Abstract
“All old people are the same” is an unfortunate characterization of the perceived homogeneity in the older age group. This study attempts to debunk this myth in the context of the structural and functional brain. Within older relative to younger age groups, individuals are hypothesized to be more dissimilar to their similar-aged peers—thus demonstrating an age-related divergence. This study analyzed functional connectivity (FC) during multiple fMRI paradigms (2 rest + 5 tasks) and cortical thickness (CT) data from two lifespan datasets (Ntotal = 1161). On average, between-subject FC/CT correlations became weaker in the older age groups. Further analyses ruled out the possibility that more rapid age-related changes in older brains have increased the dissimilarity in these older age groups. Brain-wide analyses revealed significant effects of age-related divergence across most of the brain. Finally, CT similarity between a dyad significantly predicted their FC similarity across multiple fMRI task paradigms—demonstrating a close relationship between brain structure and function even at the between-dyad level. Contrary to the myth that “all old people are the same,” these findings suggest young people are more similar to each other. This study presents major implications in the study of neural fingerprinting and brain-behavior associations.
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References
Angus J, Reeve P. Ageism: A Threat to “Aging Well” in the 21st Century. J Appl Gerontol. 2006;25:137–52. https://doi.org/10.1177/0733464805285745.
Linville PW, Fischer GW, Salovey P. Perceived distributions of the characteristics of in-group and out-group members: empirical evidence and a computer simulation. J Pers Soc Psychol. 1989;57:165–88. https://doi.org/10.1037/0022-3514.57.2.165.
Ostrom TM, Sedikides C. Out-group homogeneity effects in natural and minimal groups. Psychol Bull. 1992;112:536–52. https://doi.org/10.1037/0033-2909.112.3.536.
Levy B. Stereotype embodiment: a psychosocial approach to aging. Curr Dir Psychol Sci. 2009;18:332–6. https://doi.org/10.1111/j.1467-8721.2009.01662.x.
Thornton JE. Myths of aging or ageist stereotypes. Educ Gerontol. 2002;28:301–12. https://doi.org/10.1080/036012702753590415.
Tooley UA, Bassett DS, Mackey AP. Environmental influences on the pace of brain development. Nat Rev Neurosci. 2021;22:372–84. https://doi.org/10.1038/s41583-021-00457-5.
Seghier ML, Price CJ. Interpreting and utilising intersubject variability in brain function. Trends Cogn Sci. 2018;22:517–30. https://doi.org/10.1016/j.tics.2018.03.003.
Chavan CF, Mouthon M, Draganski B, van der Zwaag W, Spierer L. Differential patterns of functional and structural plasticity within and between inferior frontal gyri support training-induced improvements in inhibitory control proficiency. Hum Brain Mapp. 2015;36:2527–43. https://doi.org/10.1002/hbm.22789.
Groussard M, Joie RL, Rauchs G, Landeau B, Chételat G, Viader F, et al. When music and long-term memory interact: effects of musical expertise on functional and structural plasticity in the hippocampus. PLoS One. 2010;5:e13225. https://doi.org/10.1371/journal.pone.0013225.
Reuter-Lorenz PA, Cappell KA. Neurocognitive aging and the compensation hypothesis. Curr Dir Psychol Sci. 2008;17:177–82. https://doi.org/10.1111/j.1467-8721.2008.00570.x.
Walker LC. Proteopathic strains and the heterogeneity of neurodegenerative diseases. Annu Rev Genet. 2016;50:329–46. https://doi.org/10.1146/annurev-genet-120215-034943.
Storsve AB, Fjell AM, Tamnes CK, Westlye LT, Overbye K, Aasland HW, et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci. 2014;34:8488–98. https://doi.org/10.1523/JNEUROSCI.0391-14.2014.
Thambisetty M, Wan J, Carass A, An Y, Prince JL, Resnick SM. Longitudinal changes in cortical thickness associated with normal aging. Neuroimage. 2010;52:1215–23. https://doi.org/10.1016/j.neuroimage.2010.04.258.
Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7:e105–25. https://doi.org/10.1016/S2468-2667(21)00249-8.
Saccà V, Sarica A, Quattrone A, Rocca F, Quattrone A, Novellino F. Aging effect on head motion: a machine learning study on resting state fMRI data. J Neurosci Methods. 2021;352:109084. https://doi.org/10.1016/j.jneumeth.2021.109084.
Bradshaw PJ, Stobie P, Knuiman MW, Briffa TG, Hobbs MST. Trends in the incidence and prevalence of cardiac pacemaker insertions in an ageing population. Open Heart. 2014;1:e000177. https://doi.org/10.1136/openhrt-2014-000177.
Bookheimer SY, Salat DH, Terpstra M, Ances BM, Barch DM, Buckner RL, et al. The lifespan Human Connectome Project in aging: an overview. Neuroimage. 2019;185:335–48. https://doi.org/10.1016/j.neuroimage.2018.10.009.
Harms MP, Somerville LH, Ances BM, Andersson J, Barch DM, Bastiani M, et al. Extending the Human Connectome Project across ages: imaging protocols for the lifespan development and aging projects. Neuroimage. 2018;183:972–84. https://doi.org/10.1016/j.neuroimage.2018.09.060.
Ances BM, Liang CL, Leontiev O, Perthen JE, Fleisher AS, Lansing AE, et al. Effects of aging on cerebral blood flow, oxygen metabolism, and blood oxygenation level dependent responses to visual stimulation. Hum Brain Mapp. 2009;30:1120–32. https://doi.org/10.1002/hbm.20574.
Somerville LH, Bookheimer SY, Buckner RL, Burgess GC, Curtiss SW, Dapretto M, et al. The lifespan Human Connectome Project in development: a large-scale study of brain connectivity development in 5–21 year olds. Neuroimage. 2018;183:456–68. https://doi.org/10.1016/j.neuroimage.2018.08.050.
Sperling RA, Bates JF, Cocchiarella AJ, Schacter DL, Rosen BR, Albert MS. Encoding novel face-name associations: a functional MRI study. Hum Brain Mapp. 2001;14:129–39. https://doi.org/10.1002/hbm.1047.
Hasson U, Malach R, Heeger DJ. Reliability of cortical activity during natural stimulation. Trends Cogn Sci. 2010;14:40–8. https://doi.org/10.1016/j.tics.2009.10.011.
Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80:105–24. https://doi.org/10.1016/j.neuroimage.2013.04.127.
Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, et al. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453–65. https://doi.org/10.1016/j.neuroimage.2017.10.037.
Pham DD, Muschelli J, Mejia AF. ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R. Neuroimage. 2022;250:118877. https://doi.org/10.1016/j.neuroimage.2022.118877.
Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X-N, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex N Y N. 1991;2018(28):3095–114. https://doi.org/10.1093/cercor/bhx179.
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–55. https://doi.org/10.1016/S0896-6273(02)00569-X.
Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods. 2019;16:111–6. https://doi.org/10.1038/s41592-018-0235-4.
Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29:162–73. https://doi.org/10.1006/cbmr.1996.0014.
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825–41. https://doi.org/10.1006/nimg.2002.1132.
Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 2009;48:63–72.
Liu W, Kohn N, Fernández G. Intersubject similarity of personality is associated with intersubject similarity of brain connectivity patterns. Neuroimage. 2019;186:56–69. https://doi.org/10.1016/j.neuroimage.2018.10.062.
Sun X, Liu J, Ma Q, Duan J, Wang X, Xu Y, et al. Disrupted intersubject variability architecture in functional connectomes in schizophrenia. Schizophr Bull. 2021;47:837–48.
Gracia-Tabuenca Z, Alcauter S. NBR: network-based R-statistics for (unbalanced) longitudinal samples 2020;2020.11.07.373019. https://doi.org/10.1101/2020.11.07.373019.
Larivière S, Bayrak Ş, Vos de Wael R, Benkarim O, Herholz P, Rodriguez-Cruces R, et al. BrainStat: a toolbox for brain-wide statistics and multimodal feature associations. NeuroImage. 2023;266:119807. https://doi.org/10.1016/j.neuroimage.2022.119807.
Brett M, Penny W, Kiebel S. Introduction to random field theory. Hum Brain Funct. 2003;2:867–79.
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48. https://doi.org/10.18637/jss.v067.i01.
Ma L, Tian L, Hu T, Jiang T, Zuo N. Development of individual variability in brain functional connectivity and capability across the adult lifespan. Cereb Cortex. 2021;31:3925–38. https://doi.org/10.1093/cercor/bhab059.
St-Onge F, Javanray M, PichetBinette A, Strikwerda-Brown C, Remz J, Spreng RN, et al. Functional connectome fingerprinting across the lifespan. Netw Neurosci. 2023;1–55. https://doi.org/10.1162/netn_a_00320.
Goh JO, An Y, Resnick SM. Differential trajectories of age-related changes in components of executive and memory processes. Psychol Aging. 2012;27:707–19. https://doi.org/10.1037/a0026715.
Meunier D, Stamatakis EA, Tyler LK. Age-related functional reorganization, structural changes, and preserved cognition. Neurobiol Aging. 2014;35:42–54. https://doi.org/10.1016/j.neurobiolaging.2013.07.003.
Li H-J, Hou X-H, Liu H-H, Yue C-L, Lu G-M, Zuo X-N. Putting age-related task activation into large-scale brain networks: a meta-analysis of 114 fMRI studies on healthy aging. Neurosci Biobehav Rev. 2015;57:156–74. https://doi.org/10.1016/j.neubiorev.2015.08.013.
Sele S, Liem F, Mérillat S, Jäncke L. Age-related decline in the brain: a longitudinal study on inter-individual variability of cortical thickness, area, volume, and cognition. Neuroimage. 2021;240:118370. https://doi.org/10.1016/j.neuroimage.2021.118370.
Damoiseaux JS, Greicius MD. Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct Funct. 2009;213:525–33. https://doi.org/10.1007/s00429-009-0208-6.
Yu J, Fischer NL. Asymmetric generalizability of multimodal brain-behavior associations across age-groups. Hum Brain Mapp. 2022;43:5593–604. https://doi.org/10.1002/hbm.26035.
Funding
Junhong Yu is supported by the Nanyang Assistant Professorship (Award no. 021080–00001). The HCP-Aging data used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in mental health. Dataset identifier(s): https://doi.org/10.15154/6faj-nf83. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDA. The HCP-Aging study was supported by the National Institute On Aging of the National Institutes of Health under Award Number U01AG052564 and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The HCP-Aging 2.0 Release data used in this report came from https://doi.org/10.15154/1520707. Data collection and sharing for the CAM-CAN dataset was funded by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and the University of Cambridge, UK.
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Yu, J. Age-related decrease in inter-subject similarity of cortical morphology and task and resting-state functional connectivity. GeroScience 46, 697–711 (2024). https://doi.org/10.1007/s11357-023-01008-9
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DOI: https://doi.org/10.1007/s11357-023-01008-9