J. Neurol. Neurosurg. Psychia try 72 742–746.12023417 [OpenAIRE] [PubMed]

Byers A. L.Yaffe K. (2011). Depression and risk of developing dementia. Nat. Rev. Neurol. 7 323–331. 10.1038/nrneurol.2011.60 21537355 [OpenAIRE] [PubMed] [DOI]

Calle E. E.Thun M. J.Petrelli J. M.Rodriguez C.Heath C. W.Jr. (1999). Body-mass index and mortality in a prospective cohort of US adults. N. England J. Med. 341 1097–1105. 10511607 [OpenAIRE] [PubMed]

Chêne G.Beiser A.Au R.Preis S. R.Wolf P. A.Dufouil C. (2015). Gender and incidence of dementia in the framingham heart study from mid-adult life. Alzheimers Dement. 11 310–320. 10.1016/j.jalz.2013.10.005 24418058 [OpenAIRE] [PubMed] [DOI]

Chua T. C.Wen W.Slavin M. J.Sachdev P. S. (2008). Diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease: a review. Curr. Opin. Neurol. 21 83–92. 10.1097/WCO.0b013e3282f4594b 18180656 [PubMed] [DOI]

Clare L.Wu Y.-T.Teale J. C.Macleod C.Matthews F.Brayne C. (2017). Potentially modifiable lifestyle factors, cognitive reserve, and cognitive function in later life: a cross-sectional study. PLoS Med. 14:e1002259. 10.1371/journal.pmed.1002259 28323829 [OpenAIRE] [PubMed] [DOI]

Coresh J.Selvin E.Stevens L. A.Manzi J.Kusek J. W.Eggers P. (2007). Prevalence of chronic kidney disease in the United States. JAMA 298 2038–2047. 17986697 [OpenAIRE] [PubMed]

Craft S. (2009). The role of metabolic disorders in Alzheimer Disease and vascular dementia. Arch. Neurol. 66 300–305. 10.1001/archneurol.2009.27 19273747 [OpenAIRE] [PubMed] [DOI]

Crooks V. C.Lubben J.Petitti D. B.Little D.Chiu V. (2008). Social network, cognitive function, and dementia incidence among elderly women. Am. J. Public Health 98 1221–1227. 10.2105/AJPH.2007.115923 18511731 [OpenAIRE] [PubMed] [DOI]

Cunnane S.Nugent S.Roy M.Courchesne-Loyer A.Croteau E.Tremblay S. (2011). Brain fuel metabolism, aging, and Alzheimer’s disease. Nutrition 27 3–20.21035308 [OpenAIRE] [PubMed]

Debette S.Seshadri S.Beiser A.Au R.Himali J.Palumbo C. (2011). Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77 461–468. 10.1212/WNL.0b013e318227b227 21810696 [OpenAIRE] [PubMed] [DOI]

Ewers M.Insel P. S.Stern Y.Weiner M. W.Alzheimer’s Disease Neuroimaging Intitative. (2013). Cognitive reserve associated with FDG-PET in preclinical Alzheimer disease. Neurology 80 1194–1201. 10.1212/WNL.0b013e31828970c2 23486873 [OpenAIRE] [PubMed] [DOI]

61 references, page 1 of 5
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publication . Article . 2020

Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging

Neth, Bryan J.; Graff-Radford, Jonathan; Mielke, Michelle M.; Przybelski, Scott A.; Lesnick, Timothy G.; Schwarz, Christopher G.; Reid, Robert I.; Senjem, Matthew L.; Lowe, Val J.; Machulda, Mary M.; ...