Structural inequality and temporal brain dynamics across diverse samples

Sandra Baez , Hernan Hernandez , Sebastian Moguilner , Jhosmary Cuadros , Hernando Santamaria-Garcia , Vicente Medel , Joaquín Migeot , Josephine Cruzat , Pedro A. Valdes-Sosa , Francisco Lopera , Alfredis González-Hernández , Jasmin Bonilla-Santos , Rodrigo A. Gonzalez-Montealegre , Tuba Aktürk , Agustina Legaz , Florencia Altschuler , Sol Fittipaldi , Görsev G. Yener , Javier Escudero , Claudio Babiloni , Susanna Lopez , Robert Whelan , Alberto A Fernández Lucas , David Huepe , Marcio Soto-Añari , Carlos Coronel-Oliveros , Eduar Herrera , Daniel Abasolo , Ruaridh A. Clark , Bahar Güntekin , Claudia Duran-Aniotz , Mario A. Parra , Brian Lawlor , Enzo Tagliazucchi , Pavel Prado , Agustin Ibanez

Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (10) : e70032

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Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (10) : e70032 DOI: 10.1002/ctm2.70032
RESEARCH ARTICLE

Structural inequality and temporal brain dynamics across diverse samples

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Abstract

Background: Structural income inequality – the uneven income distribution across regions or countries – could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored.

Methods: Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed.

Findings: Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions.

Conclusion: These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Keywords

brain dynamics / cognition / demographics / EEG / individual differences / structural income inequality

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Sandra Baez, Hernan Hernandez, Sebastian Moguilner, Jhosmary Cuadros, Hernando Santamaria-Garcia, Vicente Medel, Joaquín Migeot, Josephine Cruzat, Pedro A. Valdes-Sosa, Francisco Lopera, Alfredis González-Hernández, Jasmin Bonilla-Santos, Rodrigo A. Gonzalez-Montealegre, Tuba Aktürk, Agustina Legaz, Florencia Altschuler, Sol Fittipaldi, Görsev G. Yener, Javier Escudero, Claudio Babiloni, Susanna Lopez, Robert Whelan, Alberto A Fernández Lucas, David Huepe, Marcio Soto-Añari, Carlos Coronel-Oliveros, Eduar Herrera, Daniel Abasolo, Ruaridh A. Clark, Bahar Güntekin, Claudia Duran-Aniotz, Mario A. Parra, Brian Lawlor, Enzo Tagliazucchi, Pavel Prado, Agustin Ibanez. Structural inequality and temporal brain dynamics across diverse samples. Clinical and Translational Medicine, 2024, 14(10): e70032 DOI:10.1002/ctm2.70032

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References

[1]

Ibanez A, Melloni L, Świeboda P, et al. Neuroecological links of the exposome and One Health. Neuron. 2024; 112: 1905-191.

[2]

McCartney G, Hearty W, Arnot J, Popham F, Cumbers A, McMaster R. Impact of political economy on population health: a systematic review of reviews. Am J Public Health. 2019; 109(6): e1-e12.

[3]

Hatzenbuehler ML, McLaughlin KA, Weissman DG, Cikara M. A research agenda for understanding how social inequality is linked to brain structure and function. Nat Human Behav. 2024; 8: 20-31.

[4]

Ibáñez A, Legaz A, Ruiz-Adame M. Addressing the gaps between socioeconomic disparities and biological models of dementia. Brain. 2023; 146(9): 3561-3564.

[5]

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

[6]

Ibanez A, Legaz A, Ruiz-Adame M. Addressing the gaps between socioeconomic disparities and biological models of dementia. Brain. 2023; 146(9): 3561-3564.

[7]

Aranda MP, Kremer IN, Hinton L, et al. Impact of dementia: Health disparities, population trends, care interventions, and economic costs. J Am Geriatr Soc. 2021; 69(7): 1774-1783.

[8]

Resende EdPF, Guerra JJL, Miller BL. Health and socioeconomic inequities as contributors to brain health. JAMA Neurol. 2019; 76(6): 633-634.

[9]

Legaz A, Altschuler F, Gonzalez-Gomez R, et al. Structural inequality linked to brain volume and network dynamics in aging and dementia across the Americas. Nat Aging. Accepted.

[10]

Moguilner S, Baez S, Hernandez H, et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat Med. 2024.

[11]

Santamaria-Garcia NS-BA, Hernandez, H, Moguilner, S, et al. Factors associated with healthy aging in Latin American populations. Nat Med. 2023; 29: 2248-2258.

[12]

Migeot J, Calivar M, Granchetti H, Ibáñez A, Fittipaldi S. Socioeconomic status impacts cognitive and socioemotional processes in healthy ageing. Sci Rep. 2022; 12(1): 6048.

[13]

Wang A-Y, Hu H-Y, Ou Y-N, et al. Socioeconomic status and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 39 prospective studies. J Prev Alzheimers Dis. 2022; 10: 83-94.

[14]

Robinson O, Chadeau Hyam M, Karaman I, et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell. 2020; 19(6): e13149.

[15]

De Looze C, Demnitz N, Knight S, et al. Examining the impact of socioeconomic position across the life course on cognitive function and brain structure in healthy aging. J Gerontol A. 2023; 78(6): 890-901.

[16]

Walhovd KB, Fjell AM, Wang Y, et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cereb Cortex. 2022; 32(4): 839-854.

[17]

Chan MY, Na J, Agres PF, Savalia NK, Park DC, Wig GS. Socioeconomic status moderates age-related differences in the brain’s functional network organization and anatomy across the adult lifespan. Proc Natl Acad Sci USA. 2018; 115(22): E5144-E5153.

[18]

Lotze M, Domin M, Schmidt C, Hosten N, Grabe H, Neumann N. Income is associated with hippocampal/amygdala and education with cingulate cortex grey matter volume. Sci Rep. 2020; 10(1): 18786.

[19]

Tomalski P, Moore DG, Ribeiro H, et al. Socioeconomic status and functional brain development -associations in early infancy. Dev Sci. 2013; 16(5): 676-687.

[20]

Wilkinson CL, Pierce LJ, Sideridis G, Wade M, Nelson CA. Associations between EEG trajectories, family income, and cognitive abilities over the first two years of life. Dev Cogn Neurosci. 2023; 61: 101260.

[21]

Cantiani C, Piazza C, Mornati G, Molteni M, Riva V. Oscillatory gamma activity mediates the pathway from socioeconomic status to language acquisition in infancy. Infant Behav Dev. 2019; 57: 101384.

[22]

Maguire MJ, Schneider JM. Socioeconomic status related differences in resting state EEG activity correspond to differences in vocabulary and working memory in grade school. Brain Cogn. 2019; 137: 103619.

[23]

Parameshwaran D, Sathishkumar S, Thiagarajan TC. The impact of socioeconomic and stimulus inequality on human brain physiology. Sci Rep. 2021; 11(1): 7439.

[24]

Villada C, Gonzalez-Lopez M, Aguilar-Zavala H, Fernandez T. Resting EEG, hair cortisol and cognitive performance in healthy older people with different perceived socioeconomic status. Brain Sci. 2020; 10(9): 635.

[25]

Hatzenbuehler ML, McLaughlin KA, Weissman DG, Cikara M. A research agenda for understanding how social inequality is linked to brain structure and function. Nat Hum Behav. 2024; 8(1): 20-31.

[26]

Rebello V, Uban KA. A call to leverage a health equity lens to accelerate human neuroscience research. Front Integr Neurosci. 2023; 17: 1035597.

[27]

Charles V, Gherman T, Paliza JC. The Gini Index: a modern measure of inequality. In: Charles V, Emrouznejad A, eds. Modern Indices for International Economic Diplomacy. Palgrave Macmillan; 2022: 55-84.

[28]

Parker N, Wong AP, Leonard G, et al. Income inequality, gene expression, and brain maturation during adolescence. Sci Rep. 2017; 7(1): 7397.

[29]

Cifuentes M, Sembajwe G, Tak S, Gore R, Kriebel D, Punnett L. The association of major depressive episodes with income inequality and the human development index. Soc Sci Med. 2008; 67(4): 529-539.

[30]

Ladin K, Daniels N, Kawachi I. Exploring the relationship between absolute and relative position and late-life depression: evidence from 10 European countries. Gerontologist. 2010; 50(1): 48-59.

[31]

Dierckens M, Weinberg D, Huang Y, et al. National-level wealth inequality and socioeconomic inequality in adolescent mental well-being: a time series analysis of 17 countries. J Adolesc Health. 2020; 66(6S): S21-S28.

[32]

Yu S. Uncovering the hidden impacts of inequality on mental health: a global study. Transl Psychiatry. 2018; 8(1): 98.

[33]

Peterson RL, Carvajal SC, McGuire LC, Fain MJ, Bell ML. State inequality, socioeconomic position and subjective cognitive decline in the United States. SSM Popul Health. 2019; 7: 100357.

[34]

Ribeiro AI, Fraga S, Kelly-Irving M, et al. Neighbourhood socioeconomic deprivation and allostatic load: a multi-cohort study. Sci Rep. 2019; 9(1): 1-11.

[35]

Yu S, Qian L, Ma J. The influence of gender and wealth inequality on Alzheimer’s disease among the elderly: A global study. Heliyon. 2023; 9(4): e14677.

[36]

Sheridan MA. Measuring the impact of structural inequality on the structure of the brain. Proc Natl Acad Sci USA. 2023; 120(25): e2306076120.

[37]

Zugman A, Alliende LM, Medel V, et al. Country-level gender inequality is associated with structural differences in the brains of women and men. Proc Natl Acad Sci USA. 2023; 120(20): e2218782120.

[38]

Weissman DG, Hatzenbuehler ML, Cikara M, Barch DM, McLaughlin KA. State-level macro-economic factors moderate the association of low income with brain structure and mental health in US children. Nat Commun. 2023; 14(1): 2085.

[39]

Hunt JF, Buckingham W, Kim AJ, et al. Association of neighborhood-level disadvantage with cerebral and hippocampal volume. JAMA Neurol. 2020; 77(4): 451-460.

[40]

Tooley UA, Mackey AP, Ciric R, et al. Associations between neighborhood SES and functional brain network development. Cereb Cortex. 2020; 30(1): 1-19.

[41]

Michael C, Tillem S, Sripada CS, Burt SA, Klump KL, Hyde LW. Neighborhood poverty during childhood prospectively predicts adolescent functional brain network architecture. Dev Cogn Neurosci. 2023; 64: 101316.

[42]

Prado P, Birba A, Cruzat J, et al. Dementia ConnEEGtome: towards multicentric harmonization of EEG connectivity in neurodegeneration. Int J Psychophysiol. 2022; 172: 24-38.

[43]

Gonzalez-Gomez R, Legaz A, Moguilner S, et al. Educational disparities in brain health and dementia across Latin America and the United States. Alzheimers Dement. 2024.

[44]

Forum WE. The Global Gender Gap Report 2023. 2023. Accessed April 8, 2024. https://www.weforum.org/reports/global-gender-gap-report-2023

[45]

Cesnaite E, Steinfath P, Jamshidi Idaji M, et al. Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age. Neuroimage. 2023; 268: 119810.

[46]

Stacey JE, Crook-Rumsey M, Sumich A, et al. Age differences in resting state EEG and their relation to eye movements and cognitive performance. Neuropsychologia. 2021; 157: 107887.

[47]

Gaal ZA, Boha R, Stam CJ, Molnar M. Age-dependent features of EEG-reactivity–spectral, complexity, and network characteristics. Neurosci Lett. 2010; 479(1): 79-84.

[48]

Pravitha R, Sreenivasan R, Nampoori VP. Complexity analysis of dense array EEG signal reveals sex difference. Int J Neurosci. 2005; 115(4): 445-460.

[49]

Carrier J, Land S, Buysse DJ, Kupfer DJ, Monk TH. The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old). Psychophysiology. 2001; 38(2): 232-242.

[50]

Miraglia F, Vecchio F, Bramanti P, Rossini PM. Small-worldness characteristics and its gender relation in specific hemispheric networks. Neuroscience. 2015; 310: 1-11.

[51]

Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009; 10(3): 186-198.

[52]

Iinuma Y, Nobukawa S, Mizukami K, et al. Enhanced temporal complexity of EEG signals in older individuals with high cognitive functions. Front Neurosci. 2022; 16: 878495.

[53]

McBride JC, Zhao X, Munro NB, et al. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease. Comput Methods Programs Biomed. 2014; 114(2): 153-163.

[54]

Finnigan S, Robertson IH. Resting EEG theta power correlates with cognitive performance in healthy older adults. Psychophysiology. 2011; 48(8): 1083-1087.

[55]

Gicas KM, Jones AA, Panenka WJ, et al. Cognitive profiles and associated structural brain networks in a multimorbid sample of marginalized adults. PLoS One. 2019; 14(6): e0218201.

[56]

Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007; 39(2): 175-191.

[57]

Crossley T, Pendakur K. Consumption Inequality. Department of Economics Working Papers, McMaster University; 2002.

[58]

Jiang L, Probst TM. The rich get richer and the poor get poorer: country-and state-level income inequality moderates the job insecurity-burnout relationship. J Appl Psychol. 2017; 102(4): 672-681.

[59]

Oishi S, Kesebir S, Diener E. Income inequality and happiness. Psychol Sci. 2011; 22(9): 1095-1100.

[60]

Gini C. Variabilità e mutabilità: contributo allo studio delle distribuzioni e delle relazioni statistiche. [Fasc. I.]. Tipogr. di P. Cuppini; 1912.

[61]

Chen Z, Crawford CAG. The role of geographic scale in testing the income inequality hypothesis as an explanation of health disparities. Soc Sci Med. 2012; 75(6): 1022-1031.

[62]

Jiang L, Probst TM. The rich get richer and the poor get poorer: country-and state-level income inequality moderates the job insecurity-burnout relationship. J Appl Psychol. 2017; 102(4): 672.

[63]

Fujita M, Nagashima K, Takahashi S, Hata A. Inequality within a community at the neighborhood level and the incidence of mood disorders in Japan: a multilevel analysis. Soc Psychiatry Psychiatr Epidemiol. 2019; 54: 1125-1131.

[64]

WorldBank. Poverty and Inequality Platform Methodology Handbook, 2023. Accessed September, 2023. https://datanalytics.worldbank.org/PIP-Methodology/

[65]

Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975; 12(3): 189-198.

[66]

Creavin ST, Wisniewski S, Noel-Storr AH, et al. Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations. Cochrane Database Syst Rev. 2016; 2016(1): CD011145.

[67]

Mukaetova-Ladinska EB, De Lillo C, Arshad Q, Subramaniam HE, Maltby J. Cognitive assessment of dementia: the need for an inclusive design tool. Curr Alzheimer Res. 2022; 19(4): 265-273.

[68]

Cebi M, Babacan G, Oktem Tanor O, Gurvit H. Discrimination ability of the Short Test of Mental Status (STMS) compared to the Mini Mental State Examination (MMSE) in the spectrum of normal cognition, mild cognitive impairment, and probable Alzheimer’s disease dementia: The Turkish standardization study. J Clin Exp Neuropsychol. 2020; 42(5): 450-458.

[69]

Foderaro G, Isella V, Mazzone A, et al. Brand new norms for a good old test: Northern Italy normative study of MiniMental State Examination. Neurol Sci. 2022; 43(5): 3053-3063.

[70]

Kochhann R, Varela JS, Lisboa CSM, Chaves MLF. The Mini Mental State Examination: review of cutoff points adjusted for schooling in a large Southern Brazilian sample. Dement Neuropsychol. 2010; 4(1): 35-41.

[71]

Ballesteros AS, Prado P, Ibanez A, Perez JAM, Moguilner S. A pipeline for large-scale assessments of dementia EEG connectivity across multicentric settings. Preprints; 2023.

[72]

Prado P, Mejia JA, Sainz-Ballesteros A, et al. Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization. Alzheimers Dement (Amst). 2023; 15(3): e12455.

[73]

Hu S, Lai Y, Valdes-Sosa PA, Bringas-Vega ML, Yao D. How do reference montage and electrodes setup affect the measured scalp EEG potentials? J Neural Eng. 2018; 15(2): 026013.

[74]

Hu S, Yao D, Valdes-Sosa PA. Unified Bayesian estimator of EEG reference at infinity: rREST (regularized reference electrode standardization technique). Front Neurosci. 2018; 12: 297.

[75]

Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004; 134(1): 9-21.

[76]

Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. Neuroimage. 2019; 198: 181-197.

[77]

Bigdely-Shamlo N, Kreutz-Delgado K, Kothe C, Makeig S. EyeCatch: data-mining over half a million EEG independent components to construct a fully-automated eye-component detector. Annu Int Conf IEEE Eng Med Biol Soc. 2013; 2013: 5845-5848.

[78]

Kothe CA, Makeig S. BCILAB: a platform for brain-computer interface development. J Neural Eng. 2013; 10(5): 056014.

[79]

Birba A, Santamaria-Garcia H, Prado P, et al. Allostatic-interoceptive overload in frontotemporal dementia. Biol Psychiatry. 2022; 92(1): 54-67.

[80]

Legaz A, Prado P, Moguilner S, et al. Social and non-social working memory in neurodegeneration. Neurobiol Dis. 2023; 183: 106171.

[81]

Melnik A, Legkov P, Izdebski K, et al. Systems, subjects, sessions: to what extent do these factors influence EEG data? Front Hum Neurosci. 2017; 11: 150.

[82]

Prado P, Moguilner S, Mejia JA, et al. Source space connectomics of neurodegeneration: one-metric approach does not fit all. Neurobiol Dis. 2023; 179: 106047.

[83]

Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002; 24(Suppl D): 5-12.

[84]

Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol. 1994; 18(1): 49-65.

[85]

Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, Grave de Peralta R. EEG source imaging. Clin Neurophysiol. 2004; 115(10): 2195-2222.

[86]

Mazziotta J, Toga A, Evans A, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci. 2001; 356(1412): 1293-322.

[87]

Asadzadeh S, Yousefi Rezaii T, Beheshti S, Delpak A, Meshgini S. A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. J Neurosci Methods. 2020; 339: 108740.

[88]

Rolls ET, Joliot M, Tzourio-Mazoyer N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage. 2015; 122: 1-5.

[89]

Cruzat J, Herzog R, Prado P, et al. Temporal irreversibility of large-scale brain dynamics in Alzheimer’s disease. J Neurosci. 2023; 43(9): 1643-1656.

[90]

Herzog R, Rosas FE, Whelan R, et al. Genuine high-order interactions in brain networks and neurodegeneration. Neurobiol Dis. 2022; 175: 105918.

[91]

Buzsáki G. Rhythms of the Brain. Oxford University Press; 2006.

[92]

Donoghue T, Haller M, Peterson EJ, et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci. 2020; 23(12): 1655-1665.

[93]

Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci. 1994; 91(11): 5033-5037.

[94]

Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci. 2022; 56(7): 5047-5069.

[95]

Zappasodi F, Marzetti L, Olejarczyk E, Tecchio F, Pizzella V. Age-related changes in electroencephalographic signal complexity. PLoS One. 2015; 10(11): e0141995.

[96]

Smith AE, Chau A, Greaves D, Keage HAD, Feuerriegel D. Resting EEG power spectra across middle to late life: associations with age, cognition, APOE-varepsilon4 carriage, and cardiometabolic burden. Neurobiol Aging. 2023; 130: 93-102.

[97]

Ouyang G, Hildebrandt A, Schmitz F, Herrmann CS. Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed. Neuroimage. 2020; 205: 116304.

[98]

Pei L, Zhou X, Leung FKS, Ouyang G. Differential associations between scale-free neural dynamics and different levels of cognitive ability. Psychophysiology. 2023; 60(6): e14259.

[99]

Trondle M, Popov T, Pedroni A, Pfeiffer C, Baranczuk-Turska Z, Langer N. Decomposing age effects in EEG alpha power. Cortex. 2023; 161: 116-144.

[100]

Hill AT, Clark GM, Bigelow FJ, Lum JAG, Enticott PG. Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Dev Cogn Neurosci. 2022; 54: 101076.

[101]

Vecchio F, Miraglia F, Alu F, et al. Human brain networks in physiological and pathological aging: reproducibility of electroencephalogram graph theoretical analysis in cortical connectivity. Brain Connect. 2022; 12(1): 41-51.

[102]

Onoda K, Yamaguchi S. Small-worldness and modularity of the resting-state functional brain network decrease with aging. Neurosci Lett. 2013; 556: 104-8.

[103]

Burns T, Rajan R. Combining complexity measures of EEG data: multiplying measures reveal previously hidden information. F1000Res. 2015; 4: 137.

[104]

Sun J, Wang B, Niu Y, et al. Complexity analysis of EEG, MEG, and fMRI in mild cognitive impairment and Alzheimer’s disease: a review. Entropy (Basel). 2020; 22(2): 239.

[105]

Kalauzi A, Bojić T, Rakić L. Extracting complexity waveforms from one-dimensional signals. Nonlinear Biomed Phys. 2009; 3(1): 8.

[106]

Ouyang G, Li J, Liu X, Li X. Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis. Epilepsy Res. 2013; 104(3): 246-252.

[107]

Singh NC. Measuring the ‘complexity’ of sound. Pramana. 2011; 77(5): 811-816.

[108]

Shew WL, Plenz D. The functional benefits of criticality in the cortex. Neuroscientist. 2013; 19(1): 88-100.

[109]

Sarasso S, Casali AG, Casarotto S, Rosanova M, Sinigaglia C, Massimini M. Consciousness and complexity: a consilience of evidence. Neurosci Conscious. 2021:niab023.

[110]

Boncompte G, Medel V, Cortínez LI, Ossandón T. Brain activity complexity has a nonlinear relation to the level of propofol sedation. Br J Anaesth. 2021; 127(2): 254-263.

[111]

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

[112]

Pathania A, Schreiber M, Miller MW, Euler MJ, Lohse KR. Exploring the reliability and sensitivity of the EEG power spectrum as a biomarker. Int J Psychophysiol. 2021; 160: 18-27.

[113]

Martinez-Canada P, Perez-Valero E, Minguillon J, Pelayo F, Lopez-Gordo MA, 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 (Amst). 2023; 15(3): e12477.

[114]

van Nifterick AM, Mulder D, Duineveld DJ, et al. Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer’s disease patients. Sci Rep. 2023; 13(1): 7419.

[115]

Babiloni C, Barry RJ, Basar E, et al. International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: applications in clinical research studies. Clin Neurophysiol. 2020; 131(1): 285-307.

[116]

Aoki Y, Kazui H, Pascal-Marqui RD, et al. EEG resting-state networks in dementia with Lewy bodies associated with clinical symptoms. Neuropsychobiology. 2019; 77(4): 206-218.

[117]

Lee HY, Jung KI, Yoo WK, Ohn SH. Global synchronization index as an indicator for tracking cognitive function changes in a traumatic brain injury patient: a case report. Ann Rehabil Med. 2019; 43(1): 106-110.

[118]

Aoki Y, Ishii R, Pascual-Marqui RD, et al. Detection of EEG-resting state independent networks by eLORETA-ICA method. Front Hum Neurosci. 2015; 9: 31.

[119]

Aoki Y, Kazui H, Pascual-Marqui RD, et al. EEG resting-state networks responsible for gait disturbance features in idiopathic normal pressure hydrocephalus. Clin EEG Neurosci. 2019; 50(3): 210-218.

[120]

Ouyang CS, Chiang CT, Yang RC, Wu RC, Lin LC. Quantitative electroencephalogram analysis of frontal cortex functional changes in patients with migraine. Kaohsiung J Med Sci. 2020; 36(7): 543-551.

[121]

Krause D, Folkerts M, Karch S, et al. Prediction of treatment outcome in patients with obsessive-compulsive disorder with low-resolution brain electromagnetic tomography: a prospective EEG study. Front Psychol. 2015; 6: 1993.

[122]

Ince RA, Giordano BL, Kayser C, Rousselet GA, Gross J, Schyns PG. A statistical framework for neuroimaging data analysis based on mutual information estimated via a Gaussian copula. Hum Brain Mapp. 2017; 38(3): 1541-1573.

[123]

Martínez-Cañada P, Perez-Valero E, Minguillon J, Pelayo F, López-Gordo MA, 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 (Amst). 2023; 15(3): e12477.

[124]

Li X, Ouyang G, Richards DA. Predictability analysis of absence seizures with permutation entropy. Epilepsy Res. 2007; 77(1): 70-74.

[125]

Wang R, Wang J, Yu H, Wei X, Yang C, Deng B. Power spectral density and coherence analysis of Alzheimer’s EEG. Cogn Neurodyn. 2015; 9(3): 291-304.

[126]

Cover TM, Thomas JA, editors. Entropy, relative entropy and mutual information. In Elements of Information Theory. 2nd ed.. Wiley; 2005: 13-55.

[127]

Hernandez H, Baez S, Medel V, et al. Brain health in diverse settings: how age, demographics and cognition shape brain function. Neuroimage. 2024; 295(1095-9572 (Electronic)).

[128]

Jeong HT, Youn YC, Sung HH, Kim SY. Power spectral changes of quantitative EEG in the subjective cognitive decline: comparison of community normal control groups. Neuropsychiatr Dis Treat. 2021; 17: 2783-2790.

[129]

Moretti DV, Babiloni C, Binetti G, et al. Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin Neurophysiol. 2004; 115(2): 299-308.

[130]

Manly BFJ. Randomization, Bootstrap and Monte Carlo Methods in Biology, 2nd ed. Taylor & Francis; 1997.

[131]

Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999; 29(2): 169-195.

[132]

Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ. A practical guide to calculating Cohen’s f(2), a measure of local effect size, from PROC MIXED. Front Psychol. 2012; 3: 111.

[133]

Cole JH, Poudel RPK, Tsagkrasoulis D, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017; 163: 115-124.

[134]

Smith PF, Ganesh S, Liu P. A comparison of random forest regression and multiple linear regression for prediction in neuroscience. J Neurosci Methods. 2013; 220(1): 85-91.

[135]

Muller AC, Guido S. Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Incorporated; 2018.

[136]

World Bank. World Development Indicators. 2023. Accessed 19 November, 2024. https://databank.worldbank.org/source/world-development-indicators

[137]

Zhao L, Zhang Y, Yu X, et al. Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective. Physiol Meas. 2023; 44(3).

[138]

Bigdely-Shamlo N, Mullen T, Kothe C, Su K-M, Robbins KA. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Methods. Frontiers in Neuroinformatics. 2015; 9.

[139]

Daly I, Pichiorri F, Faller J, et al. What does clean EEG look like? Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference. 2012; 2012: 3963-3966.

[140]

Musaeus CS, Nielsen MS, Osterbye NN, Hogh P. Decreased parietal beta power as a sign of disease progression in patients with mild cognitive impairment. J Alzheimers Dis. 2018; 65(2): 475-487.

[141]

Ponomareva NV, Korovaitseva GI, Rogaev EI. EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiol Aging. 2008; 29(6): 819-827.

[142]

Basar E, Guntekin B. A short review of alpha activity in cognitive processes and in cognitive impairment. Int J Psychophysiol. 2012; 86(1): 25-38.

[143]

Lejko N, Larabi DI, Herrmann CS, Aleman A, Curcic-Blake B. Alpha power and functional connectivity in cognitive decline: A systematic review and meta-analysis. J Alzheimers Dis. 2020; 78(3): 1047-1088.

[144]

MacDonald HJ, Brittain JS, Spitzer B, Hanslmayr S, Jenkinson N. Memory deficits in Parkinson’s disease are associated with reduced beta power modulation. Brain Commun. 2019; 1(1): fcz040.

[145]

Pozar R, Kero K, Martin T, Giordani B, Kavcic V. Task aftereffect reorganization of resting state functional brain networks in healthy aging and mild cognitive impairment. Front Aging Neurosci. 2022; 14: 1061254.

[146]

Mijalkov M, Volpe G, Pereira JB. directed brain connectivity identifies widespread functional network abnormalities in Parkinson’s disease. Cereb Cortex. 2022; 32(3): 593-607.

[147]

Mijalkov M, Vereb D, Canal-Garcia A, et al. Nonlinear changes in delayed functional network topology in Alzheimer’s disease: relationship with amyloid and tau pathology. Alzheimers Res Ther. 2023; 15(1): 112.

[148]

Merkin A, Sghirripa S, Graetz L, et al. Do age-related differences in aperiodic neural activity explain differences in resting EEG alpha? Neurobiol Aging. 2023; 121: 78-87.

[149]

Parbat D, Chakraborty M. A novel methodology to study the cognitive load induced EEG complexity changes: chaos, fractal and entropy based approach. Biomed Signal Proces. 2021; 64.

[150]

Lin CT, Nascimben M, King JT, Wang YK. Task-related EEG and HRV entropy factors under different real-world fatigue scenarios. Neurocomputing. 2018; 311: 24-31.

[151]

Voytek B, Kramer MA, Case J, et al. Age-related changes in 1/f neural electrophysiological noise. J Neurosci. 2015; 35(38): 13257-13265.

[152]

Donoghue T, Haller M, Peterson EJ, et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci. 2020; 23(12): 1655-U288.

[153]

Brandes-Aitken A, Pini N, Weatherhead M, Brito NH. Maternal hair cortisol predicts periodic and aperiodic infant frontal EEG activity longitudinally across infancy. Dev Psychobiol. 2023; 65(5): e22393.

[154]

Zhang C, Stock AK, Muckschel M, Hommel B, Beste C. Aperiodic neural activity reflects metacontrol. Cereb Cortex. 2023; 33(12): 7941-7951.

[155]

Waschke L, Donoghue T, Fiedler L, et al. Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent. eLife. 2021; 10.

[156]

Maestu F, de Haan W, Busche MA, DeFelipe J. Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology. Ageing Res Rev. 2021; 69: 101372.

[157]

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

[158]

van Nifterick AM, Gouw AA, van Kesteren RE, Scheltens P, Stam CJ, de Haan W. A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing. Alzheimers Res Ther. 2022; 14(1): 101.

[159]

Gaubert S, Raimondo F, Houot M, et al. EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease. Brain. 2019; 142(7): 2096-2112.

[160]

Crist AM, Hinkle KM, Wang X, et al. Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease. Nat Commun. 2021; 12(1): 2311.

[161]

Mu Y, Gage FH. Adult hippocampal neurogenesis and its role in Alzheimer’s disease. Mol Neurodegener. 2011; 6: 85.

[162]

Noureddini M, Bagheri-Mohammadi S. Adult hippocampal neurogenesis and Alzheimer’s disease: novel application of mesenchymal stem cells and their role in hippocampal neurogenesis. Int J Mol Cell Med. 2021; 10(1): 1-10.

[163]

Terreros-Roncal J, Moreno-Jimenez EP, Flor-Garcia M, et al. Impact of neurodegenerative diseases on human adult hippocampal neurogenesis. Science. 2021; 374(6571): 1106-1113.

[164]

Anokhin AP, Birbaumer N, Lutzenberger W, Nikolaev A, Vogel F. Age increases brain complexity. Electroencephalogr Clin Neurophysiol. 1996; 99(1): 63-68.

[165]

Javaid H, Kumarnsit E, Chatpun S. Age-related alterations in EEG network connectivity in healthy aging. Brain Sci. 2022; 12(2): 218.

[166]

Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007; 3(2): e17.

[167]

Meunier D, Achard S, Morcom A, Bullmore E. Age-related changes in modular organization of human brain functional networks. Neuroimage. 2009; 44(3): 715-723.

[168]

Li M, Han Y, Aburn MJ, et al. Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain. PLoS Comput Biol. 2019; 15(10): e1006957.

[169]

Stanley ML, Simpson SL, Dagenbach D, Lyday RG, Burdette JH, Laurienti PJ. Changes in brain network efficiency and working memory performance in aging. PLoS One. 2015; 10(4): e0123950.

[170]

Yu M, Sporns O, Saykin AJ. Author correction: the human connectome in Alzheimer disease -relationship to biomarkers and genetics. Nat Rev Neurol. 2021; 17(9): 592.

[171]

Rempe MP, Ott LR, Picci G, et al. Spontaneous cortical dynamics from the first years to the golden years. Proc Natl Acad Sci USA. 2023; 120(4): e2212776120.

[172]

Tian L, Wang J, Yan C, He Y. Hemisphere-and gender-related differences in small-world brain networks: a resting-state functional MRI study. Neuroimage. 2011; 54(1): 191-202.

[173]

da Cruz JR, Favrod O, Roinishvili M, et al. EEG microstates are a candidate endophenotype for schizophrenia. Nat Commun. 2020; 11(1): 3089.

[174]

Tan Y, Chen J, Liao W, Qian Z. Brain function network and young adult smokers: a graph theory analysis study. Front Psychiatry. 2019; 10: 590.

[175]

Raz N, Lindenberger U, Rodrigue KM, et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex. 2005; 15(11): 1676-1689.

[176]

Rehkopf DH, Haughton LT, Chen JT, Waterman PD, Subramanian SV, Krieger N. Monitoring socioeconomic disparities in death: comparing individual-level education and area-based socioeconomic measures. Am J Public Health. 2006; 96(12): 2135-2138.

[177]

Alladi S, Hachinski V. World dementia: one approach does not fit all. Neurology. 2018; 91(6): 264-270.

[178]

Kaarsen N. Cross-country differences in the quality of schooling. J Dev Econ. 2014; 107: 215-224.

[179]

Babulal GM, Quiroz YT, Albensi BC, et al. Perspectives on ethnic and racial disparities in Alzheimer’s disease and related dementias: Update and areas of immediate need. Alzheimers Dement. 2019; 15(2): 292-312.

[180]

De Felice FG, Gonçalves RA, Ferreira ST. Impaired insulin signalling and allostatic load in Alzheimer disease. Nat Rev Neurosci. 2022; 23(4): 215-230.

[181]

Hekmatpour P, Leslie CM. Ecologically unequal exchange and disparate death rates attributable to air pollution: a comparative study of 169 countries from 1991 to 2017. Environ Res. 2022; 212(Pt A):113161.

[182]

Farah MJ. Socioeconomic status and the brain: prospects for neuroscience-informed policy. Nat Rev Neurosci. 2018; 19(7): 428-438.

[183]

Rossini PM, Di Iorio R, Vecchio F, et al. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophysiol. 2020; 131(6): 1287-1310.

[184]

Parra MA. Barriers to effective memory assessments for Alzheimer’s disease. J Alzheimers Dis. 2022; 90(3): 981-988.

[185]

Osberg L. On the limitations of some current usages of the Gini Index. Review Income Wealth. 2017; 63(3): 574-584.

[186]

Peterson RL, Carvajal SC, McGuire LC, Fain MJ, Bell ML. State inequality, socioeconomic position and subjective cognitive decline in the United States. SSM-Population Health. 2019; 7: 100357.

[187]

Ribeiro FS, Crivelli L, Leist AK. Gender inequalities as contributors to dementia in Latin America and the Caribbean: what factors are missing from research? The Lancet Healthy Longevity. 2023; 4(6): e284-e291.

[188]

Shi L, Zhu Q, Wang Y, et al. Incident dementia and long-term exposure to constituents of fine particle air pollution: a national cohort study in the United States. Proc Natl Acad Sci. 2023; 120(1): e2211282119.

[189]

O’Donovan G, Petermann-Rocha F, Ferrari G, et al. Associations of the ’weekend warrior’physical activity pattern with all-cause, cardiovascular disease and cancer mortality: the Mexico City Prospective Study. Br J Sports Med. 2024; 58: 359-365.

[190]

Vega-Salas MJ, Caro P, Johnson L, Papadaki A. Socio-economic inequalities in dietary intake in Chile: a systematic review. Public Health Nutr. 2022; 25(7): 1819-1834.

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