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

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ibanez, A., Kringelbach, M. L. & Deco, G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn. Sci. (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Coronel‐Oliveros, C. et al. Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole‐brain modeling. Alzheimers Dement. (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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. (2021).

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Arbabyazd, L. et al. Virtual connectomic datasets in Alzheimer’s disease and aging using whole-brain network dynamics modelling. eNeuro (2021).

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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 oset. Biomed. Phys. Eng. Express (2022).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

  • 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).

    Article 

    Google Scholar 

  • carlosmig/EEG-Dementias: EEG modeling in aging and dementia. Zenodo (2025).

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • link

    You May Also Like

    More From Author

    + There are no comments

    Add yours