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Connecting memory and functional brain networks in older adults: a resting-state fMRI study

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Abstract

Limited research exists on the association between resting-state functional network connectivity in the brain and learning and memory processes in advanced age. This study examined within-network connectivity of cingulo-opercular (CON), frontoparietal control (FPCN), and default mode (DMN) networks, and verbal and visuospatial learning and memory in older adults. Across domains, we hypothesized that greater CON and FPCN connectivity would associate with better learning, and greater DMN connectivity would associate with better memory. A total of 330 healthy older adults (age range = 65–89) underwent resting-state fMRI and completed the Hopkins Verbal Learning Test–Revised (HVLT-R) and Brief Visuospatial Memory Test–Revised (BVMT-R) in a randomized clinical trial. Total and delayed recall scores were assessed from baseline data, and a learning ratio calculation was applied to participants’ scores. Average CON, FPCN, and DMN connectivity values were obtained with CONN Toolbox. Hierarchical regressions controlled for sex, race, ethnicity, years of education, and scanner site, as this was a multi-site study. Greater within-network CON connectivity was associated with better verbal learning (HVLT-R Total Recall, Learning Ratio), visuospatial learning (BVMT-R Total Recall), and visuospatial memory (BVMT-R Delayed Recall). Greater FPCN connectivity was associated with better visuospatial learning (BVMT-R Learning Ratio) but did not survive multiple comparison correction. DMN connectivity was not associated with these measures of learning and memory. CON may make small but unique contributions to learning and memory across domains, making it a valuable target in future longitudinal studies and interventions to attenuate memory decline. Further research is necessary to understand the role of FPCN in learning and memory.

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Data availability

Data are managed under the data sharing agreement established with NIA and the parent R01 clinical trial Data Safety and Monitoring Board (DSMB) in the context of a phase III clinical trial (ACT study, R01AG054077). All trial data will be made publicly available 2 years after completion of the parent clinical trial, per NIA and DSMB agreement. Requests for baseline data can be submitted to the ACT Publication and Presentation (P&P) Committee and will require submission of a data use, authorship, and analytic plan for review by the P&P committee (ajwoods@phhp.ufl.edu).

References

  1. United Nations Department of Economic and Social Affairs, Population Division. World population prospects 2022: summary of results. [online] 2022. Available at: https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf. Accessed 9/1/23.

  2. Farias ST, Harrell E, Neumann C, Houtz A. The relationship between neuropsychological performance and daily functioning in individuals with Alzheimer’s disease: ecological validity of neuropsychological tests. Arch Clin Neuropsychol. 2003;18:655–72.

    Article  PubMed  Google Scholar 

  3. Hall JR, Vo HT, Johnson LA, Barber RC, O’Bryant SE. The link between cognitive measures and ADLs and IADL functioning in mild Alzheimer’s: what has gender got to do with it? Int J Alzheimers Dis. 2011;2011:276734.

    PubMed  PubMed Central  Google Scholar 

  4. Hedden T, Gabrieli JDE. Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci. 2004;5:87–96.

    Article  CAS  PubMed  Google Scholar 

  5. Woods SP, Weinborn M, Velnoweth A, Rooney A, Bucks RS. Memory for intentions is uniquely associated with instrumental activities of daily living in healthy older adults. J Int Neuropsychol Soc. 2012;18:134–8.

    Article  PubMed  Google Scholar 

  6. Bopp KL, Verhaeghen P. Age-related differences in control processes in verbal and visuospatial working memory: storage, transformation, supervision, and coordination. J Gerontol B Psychol Sci Soc Sci. 2007;62:P239–46.

    Article  PubMed  Google Scholar 

  7. Janssen SMJ, Kristo G, Rouw R, Murre JMJ. The relation between verbal and visuospatial memory and autobiographical memory. Conscious Cogn. 2015;31:12–23.

    Article  PubMed  Google Scholar 

  8. Murre JMJ, Janssen SMJ, Rouw R, Meeter M. The rise and fall of immediate and delayed memory for verbal and visuospatial information from late childhood to late adulthood. Acta Psychol. 2013;142:96–107.

    Article  Google Scholar 

  9. Park DC, Lautenschlager G, Hedden T, Davidson NS, Smith AD, Smith PK. Models of visuospatial and verbal memory across the adult life span. Psychol Aging. 2002;17:299.

    Article  PubMed  Google Scholar 

  10. Reichert JL, Kober SE, Witte M, Neuper C, Wood G. Age-related effects on verbal and visuospatial memory are mediated by theta and alpha II rhythms. Int J Psychophysiol. 2016;99:67–78.

    Article  PubMed  Google Scholar 

  11. Cronin-Golomb A. Visuospatial function in Alzheimer’s disease and related disorders. In: Budson AE, Kowall NW, editors. The Handbook of Alzheimer’s Disease and Other Dementias. 1st ed. Chichester, West Sussex, UK: Wiley-Blackwell; 2011. p. 457–82.

    Chapter  Google Scholar 

  12. Hamilton JM, Salmon DP, Galasko D, Raman R, Emond J, Hansen LA, et al. Visuospatial deficits predict rate of cognitive decline in autopsy-verified dementia with Lewy bodies. Neuropsychology. 2008;22:729–37.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lange KL, Bondi MW, Salmon DP, Galasko D, Delis DC, Thomas RG, et al. Decline in verbal memory during preclinical Alzheimer’s disease: examination of the effect of APOE genotype. J Int Neuropsychol Soc. 2002;8:943–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Davis D, Bendayan R, Muniz Terrera G, Hardy R, Richards M, Kuh D. Decline in search speed and verbal memory over 26 years of midlife in a British birth cohort. NED. 2017;49:121–8.

    Google Scholar 

  15. Muffato V, Miola L, Pazzaglia F, Meneghetti C. Map learning in aging individuals: the role of cognitive functioning and visuospatial factors. Brain Sci. 2021;11:1033.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wearn AR, Saunders-Jennings E, Nurdal V, Hadley E, Knight MJ, Newson M, et al. Accelerated long-term forgetting in healthy older adults predicts cognitive decline over 1 year. Alzheimer’s Res Ther. 2020;12:119.

    Article  Google Scholar 

  17. Casaletto KB, Marx G, Dutt S, Neuhaus J, Saloner R, Kritikos L, et al. Is “learning” episodic memory? Distinct cognitive and neuroanatomic correlates of immediate recall during learning trials in neurologically normal aging and neurodegenerative cohorts. Neuropsychologia. 2017;102:19–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wallace A, Bucks RS. Memory and obstructive sleep apnea: a meta-analysis. SLEEP. 2013;36:203–20.

    PubMed  PubMed Central  Google Scholar 

  19. Díaz-Santos M, Suárez PA, Marquine MJ, Umlauf A, Rivera Mindt M, Artiola i Fortuny L, et al. Updated demographically adjusted norms for the Brief Visuospatial Memory Test-Revised and Hopkins Verbal Learning Test-Revised in Spanish-speakers from the U.S.-Mexico border region: The NP-NUMBRS project. Clin Neuropsychol. 2021;35:374–95.

    Article  PubMed  Google Scholar 

  20. Duff K. Demographically corrected normative data for the Hopkins Verbal Learning Test-Revised and Brief Visuospatial Memory Test-Revised in an elderly sample. Appl Neuropsychol. 2016;23:179–85.

    Google Scholar 

  21. Ryan J, Woods RL, Murray AM, Shah RC, Britt CJ, Reid CM, et al. Normative performance of older individuals on the Hopkins Verbal Learning Test-Revised (HVLT-R) according to ethno-racial group, gender, age and education level. Clin Neuropsychol. 2021;35:1174–90.

    Article  PubMed  Google Scholar 

  22. Vanderploeg RD, Schinka JA, Jones T, Small BJ, Borenstein Graves A, Mortimer JA. Elderly norms for the Hopkins Verbal Learning Test-Revised. Clin Neuropsychol. 2000;14:318–24.

    Article  CAS  PubMed  Google Scholar 

  23. Vicente SG, Ramos-Usuga D, Barbosa F, Gaspar N, Dores AR, Rivera D, et al. Regression-based norms for the Hopkins Verbal Learning Test-Revised and the Rey–Osterrieth Complex Figure in a Portuguese adult population. Arch Clin Neuropsychol. 2021;36:587–96.

    Article  PubMed  Google Scholar 

  24. Cherner M, Suarez P, Lazzaretto D, Fortuny L, Mindt M, Dawes S, et al. Demographically corrected norms for the Brief Visuospatial Memory Test-Revised and Hopkins Verbal Learning Test-Revised in monolingual Spanish speakers from the U.S.–Mexico border region. Arch Clin Neuropsychol. 2007;22:343–53.

    Article  CAS  PubMed  Google Scholar 

  25. Gale SD, Baxter L, Connor DJ, Herring A, Comer J. Sex differences on the Rey Auditory Verbal Learning Test and the Brief Visuospatial Memory Test–Revised in the elderly: normative data in 172 participants. J Clin Exp Neuropsychol. 2007;29:561–7.

    Article  PubMed  Google Scholar 

  26. Kane KD, Yochim BP. Construct validity and extended normative data for older adults for the Brief Visuospatial Memory Test, Revised. Am J Alzheimers Dis Other Demen. 2014;29:601–6.

    Article  PubMed  Google Scholar 

  27. Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37:384–400.

    Article  PubMed  Google Scholar 

  28. Shaw EE, Schultz AP, Sperling RA, Hedden T. Functional connectivity in multiple cortical networks is associated with performance across cognitive domains in older adults. Brain Connect. 2015;5:505–16.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Spreng RN, Sepulcre J, Turner GR, Stevens WD, Schacter DL. Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. J Cogn Neurosci. 2013;25:74–86.

    Article  PubMed  Google Scholar 

  30. Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:1125–65.

    Article  PubMed  Google Scholar 

  31. Damoiseaux JS, Beckmann CF, Arigita EJS, Barkhof F, Scheltens P, Stam CJ, et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex. 2008;18:1856–64.

    Article  CAS  PubMed  Google Scholar 

  32. Damoiseaux JS. Effects of aging on functional and structural brain connectivity. NeuroImage. 2017;160:32–40.

    Article  PubMed  Google Scholar 

  33. Andrews-Hanna JR, Snyder AZ, Vincent JL, Lustig C, Head D, Raichle ME, et al. Disruption of large-scale brain systems in advanced aging. Neuron. 2007;56:924–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Betzel RF, Byrge L, He Y, Goñi J, Zuo X-N, Sporns O. Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage. 2014;102:345–57.

    Article  PubMed  Google Scholar 

  35. Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci USA [Internet]. 2014;111 https://doi.org/10.1073/pnas.1415122111.

  36. He J, Carmichael O, Fletcher E, Singh B, Iosif A-M, Martinez O, et al. Influence of functional connectivity and structural MRI measures on episodic memory. Neurobiol Aging. 2012;33:2612–20.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Dixon ML, De La Vega A, Mills C, Andrews-Hanna J, Spreng RN, Cole MW, et al. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci USA [Internet]. 2018;115 https://doi.org/10.1073/pnas.1715766115.

  38. Dosenbach NUF, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RAT, et al. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci USA. 2007;104:11073–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A dual-networks architecture of top-down control. TiCS. 2008;12:99–105.

    Google Scholar 

  40. Han SW, Eaton HP, Marois R. Functional fractionation of the cingulo-opercular network: alerting insula and updating cingulate. Cereb Cortex. 2019;29:2624–38.

    Article  PubMed  Google Scholar 

  41. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214:655–67.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sestieri C, Corbetta M, Spadone S, Romani GL, Shulman GL. Domain-general signals in the cingulo-opercular network for visuospatial attention and episodic memory. J Cogn Neurosci. 2014;26:551–68.

    Article  PubMed  Google Scholar 

  44. Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol. 2008;100:3328–42.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hardcastle C, Hausman HK, Kraft JN, Albizu A, Evangelista ND, Boutzoukas EM, et al. Higher-order resting state network association with the useful field of view task in older adults. GeroScience. 2022;44:131–45.

    Article  PubMed  Google Scholar 

  46. Huo L, Li R, Wang P, Zheng Z, Li J. The default mode network supports episodic memory in cognitively unimpaired elderly individuals: different contributions to immediate recall and delayed recall. Front Aging Neurosci. 2018;10:6.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Grady C, Sarraf S, Saverino C, Campbell K. Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiol Aging. 2016;41:159–72.

    Article  PubMed  Google Scholar 

  48. Hausman HK, O’Shea A, Kraft JN, Boutzoukas EM, Evangelista ND, Van Etten EJ, et al. The role of resting-state network functional connectivity in cognitive aging. Front Aging Neurosci. 2020;12:177.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Hausman HK, Hardcastle C, Albizu A, Kraft JN, Evangelista ND, Boutzoukas EM, et al. Cingulo-opercular and frontoparietal control network connectivity and executive functioning in older adults. GeroScience. 2022;44:847–66.

    Article  PubMed  Google Scholar 

  50. Siman-Tov T, Bosak N, Sprecher E, Paz R, Eran A, Aharon-Peretz J, et al. Early age-related functional connectivity decline in high-order cognitive networks. Front Aging Neurosci [Internet]. 2017;8 https://doi.org/10.3389/fnagi.2016.00330/full.

  51. Woods AJ, Cohen R, Marsiske M, Alexander GE, Czaja SJ, Wu S. Augmenting cognitive training in older adults (the ACT study): design and methods of a phase III tDCS and cognitive training trial. Contemp Clin Trials. 2018;65:19–32.

    Article  PubMed  Google Scholar 

  52. Means and standard deviations for the UDS3 neuropsychological battery in cognitively normal participants [Internet]. NACC; 2020. Available at: https://files.alz.washington.edu/documentation/uds3-means.pdf. Accessed 9/1/2023.

  53. Benedict RHB, Schretlen D, Groninger L, Brandt J. Hopkins Verbal Learning Test – Revised: normative data and analysis of inter-form and test-retest reliability. Clin Neuropsychol. 1998;12:43–55.

    Article  Google Scholar 

  54. Shi J, Tian J, Wei M, Miao Y, Wang Y. The utility of the Hopkins Verbal Learning Test (Chinese version) for screening dementia and mild cognitive impairment in a Chinese population. BMC Neurol. 2012;12:136.

    Article  PubMed  PubMed Central  Google Scholar 

  55. O’Neil-Pirozzi TM, Goldstein R, Strangman GE, Glenn MB. Test–re-test reliability of the Hopkins Verbal Learning Test-Revised in individuals with traumatic brain injury. Brain Inj. 2012;26:1425–30.

    Article  PubMed  Google Scholar 

  56. Shapiro AM, Benedict RHB, Schretlen D, Brandt J. Construct and concurrent validity of the Hopkins Verbal Learning Test – Revised. Clin Neuropsychol. 1999;13:348–58.

    Article  CAS  PubMed  Google Scholar 

  57. Stewart NJ. The Hopkins Verbal Learning Test had high sensitivity and good specificity for detecting mild dementia in older people. Evid Based Nurs. 2001;4:24–4.

    Article  Google Scholar 

  58. Woods S, Scott J, Dawson M, Morgan E, Carey C, Heaton R, et al. Construct validity of Hopkins Verbal Learning Test—Revised component process measures in an HIV-1 sample. Arch Clin Neuropsychol. 2005;20:1061–71.

    Article  PubMed  Google Scholar 

  59. Hammers DB, Suhrie K, Dixon A, Gradwohl BD, Duff K, Spencer RJ. Validation of HVLT-R, BVMT-R, and RBANS learning slope scores along the Alzheimer’s continuum. Arch Clin Neuropsychol. 2022;37:78–90.

    Article  PubMed  Google Scholar 

  60. Spencer RJ, Gradwohl BD, Williams TF, Kordovski VM, Hammers DB. Developing learning slope scores for the repeatable battery for the assessment of neuropsychological status. Appl Neuropsychol. 2020;29:1–7.

    Google Scholar 

  61. Benedict RHB, Schretlen D, Groninger L, Dobraski M, Shpritz B. Revision of the Brief Visuospatial Memory Test: studies of normal performance, reliability, and validity. Psychol Assess. 1996;8:145.

    Article  Google Scholar 

  62. Nieto-Castanon A. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Boston: Hilbert Press; 2020.

  63. Whitfield-Gabrieli S, Nieto-Castanon A. CONN : A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2:125–41.

    Article  PubMed  Google Scholar 

  64. Friston KJ. editor. Statistical parametric mapping: the analysis of functional brain images. 1st ed. Amsterdam. Boston: Elsevier/Academic Press; 2007.

    Google Scholar 

  65. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37:90–101.

    Article  PubMed  Google Scholar 

  66. He L, Hu D, Wan M, Wen Y. Measuring temporal dynamics of resting-state fMRI data. Biomed Mater Eng. 2014;24:939–45.

    PubMed  Google Scholar 

  67. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34:537–41.

    Article  CAS  PubMed  Google Scholar 

  68. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–81.

    Article  PubMed  Google Scholar 

  69. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Weintraub S, Dikmen SS, Heaton RK, Tulsky DS, Zelazo PD, Bauer PJ, et al. Cognition assessment using the NIH Toolbox. Neurology. 2013;80:S54–64.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Jiang J, Liu T, Crawford JD, Kochan NA, Brodaty H, Sachdev PS, et al. Stronger bilateral functional connectivity of the frontoparietal control network in near-centenarians and centenarians without dementia. NeuroImage. 2020;215:116855.

    Article  PubMed  Google Scholar 

  72. Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16:1348–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Bai F, Zhang Z, Yu H, Shi Y, Yuan Y, Zhu W, et al. Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neurosci Lett. 2008;438:111–5.

    Article  CAS  PubMed  Google Scholar 

  74. Cao Y, Yang H, Zhou Z, Cheng Z, Zhao X. Abnormal default-mode network homogeneity in patients with mild cognitive impairment in Chinese communities. Front Neurol. 2021;11:569806.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Damoiseaux JS, Prater KE, Miller BL, Greicius MD. Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiol Aging. 2012;33:828.e19–30.

    Article  PubMed  Google Scholar 

  76. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci USA. 2004;101:4637–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Grieder M, Wang DJJ, Dierks T, Wahlund L-O, Jann K. Default mode network complexity and cognitive decline in mild Alzheimer’s disease. Front Neurosci. 2018;12:770.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Levine DA, Gross AL, Briceño EM, Tilton N, Giordani BJ, Sussman JB, Hayward RA, Burke JF, Hingtgen S, Elkind MSV, Manly JJ, Gottesman RF, Gaskin DJ, Sidney S, Sacco RL, Tom SE, Wright CB, Yaffe K, Galecki AT. Sex differences in cognitive decline among US adults. JAMA Netw Open. 2021 Feb 1;4(2):e210169. https://doi.org/10.1001/jamanetworkopen.2021.0169.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Administration for Community Living. 2020 Profile of older Americans [Internet]. 2021. Available at: https://acl.gov/sites/default/files/aging%20and%20Disability%20In%20America/2020Profileolderamericans.final_.pdf. Accessed 9/1/23.

  80. Brewster P, Barnes L, Haan M, Johnson JK, Manly JJ, Nápoles AM, et al. Progress and future challenges in aging and diversity research in the United States. Alzheimers Dement. 2019;15:995–1003.

    Article  PubMed  Google Scholar 

  81. Gilmore-Bykovskyi A, Croff R, Glover CM, Jackson JD, Resendez J, Perez A, et al. Traversing the aging research and health equity divide: toward intersectional frameworks of research justice and participation. Meeks S, editor. Gerontologist. 2022;62:711–720.

  82. Onoda K, Ishihara M, Yamaguchi S. Decreased functional connectivity by aging is associated with cognitive decline. J Cogn Neurosci. 2012;24:2186–98.

    Article  PubMed  Google Scholar 

  83. Squire LR, Zola-Morgan S. The medial temporal lobe memory system. Science. 1991;253:1380–6.

    Article  CAS  PubMed  Google Scholar 

  84. Bird CM, Burgess N. The hippocampus and memory: insights from spatial processing. Nat Rev Neurosci. 2008;9:182–94.

    Article  CAS  PubMed  Google Scholar 

  85. Bonner-Jackson A, Mahmoud S, Miller J, Banks SJ. Verbal and non-verbal memory and hippocampal volumes in a memory clinic population. Alzheimer’s Res Ther. 2015;7:61.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank all the research participants at the McKnight Brain Institutes of the Universities of Florida and Arizona, who generously volunteered their time and effort to help make this manuscript possible. We are also grateful to all research team members for their contributions to this project.

Funding

This study received support from the National Institute on Aging (NIA R01AG054077, NIA K01AG050707, NIA P30AG019610), the State of Arizona and Arizona Department of Health Services (ADHS), and the McKnight Brain Research Foundation.

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Authors and Affiliations

  1. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, PO Box 100196, 1249 Center Drive, Gainesville, FL, 32610-0165, USA

    Jori L. Waner, Hanna K. Hausman, Jessica N. Kraft, Cheshire Hardcastle, Nicole D. Evangelista, Andrew O’Shea, Alejandro Albizu, Emanuel M. Boutzoukas, Michael Marsiske, Ronald Cohen, Eric C. Porges & Adam J. Woods

  2. Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA

    Jori L. Waner, Hanna K. Hausman, Cheshire Hardcastle, Nicole D. Evangelista, Emanuel M. Boutzoukas, Michael Marsiske, Ronald Cohen, Eric C. Porges & Adam J. Woods

  3. Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA

    Jessica N. Kraft, Alejandro Albizu & Adam J. Woods

  4. Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA

    Emily J. Van Etten, Pradyumna K. Bharadwaj, Hyun Song, Samantha G. Smith & Gene E. Alexander

  5. Department of Neurology and McKnight Brain Institute, University of Florida, Gainesville, FL, USA

    Steven T. DeKosky

  6. Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA

    Georg A. Hishaw

  7. Department of Neurology, College of Medicine, University of Arizona, Tucson, AZ, USA

    Georg A. Hishaw

  8. Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA

    Samuel S. Wu

  9. Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer’s Disease Consortium, Tucson, AZ, USA

    Gene E. Alexander

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  1. Jori L. Waner
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  9. Emily J. Van Etten
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  11. Hyun Song
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  14. Georg A. Hishaw
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  15. Samuel S. Wu
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  16. Michael Marsiske
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  17. Ronald Cohen
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  18. Gene E. Alexander
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  19. Eric C. Porges
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  20. Adam J. Woods
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Correspondence to Adam J. Woods.

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Waner, J.L., Hausman, H.K., Kraft, J.N. et al. Connecting memory and functional brain networks in older adults: a resting-state fMRI study. GeroScience 45, 3079–3093 (2023). https://doi.org/10.1007/s11357-023-00967-3

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  • DOI: https://doi.org/10.1007/s11357-023-00967-3

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