Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia

Nature Mental Health volume 3, pages 1214–1229 (2025)Cite this article

Subjects

Abstract

Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here we combine source space connectivity (via electroencephalography) with generative brain modeling in healthy controls from the global south and north, alongside patients with Alzheimer’s disease and behavioral variant frontotemporal dementia (bvFTD) (N = 1,399). BAGs in aging were influenced by geography (south > north), income (low > high), sex (female > male) and education (low > high), with larger BAGs in patients, especially females, with Alzheimer’s disease. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Pipeline for EEG data preparation, BAG estimation and generative modeling.
Fig. 2: The BAG is modulated by the samples’ diversity and condition.
Fig. 3: Whole-brain neural mass model and biophysical mechanisms.
Fig. 4: Biophysical mechanisms associated with aging and dementia.

Similar content being viewed by others

Data availability

Raw data are available upon request from the corresponding authors. Functional and structural connectivity matrices, demographics and the data required to reproduce the results of this work are available at https://github.com/carlosmig/EEG-Dementias, with a link to Zenodo at https://doi.org/10.5281/zenodo.16420033 ref. 139.

Code availability

Analyses were conducted in MATLAB R2022a and Python (v.3.12.3). Machine learning and modeling used scikit-learn (v.1.4.2) and Numba (v.0.59.1). Statistical analysis used numpy (v.1.26.4), scipy (v.1.15.2), pandas (v.2.2.2) and statsmodels (v.0.14.2). Image processing used scikit-image (v.0.23.2). Visualization used matplotlib (v.3.8.4). Brain network analyses were performed using Brain Connectivity Toolbox (BCT, v.0.6.0) and NetworkX (v.3.2.1). EEG preprocessing used EEGLAB (v.2022.1). The codes for simulations are available at https://github.com/carlosmig/EEG-Dementias, and mirrored to a Zenodo repository at https://doi.org/10.5281/zenodo.16420033 ref. 139. The Brain Connectivity Toolbox for Python (https://github.com/fiuneuro/brainconn)133 was used for graph analysis and the BrainNet Viewer toolbox140 for visualization.

References

  1. Moguilner, S. et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat. Med. 30, 3646–3657 (2024).

    PubMed  PubMed Central  Google Scholar 

  2. Tian, Y. E. et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat. Med. 29, 1221–1231 (2023).

    PubMed  Google Scholar 

  3. Jones, D. T., Lee, J. & Topol, E. J. Digitising brain age. Lancet 400, 988–988 (2022).

    PubMed  Google Scholar 

  4. Cole, J. H. & Franke, K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690 (2017).

    PubMed  Google Scholar 

  5. Bashyam, V. M. et al. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14,468 individuals worldwide. Brain 143, 2312–2324 (2020).

    PubMed  PubMed Central  Google Scholar 

  6. Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019).

    PubMed  Google Scholar 

  7. Legaz, A. et al. Structural inequality linked to brain volume and network dynamics in aging and dementia across the Americas. Nat. Aging 5, 259–274 (2024).

    PubMed  PubMed Central  Google Scholar 

  8. Scheltens, P. et al. Alzheimer’s disease. Lancet 388, 505–517 (2016).

    PubMed  Google Scholar 

  9. Tseng, W.-Y. I., Hsu, Y.-C. & Kao, T.-W. Brain age difference at baseline predicts clinical dementia rating change in approximately two years. J. Alzheimers Dis. 86, 613–627 (2022).

    PubMed  Google Scholar 

  10. Prado, P. et al. The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Sci. Data 10, 889 (2023).

    PubMed  PubMed Central  Google Scholar 

  11. Baez, S., Alladi, S. & Ibanez, A. Global South research is critical for understanding brain health, ageing and dementia. Clin. Transl. Med. 13, e1486 (2023).

    PubMed  PubMed Central  Google Scholar 

  12. Greene, A. S. et al. Brain–phenotype models fail for individuals who defy sample stereotypes. Nature 609, 109–118 (2022).

    PubMed  PubMed Central  Google Scholar 

  13. Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).

    PubMed  PubMed Central  Google Scholar 

  14. Lewandowska, P. et al. Association between real-time strategy video game learning outcomes and pre-training brain white matter structure: preliminary study. Sci. Rep. 12, 20741 (2022).

    PubMed  PubMed Central  Google Scholar 

  15. Ranasinghe, K. G. et al. Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease. eLife 11, e77850 (2022).

    PubMed  PubMed Central  Google Scholar 

  16. Ibanez, A., Kringelbach, M. L. & Deco, G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn. Sci. https://doi.org/10.1016/j.tics.2023.12.006 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Santamaria-Garcia, H. et al. Factors associated with healthy aging in Latin American populations. Nat. Med. 29, 2248–2258 (2023).

    PubMed  PubMed Central  Google Scholar 

  18. Parra, M. A. et al. Dementia in Latin America. Neurology 90, 222–231 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. McGlinchey, E. et al. Biomarkers of neurodegeneration across the Global South. Lancet Healthy Longev 5, 100616 (2024).

    PubMed  PubMed Central  Google Scholar 

  20. Lotze, M. et al. Income is associated with hippocampal/amygdala and education with cingulate cortex grey matter volume. Sci. Rep. 10, 18786 (2020).

    PubMed  PubMed Central  Google Scholar 

  21. De Looze, C. et al. Examining the impact of socioeconomic position across the life course on cognitive function and brain structure in healthy aging. J. Gerontol. A 78, 890–901 (2023).

    Google Scholar 

  22. Yaple, Z. A. & Yu, R. Functional and structural brain correlates of socioeconomic status. Cerebral Cortex 30, 181–196 (2020).

    PubMed  Google Scholar 

  23. Wang, A. Y. et al. Socioeconomic status and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 39 prospective studies. J. Prevent. Alzheimers Dis 10, 83–94 (2023).

    Google Scholar 

  24. Migeot, J., Calivar, M., Granchetti, H., Ibáñez, A. & Fittipaldi, S. Socioeconomic status impacts cognitive and socioemotional processes in healthy ageing. Sci. Rep. 12, 6048 (2022).

    PubMed  PubMed Central  Google Scholar 

  25. Hatzenbuehler, M. L., McLaughlin, K. A., Weissman, D. G. & Cikara, M. A research agenda for understanding how social inequality is linked to brain structure and function. Nat. Hum. Behav. 8, 20–31 (2024).

    PubMed  PubMed Central  Google Scholar 

  26. Ibáñez, A., Legaz, A. & Ruiz-Adame, M. Addressing the gaps between socioeconomic disparities and biological models of dementia. Brain 146, 3561–3564 (2023).

    PubMed  PubMed Central  Google Scholar 

  27. Parra-Rodriguez, M. A. et al. The EuroLaD‐EEG consortium: towards a global EEG platform for dementia, for seeking to reduce the regional impact of dementia. Alzheimers Dement. 18, e059944 (2022).

    Google Scholar 

  28. Ribeiro, F., Teixeira-Santos, A. C., Caramelli, P. & Leist, A. K. Prevalence of dementia in Latin America and Caribbean countries: systematic review and meta-analyses exploring age, sex, rurality and education as possible determinants. Ageing Res. Rev. 81, 101703 (2022).

    PubMed  PubMed Central  Google Scholar 

  29. Vega, I. E., Cabrera, L. Y., Wygant, C. M., Velez-Ortiz, D. & Counts, S. E. Alzheimer’s disease in the Latino community: intersection of genetics and social determinants of health. J. Alzheimers Dis. 58, 979–992 (2017).

    PubMed  PubMed Central  Google Scholar 

  30. Gatica, M. et al. High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model. PLoS Comput. Biol. 18, e1010431 (2022).

    PubMed  PubMed Central  Google Scholar 

  31. Deco, G. & Kringelbach, M. L. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84, 892–905 (2014).

    PubMed  Google Scholar 

  32. Lynn, C. W. & Bassett, D. S. The physics of brain network structure, function and control. Nat. Rev. Phys. 1, 318–332 (2019).

    Google Scholar 

  33. Coronel-Oliveros, C., Gießing, C., Medel, V., Cofré, R. & Orio, P. Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation. NeuroImage 265, 119782 (2023).

    PubMed  Google Scholar 

  34. Gatica, M. et al. High-order interdependencies in the aging brain. Brain Connect. 11, 734–744 (2021).

    PubMed  Google Scholar 

  35. Prado, P. et al. Dementia ConnEEGtome: towards multicentric harmonization of EEG connectivity in neurodegeneration. Int. J. Psychophysiol. 172, 24–38 (2022).

    PubMed  Google Scholar 

  36. Alber, M. et al. Integrating machine learning and multiscale modeling—perspectives, challenges and opportunities in the biological, biomedical and behavioral sciences. npj Digit. Med. 2, 115 (2019).

    PubMed  PubMed Central  Google Scholar 

  37. Al Zoubi, O. et al. Predicting age from brain EEG signals—a machine learning approach. Front. Aging Neurosci 10, 184 (2018).

    PubMed  PubMed Central  Google Scholar 

  38. Khayretdinova, M. et al. Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset. Front. Aging Neurosci 14, 1019869 (2022).

    PubMed  PubMed Central  Google Scholar 

  39. Signorino, M., Pucci, E., Belardinelli, N., Nolfe, G. & Angeleri, F. EEG spectral analysis in vascular and Alzheimer dementia. Electroencephalogr. Clin. Neurophysiol. 94, 313–325 (1995).

    PubMed  Google Scholar 

  40. Besthorn, C. et al. Discrimination of Alzheimer’s disease and normal aging by EEG data. Electroencephalogr. Clin. Neurophysiol. 103, 241–248 (1997).

    PubMed  Google Scholar 

  41. Stefanovski, L. et al. Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease. Front. Comput. Neurosci 13, 54 (2019).

    PubMed  PubMed Central  Google Scholar 

  42. Coronel‐Oliveros, C. et al. Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole‐brain modeling. Alzheimers Dement. https://doi.org/10.1002/alz.13788 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Maestú, F., de Haan, W., Busche, M. A. & DeFelipe, J. Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology. Ageing Res. Rev. 69, 101372 (2021).

    PubMed  Google Scholar 

  44. Lopatina, O. L. et al. Excitation/inhibition imbalance and impaired neurogenesis in neurodevelopmental and neurodegenerative disorders. Rev. Neurosci. 30, 807–820 (2019).

    PubMed  Google Scholar 

  45. Martínez‐Cañada, P., Perez‐Valero, E., Minguillon, J., Pelayo, F., López Gordo, M. A. & Morillas, C. Combining aperiodic 1/f slopes and brain simulation: an EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease. Alzheimers Dement. 15, e12477 (2023).

  46. van den Heuvel, M. P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683–696 (2013).

    PubMed  Google Scholar 

  47. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).

    PubMed  PubMed Central  Google Scholar 

  48. Coronel-Oliveros, C., Castro, S., Cofré, R. & Orio, P. Structural features of the human connectome that facilitate the switching of brain dynamics via noradrenergic neuromodulation. Front. Comput. Neurosci. https://doi.org/10.3389/fncom.2021.687075 (2021).

  49. Deco, G., Van Hartevelt, T. J., Fernandes, H. M., Stevner, A. & Kringelbach, M. L. The most relevant human brain regions for functional connectivity: evidence for a dynamical workspace of binding nodes from whole-brain computational modelling. NeuroImage 146, 197–210 (2017).

    PubMed  Google Scholar 

  50. Dai, Z. et al. Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cerebral Cortex 25, 3723–3742 (2015).

    PubMed  Google Scholar 

  51. Cohen, J. R. & D’Esposito, M. The segregation and integration of distinct brain networks and their relationship to cognition. J. Neurosci. 36, 12083–12094 (2016).

    PubMed  PubMed Central  Google Scholar 

  52. Lord, L.-D., Stevner, A. B., Deco, G. & Kringelbach, M. L. Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders. Phil. Trans. R. Soc. A 375, 20160283 (2017).

  53. Moguilner, S. et al. Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration and loss of neural inhibition. Alzheimers Res. Ther. 16, 79 (2024).

    PubMed  PubMed Central  Google Scholar 

  54. Amato, L. G. et al. Personalized modeling of Alzheimer’s disease progression estimates neurodegeneration severity from EEG recordings. Alzheimers Dement. 16, e12526 (2024).

    Google Scholar 

  55. Snyder, H. M. et al. Sex biology contributions to vulnerability to Alzheimer’s disease: a think tank convened by the Women’s Alzheimer’s Research Initiative. Alzheimers Dement. 12, 1186–1196 (2016).

    PubMed  Google Scholar 

  56. de Boer, S. C. M. et al. Differences in sex distribution between genetic and sporadic frontotemporal dementia. J. Alzheimers Dis. 84, 1153–1161 (2021).

    PubMed  PubMed Central  Google Scholar 

  57. Fisher, D. W., Bennett, D. A. & Dong, H. Sexual dimorphism in predisposition to Alzheimer’s disease. Neurobiol. Aging 70, 308–324 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Ganaie, M. A., Tanveer, M. & Beheshti, I. Brain age prediction with improved least squares twin SVR. IEEE J. Biomed. Health Inform. 27, 1661–1669 (2023).

    PubMed  Google Scholar 

  59. Birba, A. et al. Allostatic-interoceptive overload in frontotemporal dementia. Biol. Psychiatry 92, 54–67 (2022).

    PubMed  PubMed Central  Google Scholar 

  60. Migeot, J. A., Duran-Aniotz, C. A., Signorelli, C. M., Piguet, O. & Ibáñez, A. A predictive coding framework of allostatic-interoceptive overload in frontotemporal dementia. Trends Neurosci. 45, 838–853 (2022).

    PubMed  PubMed Central  Google Scholar 

  61. Abdel-Naseer, M. Epidemiology of dementia in developing countries. J. Neurol. Sci. 405, 72–73 (2019).

    Google Scholar 

  62. Ibañez, A. & Manes, F. Contextual social cognition and the behavioral variant of frontotemporal dementia. Neurology 78, 1354–1362 (2012).

    PubMed  PubMed Central  Google Scholar 

  63. Pandics, T. et al. Exposome and unhealthy aging: environmental drivers from air pollution to occupational exposures. GeroScience 45, 3381–3408 (2023).

    PubMed  PubMed Central  Google Scholar 

  64. Finch, C. E. & Kulminski, A. M. The Alzheimer’s disease exposome. Alzheimers Dement. 15, 1123–1132 (2019).

    PubMed  Google Scholar 

  65. Risk factors related to population diversity and disparity determine healthy aging. Nat. Med. 29, 2183–2184 (2023).

  66. Griffa, A. & Van den Heuvel, M. P. Rich-club neurocircuitry: function, evolution and vulnerability. Dialogues Clin. Neurosci. 20, 121–132 (2018).

    PubMed  PubMed Central  Google Scholar 

  67. Chenot, Q., Lepron, E., De Boissezon, X. & Scannella, S. Functional connectivity within the fronto-parietal network predicts complex task performance: a fNIRS study. Front. Neuroergonomics 2, 718176 (2021).

    Google Scholar 

  68. Ptak, R. The frontoparietal attention network of the human brain. Neuroscientist 18, 502–515 (2012).

    PubMed  Google Scholar 

  69. Küchenhoff, S. et al. Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks. Eur. J. Neurosci. 53, 3362–3377 (2021).

    PubMed  Google Scholar 

  70. Kim, Y. H. et al. Real-time strategy video game experience and visual perceptual learning. J. Neurosci. 35, 10485–10492 (2015).

    PubMed  PubMed Central  Google Scholar 

  71. Campbell, K. L., Grady, C. L., Ng, C. & Hasher, L. Age differences in the frontoparietal cognitive control network: implications for distractibility. Neuropsychologia 50, 2212–2223 (2012).

    PubMed  PubMed Central  Google Scholar 

  72. Xia, H., He, Q. & Chen, A. Understanding cognitive control in aging: a brain network perspective. Front. Aging Neurosci 14, 1038756 (2022).

    PubMed  PubMed Central  Google Scholar 

  73. Musa, G. et al. Alzheimer’s disease or behavioral variant frontotemporal dementia? Review of key points toward an accurate clinical and neuropsychological diagnosis. J. Alzheimers Dis. 73, 833–848 (2020).

    PubMed  PubMed Central  Google Scholar 

  74. Boroshok, A. L. et al. Individual differences in frontoparietal plasticity in humans. npj Sci. Learn. 7, 14 (2022).

    PubMed  PubMed Central  Google Scholar 

  75. Berry, K. P. & Nedivi, E. Experience-dependent structural plasticity in the visual system. Annu. Rev. Vis. Sci. 2, 17–35 (2016).

    PubMed  PubMed Central  Google Scholar 

  76. Nikolaidis, A., Voss, M. W., Lee, H., Vo, L. T. K. & Kramer, A. F. Parietal plasticity after training with a complex video game is associated with individual differences in improvements in an untrained working memory task. Front. Hum. Neurosci 8, 169 (2014).

    PubMed  PubMed Central  Google Scholar 

  77. Schmidt, S. et al. Experience-dependent structural plasticity in the adult brain: how the learning brain grows. NeuroImage 225, 117502 (2021).

    PubMed  Google Scholar 

  78. Kowalczyk, N. et al. Real‐time strategy video game experience and structural connectivity–a diffusion tensor imaging study. Hum. Brain Mapp. 39, 3742–3758 (2018).

    PubMed  PubMed Central  Google Scholar 

  79. Toepper, M. Dissociating normal aging from Alzheimer’s disease: a view from cognitive neuroscience. J. Alzheimers Dis. 57, 331–352 (2017).

    PubMed  PubMed Central  Google Scholar 

  80. Illán‐Gala, I. et al. Sex differences in the behavioral variant of frontotemporal dementia: a new window to executive and behavioral reserve. Alzheimers Dement. 17, 1329–1341 (2021).

    PubMed  Google Scholar 

  81. Moore, K. M. et al. Age at symptom onset and death and disease duration in genetic frontotemporal dementia: an international retrospective cohort study. Lancet Neurol. 19, 145–156 (2020).

    PubMed  Google Scholar 

  82. Perry, D. C. et al. Clinicopathological correlations in behavioural variant frontotemporal dementia. Brain 140, 3329–3345 (2017).

    PubMed  PubMed Central  Google Scholar 

  83. Pini, L. et al. Brain atrophy in Alzheimer’s disease and aging. Ageing Res. Rev. 30, 25–48 (2016).

    PubMed  Google Scholar 

  84. Gili, T. et al. Regional brain atrophy and functional disconnection across Alzheimer’s disease evolution. J. Neurol. Neurosurg. Psychiatry 82, 58–66 (2011).

    PubMed  Google Scholar 

  85. La Joie, R. et al. Region-specific hierarchy between atrophy, hypometabolism and β-amyloid (Aβ) load in Alzheimer’s disease dementia. J. Neurosci. 32, 16265–16273 (2012).

    PubMed  PubMed Central  Google Scholar 

  86. Klupp, E. et al. Prefrontal hypometabolism in Alzheimer disease is related to longitudinal amyloid accumulation in remote brain regions. J. Nucl. Med. 56, 399–404 (2015).

    PubMed  Google Scholar 

  87. Förster, S. et al. Regional expansion of hypometabolism in Alzheimer’s disease follows amyloid deposition with temporal delay. Biol. Psychiatry 71, 792–797 (2012).

    PubMed  Google Scholar 

  88. Herman, J. P., Nawreen, N., Smail, M. A. & Cotella, E. M. Brain mechanisms of HPA axis regulation: neurocircuitry and feedback in context Richard Kvetnansky lecture. Stress 23, 617–632 (2020).

    PubMed  PubMed Central  Google Scholar 

  89. Dedovic, K., Duchesne, A., Andrews, J., Engert, V. & Pruessner, J. C. The brain and the stress axis: the neural correlates of cortisol regulation in response to stress. NeuroImage 47, 864–871 (2009).

    PubMed  Google Scholar 

  90. Kelberman, M. A. et al. Consequences of hyperphosphorylated tau in the locus coeruleus on behavior and cognition in a rat model of Alzheimer’s disease. J. Alzheimers Dis. 86, 1037–1059 (2022).

    PubMed  PubMed Central  Google Scholar 

  91. Kelberman, M. A. et al. Age-dependent dysregulation of locus coeruleus firing in a transgenic rat model of Alzheimer’s disease. Neurobiol. Aging 125, 98–108 (2023).

    PubMed  PubMed Central  Google Scholar 

  92. Corriveau-Lecavalier, N., Mellah, S., Clément, F. & Belleville, S. Evidence of parietal hyperactivation in individuals with mild cognitive impairment who progressed to dementia: a longitudinal fMRI study. NeuroImage Clin. 24, 101958 (2019).

    PubMed  PubMed Central  Google Scholar 

  93. Arbabyazd, L. et al. Virtual connectomic datasets in Alzheimer’s disease and aging using whole-brain network dynamics modelling. eNeuro https://doi.org/10.1523/ENEURO.0475-20.2021 (2021).

  94. Sanz Perl, Y. et al. Model-based whole-brain perturbational landscape of neurodegenerative diseases. eLife 12, e83970 (2023).

    PubMed  PubMed Central  Google Scholar 

  95. Ipiña, I. P. et al. Modeling regional changes in dynamic stability during sleep and wakefulness. NeuroImage 215, 116833 (2020).

    PubMed  Google Scholar 

  96. Sanz Perl, Y. et al. Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness. PLoS Comput. Biol. 17, e1009139 (2021).

    PubMed  PubMed Central  Google Scholar 

  97. Woodard, J. L. & Sugarman, M. A. in Behavioral Neurobiology of Aging 113–136 (Springer, 2011).

  98. Fujita, S. et al. Characterization of brain volume changes in aging individuals with normal cognition using serial magnetic resonance imaging. JAMA Netw. Open 6, e2318153 (2023).

    PubMed  PubMed Central  Google Scholar 

  99. Ibañez, A. et al. Predicting and characterizing neurodegenerative subtypes with multimodal neurocognitive signatures of social and cognitive processes. J. Alzheimers Dis. 83, 227–248 (2021).

    PubMed  PubMed Central  Google Scholar 

  100. Walhovd, K. B. et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cerebral Cortex 32, 839–854 (2022).

    PubMed  Google Scholar 

  101. Hunt, J. F. V. et al. Association of neighborhood-level disadvantage with cerebral and hippocampal volume. JAMA Neurol. 77, 451–460 (2020).

    PubMed  PubMed Central  Google Scholar 

  102. Hernandez, H. et al. Brain health in diverse settings: how age, demographics and cognition shape brain function. NeuroImage 295, 120636 (2024).

    PubMed  Google Scholar 

  103. Marek, S. & Laumann, T. O. Replicability and generalizability in population psychiatric neuroimaging. Neuropsychopharmacology 50, 52–57 (2024).

    PubMed  PubMed Central  Google Scholar 

  104. Ibanez, A. et al. The multi-partner consortium to expand dementia research in Latin America (ReDLat): driving multicentric research and implementation science. Front. Neurol 12, 631722 (2021).

    PubMed  PubMed Central  Google Scholar 

  105. McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging‐Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269 (2011).

    PubMed  Google Scholar 

  106. Dubois, B. et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS–ADRDA criteria. Lancet Neurol. 6, 734–746 (2007).

    PubMed  Google Scholar 

  107. Rascovsky, K. et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134, 2456–2477 (2011).

    PubMed  PubMed Central  Google Scholar 

  108. Hu, S., Lai, Y., Valdes-Sosa, P. A., Bringas-Vega, M. L. & Yao, D. How do reference montage and electrodes setup affect the measured scalp EEG potentials? J. Neural Eng. 15, 026013 (2018).

    PubMed  Google Scholar 

  109. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    PubMed  Google Scholar 

  110. Pion-Tonachini, L., Kreutz-Delgado, K. & Makeig, S. ICLabel: an automated electroencephalographic independent component classifier, dataset and website. NeuroImage 198, 181–197 (2019).

    PubMed  Google Scholar 

  111. Zhao, L. et al. Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective. Physiol. Meas. 44, 035009–035009 (2023).

    Google Scholar 

  112. Grech, R. et al. Review on solving the inverse problem in EEG source analysis. J. NeuroEng. Rehabil. 5, 25 (2008).

    PubMed  PubMed Central  Google Scholar 

  113. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289 (2002).

    PubMed  Google Scholar 

  114. Rommel, C., Paillard, J., Moreau, T. & Gramfort, A. Data augmentation for learning predictive models on EEG: a systematic comparison. J. Neural Eng. 19, 066020 (2022).

    Google Scholar 

  115. He, C., Liu, J., Zhu, Y. & Du, W. Data augmentation for deep neural networks model in EEG classification task: a review. Front. Hum. Neurosci 15, 765525 (2021).

    PubMed  PubMed Central  Google Scholar 

  116. Smith, S. M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T. E. & Miller, K. L. Estimation of brain age delta from brain imaging. NeuroImage 200, 528–539 (2019).

    PubMed  Google Scholar 

  117. Chen, B. et al. Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nat. Commun. 13, 4636 (2022).

    PubMed  PubMed Central  Google Scholar 

  118. Gonzalez-Gomez, R. et al. Qualitative and quantitative educational disparities and brain signatures in healthy aging and dementia across global settings. eClinicalMedicine 82, 103187 (2025).

    PubMed  PubMed Central  Google Scholar 

  119. Filmer, D., Rogers, H., Angrist, N. & Sabarwal, S. Learning-adjusted years of schooling (LAYS): defining a new macro measure of education. Econ. Educ. Rev. 77, 101971 (2020).

    Google Scholar 

  120. David, O. & Friston, K. J. A neural mass model for MEG/EEG. NeuroImage 20, 1743–1755 (2003).

    PubMed  Google Scholar 

  121. Otero, M., Lea-Carnall, C., Prado, P., Escobar, M.-J. & El-Deredy, W. Modelling neural entrainment and its persistence: influence of frequency of stimulation and phase at the stimulus offset. Biomed. Phys. Eng. Express https://doi.org/10.1088/2057-1976/ac605a (2022).

    Article  PubMed  Google Scholar 

  122. Jansen, B. H. & Rit, V. G. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol. Cybern. 73, 357–366 (1995).

    PubMed  Google Scholar 

  123. Gilbert, C. D., Hirsch, J. A. & Wiesel, T. N. Lateral interactions in visual cortex. Cold Spring Harb. Symp. Quant. Biol. 55, 663–677 (1990).

    PubMed  Google Scholar 

  124. McGuire, B. A., Gilbert, C. D., Rivlin, P. K. & Wiesel, T. N. Targets of horizontal connections in macaque primary visual cortex. J. Comp. Neurol. 305, 370–392 (1991).

    PubMed  Google Scholar 

  125. Abeysuriya, R. G. et al. A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Comput. Biol. 14, e1006007 (2018).

    PubMed  PubMed Central  Google Scholar 

  126. Gütig, R., Aharonov, R., Rotter, S. & Sompolinsky, H. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J. Neurosci. 23, 3697–3714 (2003).

    PubMed  PubMed Central  Google Scholar 

  127. Ito, T. et al. Task-evoked activity quenches neural correlations and variability across cortical areas. PLoS Comput. Biol. 16, e1007983 (2020).

    PubMed  PubMed Central  Google Scholar 

  128. Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).

    PubMed  Google Scholar 

  129. Medel, V., Irani, M., Crossley, N., Ossandón, T. & Boncompte, G. Complexity and 1/f slope jointly reflect brain states. Sci. Rep. 13, 21700 (2023).

  130. Daianu, M. et al. Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer’s disease. Hum. Brain Mapp. 37, 868–883 (2016).

    PubMed  Google Scholar 

  131. de Haan, W., Mott, K., van Straaten, E. C. W., Scheltens, P. & Stam, C. J. Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease. PLoS Comput. Biol. 8, e1002582 (2012).

    PubMed  PubMed Central  Google Scholar 

  132. Kizilirmak, J. M., Soch, J., Richter, A. & Schott, B. H. Age-related differences in fMRI subsequent memory effects are directly linked to local grey matter volume differences. Neurobiol. Aging 134, 160–164 (2024).

    PubMed  Google Scholar 

  133. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010).

    PubMed  Google Scholar 

  134. Lancichinetti, A. & Fortunato, S. Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012).

    PubMed  PubMed Central  Google Scholar 

  135. Newman, M. E. J. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).

    PubMed  PubMed Central  Google Scholar 

  136. Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

    PubMed  PubMed Central  Google Scholar 

  137. Hofmann, M. A. Searching for effects in big data: why p-values are not advised and what to use instead. In Proc. Winter Simulation Conference (eds Yilmaz, L. et al.) 725–736 (IEEE, 2015).

  138. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  139. carlosmig/EEG-Dementias: EEG modeling in aging and dementia. Zenodo https://doi.org/10.5281/zenodo.16420033 (2025).

  140. Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910 (2013).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by Latin American Brain Health Institute (BrainLat) award BL-SRGP2020-02 to M.A.P. and A.I. A.I. is supported by grants from ReDLat (National Institutes of Health and the Fogarty International Center (FIC), the National Institutes of Aging (R01 AG057234, R01 AG075775, AG021051, R01 AG083799, CARDS-NIH 75N95022C00031), Alzheimer’s Association (SG-20-725707), Rainwater Charitable Foundation, The Bluefield project to cure FTD and the Global Brain Health Institute), ANID/FONDECYT Regular (1210195, 1210176 and 1220995) and ANID/FONDAP/15150012. A.M.G. is partially supported by the National Institute On Aging of the National Institutes of Health (R01 AG075775, R01 AG083799, 2P01AG019724), ANID (FONDECYT Regular 1210176, 1210195) and DICYT-USACH (032351G_DAS). The contents of this publication are solely the author’s responsibility and do not represent the official views of these institutions.

Author information

Authors and Affiliations

  1. Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile

    Carlos Coronel-Oliveros, Sebastián Moguilner, Hernan Hernandez, Josephine Cruzat, Vicente Medel, Jhosmary Cuadros, Agustina Legaz, Sol Fittipaldi, Raul Gonzalez-Gomez, Rubén Herzog, Pavel Prado, Enzo Tagliazucchi & Agustin Ibanez

  2. Global Brain Health Institute (GBHI), University of California, San Francisco, US and Trinity College Dublin, Dublin, Ireland

    Carlos Coronel-Oliveros, Sandra Baez, Sol Fittipaldi, Robert Whelan, Brian Lawlor & Agustin Ibanez

  3. Trinity College Dublin, The University of Dublin, Dublin, Ireland

    Carlos Coronel-Oliveros, Brian Lawlor & Agustin Ibanez

  4. Harvard Medical School, Boston, MA, USA

    Sebastián Moguilner

  5. Universidad de los Andes, Bogota, Colombia

    Sandra Baez

  6. Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Jhosmary Cuadros

  7. Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal, Venezuela

    Jhosmary Cuadros

  8. Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia

    Hernando Santamaria-Garcia

  9. Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia

    Hernando Santamaria-Garcia

  10. The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China

    Pedro A. Valdes-Sosa

  11. Cuban Neuroscience Center, La Habana, Cuba

    Pedro A. Valdes-Sosa

  12. Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia

    Francisco Lopera & John Fredy Ochoa-Gómez

  13. Master programme of Clinical Neuropsychology, Universidad Surcolombiana, Neiva, Colombia

    Alfredis González-Hernández

  14. Department of Psychology, Universidad Cooperativa de Colombia, Medellín, Colombia

    Jasmín Bonilla-Santos

  15. Neurocognition and Psychophysiology Laboratory, Universidad Surcolombiana, Neiva, Colombia

    Rodrigo A. Gonzalez-Montealegre

  16. Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey

    Tuba Aktürk, Ebru Yıldırım & Bahar Güntekin

  17. Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

    Renato Anghinah

  18. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil

    Renato Anghinah

  19. Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina

    Agustina Legaz

  20. School of Psychology, Trinity College Dublin, Dublin, Ireland

    Sol Fittipaldi & Robert Whelan

  21. Faculty of Medicine, Izmir University of Economics, Izmir, Turkey

    Görsev G. Yener

  22. Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey

    Görsev G. Yener

  23. Izmir Biomedicine and Genome Center, Izmir, Turkey

    Görsev G. Yener

  24. School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Edinburgh, UK

    Javier Escudero

  25. Department of Physiology and Pharmacology ‘V. Erspamer’, Sapienza University of Rome, Rome, Italy

    Claudio Babiloni & Susanna Lopez

  26. Hospital San Raffaele Cassino, Cassino, Italy

    Claudio Babiloni

  27. Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain

    Alberto Fernández

  28. Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile

    David Huepe

  29. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK

    Gaetano Di Caterina

  30. Universidad Católica San Pablo, Arequipa, Perú

    Marcio Soto-Añari

  31. Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia

    Eduar Herrera

  32. Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK

    Daniel Abasolo

  33. School of Psychology, University of Glasgow, Glasgow, UK

    Kerry Kilborn

  34. Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK

    Nicolás Rubido

  35. Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Strathclyde, UK

    Ruaridh Clark

  36. Institut du Cerveau – Paris Brain Institute – ICM, Sorbonne Université, Paris, France

    Rubén Herzog

  37. Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey

    Deniz Yerlikaya

  38. Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey

    Bahar Güntekin

  39. Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain

    Gustavo Deco

  40. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

    Gustavo Deco

  41. Institució Catalana de Recerca I Estudis Avancats (ICREA), Barcelona, Spain

    Gustavo Deco

  42. Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia

    Gustavo Deco

  43. Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile

    Pavel Prado

  44. Department of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK

    Mario A. Parra

  45. Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile

    Patricio Orio

  46. Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile

    Patricio Orio

  47. Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina

    Agustin Ibanez

Authors
  1. Carlos Coronel-Oliveros
    View author publications

    Search author on:PubMed Google Scholar

  2. Sebastián Moguilner
    View author publications

    Search author on:PubMed Google Scholar

  3. Hernan Hernandez
    View author publications

    Search author on:PubMed Google Scholar

  4. Josephine Cruzat
    View author publications

    Search author on:PubMed Google Scholar

  5. Sandra Baez
    View author publications

    Search author on:PubMed Google Scholar

  6. Vicente Medel
    View author publications

    Search author on:PubMed Google Scholar

  7. Jhosmary Cuadros
    View author publications

    Search author on:PubMed Google Scholar

  8. Hernando Santamaria-Garcia
    View author publications

    Search author on:PubMed Google Scholar

  9. Pedro A. Valdes-Sosa
    View author publications

    Search author on:PubMed Google Scholar

  10. Francisco Lopera
    View author publications

    Search author on:PubMed Google Scholar

  11. John Fredy Ochoa-Gómez
    View author publications

    Search author on:PubMed Google Scholar

  12. Alfredis González-Hernández
    View author publications

    Search author on:PubMed Google Scholar

  13. Jasmín Bonilla-Santos
    View author publications

    Search author on:PubMed Google Scholar

  14. Rodrigo A. Gonzalez-Montealegre
    View author publications

    Search author on:PubMed Google Scholar

  15. Tuba Aktürk
    View author publications

    Search author on:PubMed Google Scholar

  16. Ebru Yıldırım
    View author publications

    Search author on:PubMed Google Scholar

  17. Renato Anghinah
    View author publications

    Search author on:PubMed Google Scholar

  18. Agustina Legaz
    View author publications

    Search author on:PubMed Google Scholar

  19. Sol Fittipaldi
    View author publications

    Search author on:PubMed Google Scholar

  20. Görsev G. Yener
    View author publications

    Search author on:PubMed Google Scholar

  21. Javier Escudero
    View author publications

    Search author on:PubMed Google Scholar

  22. Claudio Babiloni
    View author publications

    Search author on:PubMed Google Scholar

  23. Susanna Lopez
    View author publications

    Search author on:PubMed Google Scholar

  24. Robert Whelan
    View author publications

    Search author on:PubMed Google Scholar

  25. Alberto Fernández
    View author publications

    Search author on:PubMed Google Scholar

  26. David Huepe
    View author publications

    Search author on:PubMed Google Scholar

  27. Gaetano Di Caterina
    View author publications

    Search author on:PubMed Google Scholar

  28. Marcio Soto-Añari
    View author publications

    Search author on:PubMed Google Scholar

  29. Raul Gonzalez-Gomez
    View author publications

    Search author on:PubMed Google Scholar

  30. Eduar Herrera
    View author publications

    Search author on:PubMed Google Scholar

  31. Daniel Abasolo
    View author publications

    Search author on:PubMed Google Scholar

  32. Kerry Kilborn
    View author publications

    Search author on:PubMed Google Scholar

  33. Nicolás Rubido
    View author publications

    Search author on:PubMed Google Scholar

  34. Ruaridh Clark
    View author publications

    Search author on:PubMed Google Scholar

  35. Rubén Herzog
    View author publications

    Search author on:PubMed Google Scholar

  36. Deniz Yerlikaya
    View author publications

    Search author on:PubMed Google Scholar

  37. Bahar Güntekin
    View author publications

    Search author on:PubMed Google Scholar

  38. Gustavo Deco
    View author publications

    Search author on:PubMed Google Scholar

  39. Pavel Prado
    View author publications

    Search author on:PubMed Google Scholar

  40. Mario A. Parra
    View author publications

    Search author on:PubMed Google Scholar

  41. Patricio Orio
    View author publications

    Search author on:PubMed Google Scholar

  42. Enzo Tagliazucchi
    View author publications

    Search author on:PubMed Google Scholar

  43. Brian Lawlor
    View author publications

    Search author on:PubMed Google Scholar

  44. Agustin Ibanez
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization was provided by A.I. and C.C.-O. Supervision was provided by A.I. Methodology and analyses were performed by C.C.-O., H.H. and J. Cruzat. Data were collated by R.G.-G., J. Cruzat and P.P. The original draft was written by C.C.-O. and A.I. Review and editing of the paper were carried out by C.C.-O., S.M., H.H., J. Cruzat, S.B., V.M., J. Cuadros, H.S.-G., P.A.V.-S., F.L., J.F.O.-G., A.G.-H., J.B.-S., R.A.G.-M., T.A., E.Y., R.A., A.L., S.F., G.G.Y., J.E., C.B., S.L., R.W., A.F., D.H., G.D.C., M.S.-A., R.G.-G., E.H., D.A., K.K., N.R., R.C., R.H., D.Y., B.G., G.D., P.P., M.A.P., P.O., E.T., B.L. and A.I.

Corresponding author

Correspondence to Agustin Ibanez.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Carina Fernandes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, figures, tables and references.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coronel-Oliveros, C., Moguilner, S., Hernandez, H. et al. Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia. Nat. Mental Health 3, 1214–1229 (2025). https://doi.org/10.1038/s44220-025-00502-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44220-025-00502-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing