Aperiodic component of EEG power spectrum and cognitive performance are modulated by education in aging.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 07 2024
02 07 2024
Historique:
received:
30
11
2023
accepted:
26
06
2024
medline:
3
7
2024
pubmed:
3
7
2024
entrez:
2
7
2024
Statut:
epublish
Résumé
Recent studies have shown a growing interest in the so-called "aperiodic" component of the EEG power spectrum, which describes the overall trend of the whole spectrum with a linear or exponential function. In the field of brain aging, this aperiodic component is associated both with age-related changes and performance on cognitive tasks. This study aims to elucidate the potential role of education in moderating the relationship between resting-state EEG features (including aperiodic component) and cognitive performance in aging. N = 179 healthy participants of the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) dataset were divided into three groups based on age and education. Older adults exhibited lower exponent, offset (i.e. measures of aperiodic component), and Individual Alpha Peak Frequency (IAPF) as compared to younger adults. Moreover, visual attention and working memory were differently associated with the aperiodic component depending on education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, while an opposite trend was found in those with low education. While further investigation is needed, this study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.
Identifiants
pubmed: 38956186
doi: 10.1038/s41598-024-66049-2
pii: 10.1038/s41598-024-66049-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
15111Informations de copyright
© 2024. The Author(s).
Références
Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U. & Bäckman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305 (2012).
pubmed: 22542563
doi: 10.1016/j.tics.2012.04.005
Gazzaley, A., Cooney, J. W., Rissman, J. & D’Esposito, M. Top-down suppression deficit underlies working memory impairment in normal aging. Nat. Neurosci. 8, 1298–1300 (2005).
pubmed: 16158065
doi: 10.1038/nn1543
Buckner, R. L. Memory and executive function in aging and AD. Neuron 44, 195–208 (2004).
pubmed: 15450170
doi: 10.1016/j.neuron.2004.09.006
Salthouse, T. A. Selective review of cognitive aging. J. Int. Neuropsychol. Soc. JINS 16, 754–760 (2010).
pubmed: 20673381
doi: 10.1017/S1355617710000706
Habes, M. et al. White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain J. Neurol. 139, 1164–1179 (2016).
doi: 10.1093/brain/aww008
Taki, Y. et al. Correlations among brain gray matter volumes, age, gender, and hemisphere in healthy individuals. PloS One 6, e22734 (2011).
pubmed: 21818377
pmcid: 3144937
doi: 10.1371/journal.pone.0022734
Camandola, S. & Mattson, M. P. Brain metabolism in health, aging, and neurodegeneration. EMBO J. 36, 1474–1492 (2017).
pubmed: 28438892
pmcid: 5452017
doi: 10.15252/embj.201695810
Rossini, P. M., Rossi, S., Babiloni, C. & Polich, J. Clinical neurophysiology of aging brain: From normal aging to neurodegeneration. Prog. Neurobiol. 83, 375–400 (2007).
pubmed: 17870229
doi: 10.1016/j.pneurobio.2007.07.010
Podell, J. E. et al. Neurophysiological correlates of age-related changes in working memory updating. NeuroImage 62, 2151–2160 (2012).
pubmed: 22659476
doi: 10.1016/j.neuroimage.2012.05.066
Babiloni, C. et al. Sources of cortical rhythms in adults during physiological aging: A multicentric EEG study. Hum. Brain Mapp. 27, 162–172 (2006).
pubmed: 16108018
doi: 10.1002/hbm.20175
Michels, L. et al. Developmental changes of functional and directed resting-state connectivities associated with neuronal oscillations in EEG. NeuroImage 81, 231–242 (2013).
pubmed: 23644004
doi: 10.1016/j.neuroimage.2013.04.030
Scally, B., Burke, M. R., Bunce, D. & Delvenne, J.-F. Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging. Neurobiol. Aging 71, 149–155 (2018).
pubmed: 30144647
doi: 10.1016/j.neurobiolaging.2018.07.004
Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res. Brain Res. Rev. 29, 169–195 (1999).
pubmed: 10209231
doi: 10.1016/S0165-0173(98)00056-3
Sghirripa, S. et al. The role of alpha power in the suppression of anticipated distractors during verbal working memory. Preprint https://doi.org/10.1101/2020.07.16.207738 (2020).
Donoghue, T. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23, 1655–1665 (2020).
pubmed: 33230329
pmcid: 8106550
doi: 10.1038/s41593-020-00744-x
Schaworonkow, N. & Voytek, B. Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life. Dev. Cogn. Neurosci. 47, 100895 (2021).
pubmed: 33316695
doi: 10.1016/j.dcn.2020.100895
Voytek, B. et al. Age-related changes in 1/f neural electrophysiological noise. J. Neurosci. Off. J. Soc. Neurosci. 35, 13257–13265 (2015).
doi: 10.1523/JNEUROSCI.2332-14.2015
Tran, T. T., Rolle, C. E., Gazzaley, A. & Voytek, B. Linked sources of neural noise contribute to age-related cognitive decline. J. Cogn. Neurosci. 32, 1813–1822 (2020).
pubmed: 32427069
pmcid: 7474516
doi: 10.1162/jocn_a_01584
Waschke, L., Wöstmann, M. & Obleser, J. States and traits of neural irregularity in the age-varying human brain. Sci. Rep. 7, 17381 (2017).
pubmed: 29234128
pmcid: 5727296
doi: 10.1038/s41598-017-17766-4
Thuwal, K., Banerjee, A. & Roy, D. Aperiodic and periodic components of ongoing oscillatory brain dynamics link distinct functional aspects of cognition across adult lifespan. eNeuro 8, ENEURO.0224-21.2021 (2021).
pubmed: 34544762
pmcid: 8547598
doi: 10.1523/ENEURO.0224-21.2021
Pathania, A., Schreiber, M., Miller, M. W., Euler, M. J. & Lohse, K. R. Exploring the reliability and sensitivity of the EEG power spectrum as a biomarker. Int. J. Psychophysiol. 160, 18–27 (2021).
pubmed: 33340559
doi: 10.1016/j.ijpsycho.2020.12.002
Lövdén, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U. & Tucker-Drob, E. M. Education and cognitive functioning across the life span. Psychol. Sci. Public Interest J. Am. Psychol. Soc. 21, 6–41 (2020).
Montemurro, S., Mondini, S. & Arcara, G. Heterogeneity of effects of cognitive reserve on performance in probable Alzheimer’s disease and in subjective cognitive decline. Neuropsychology 35, 876–888 (2021).
pubmed: 34553970
doi: 10.1037/neu0000770
Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. JINS 8, 448–460 (2002).
pubmed: 11939702
doi: 10.1017/S1355617702813248
Stern, Y. et al. A framework for concepts of reserve and resilience in aging. Neurobiol. Aging 124, 100–103 (2023).
pubmed: 36653245
doi: 10.1016/j.neurobiolaging.2022.10.015
Lojo-Seoane, C., Facal, D., Guàrdia-Olmos, J., Pereiro, A. X. & Juncos-Rabadán, O. Effects of cognitive reserve on cognitive performance in a follow-up study in older adults with subjective cognitive complaints. The Role of Working Memory. Front. Aging Neurosci. 10, 189 (2018).
pubmed: 29997497
pmcid: 6028562
doi: 10.3389/fnagi.2018.00189
Mondini, S., Pucci, V., Montemurro, S. & Rumiati, R. I. Protective factors for subjective cognitive decline individuals: Trajectories and changes in a longitudinal study with Italian elderly. Eur. J. Neurol. 29, 691–697 (2022).
pubmed: 34775667
doi: 10.1111/ene.15183
Stern, Y. et al. Influence of education and occupation on the incidence of Alzheimer’s disease. JAMA 271, 1004–1010 (1994).
pubmed: 8139057
doi: 10.1001/jama.1994.03510370056032
Cesnaite, E. et al. Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age. NeuroImage 268, 119810 (2023).
pubmed: 36587708
doi: 10.1016/j.neuroimage.2022.119810
Harada, C. N., Natelson Love, M. C. & Triebel, K. L. Normal cognitive aging. Clin. Geriatr. Med. 29, 737–752 (2013).
pubmed: 24094294
pmcid: 4015335
doi: 10.1016/j.cger.2013.07.002
Dustman, R. E., Shearer, D. E. & Emmerson, R. Y. EEG and event-related potentials in normal aging. Prog. Neurobiol. 41, 369–401 (1993).
pubmed: 8210412
doi: 10.1016/0301-0082(93)90005-D
Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A. G. & Enticott, P. G. Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Dev. Cogn. Neurosci. 54, 101076 (2022).
pubmed: 35085871
pmcid: 8800045
doi: 10.1016/j.dcn.2022.101076
Knyazeva, M. G., Barzegaran, E., Vildavski, V. Y. & Demonet, J.-F. Aging of human alpha rhythm. Neurobiol. Aging 69, 261–273 (2018).
pubmed: 29920435
doi: 10.1016/j.neurobiolaging.2018.05.018
Mizukami, K. & Katada, A. EEG frequency characteristics in healthy advanced elderly. J. Psychophysiol. 32, 131–139 (2018).
doi: 10.1027/0269-8803/a000190
Kumral, D. et al. Relationship between regional white matter hyperintensities and alpha oscillations in older adults. Neurobiol. Aging 112, 1–11 (2022).
pubmed: 35007997
doi: 10.1016/j.neurobiolaging.2021.10.006
Grandy, T. H. et al. Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology 50, 570–582 (2013).
pubmed: 23551082
doi: 10.1111/psyp.12043
Colombo, M. A. et al. The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine. NeuroImage 189, 631–644 (2019).
pubmed: 30639334
doi: 10.1016/j.neuroimage.2019.01.024
Waschke, L. et al. Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent. eLife 10, e70068 (2021).
pubmed: 34672259
pmcid: 8585481
doi: 10.7554/eLife.70068
Lendner, J. D. et al. An electrophysiological marker of arousal level in humans. eLife 9, e55092 (2020).
pubmed: 32720644
pmcid: 7394547
doi: 10.7554/eLife.55092
Miniussi, C., Harris, J. A. & Ruzzoli, M. Modelling non-invasive brain stimulation in cognitive neuroscience. Neurosci. Biobehav. Rev. 37, 1702–1712 (2013).
pubmed: 23827785
doi: 10.1016/j.neubiorev.2013.06.014
Zacharopoulos, G. et al. Predicting learning and achievement using GABA and glutamate concentrations in human development. PLoS Biol. 19, e3001325 (2021).
pubmed: 34292934
pmcid: 8297926
doi: 10.1371/journal.pbio.3001325
Ouyang, G., Hildebrandt, A., Schmitz, F. & Herrmann, C. S. Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed. NeuroImage 205, 116304 (2020).
pubmed: 31654760
doi: 10.1016/j.neuroimage.2019.116304
Babayan, A. et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci. Data 6, 180308 (2019).
pubmed: 30747911
pmcid: 6371893
doi: 10.1038/sdata.2018.308
Khalilian, M. et al. Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study. Aging Brain 5, 100105 (2024).
pubmed: 38273866
pmcid: 10809105
doi: 10.1016/j.nbas.2023.100105
Ansado, J. et al. Coping with task demand in aging using neural compensation and neural reserve triggers primarily intra-hemispheric-based neurofunctional reorganization. Neurosci. Res. 75, 295–304 (2013).
pubmed: 23453977
doi: 10.1016/j.neures.2013.01.012
Montemurro, S. et al. Education differentiates cognitive performance and resting state fMRI connectivity in healthy aging. Front. Aging Neurosci. 15, 1168 (2023).
doi: 10.3389/fnagi.2023.1168576
Sánchez-Izquierdo, M. & Fernández-Ballesteros, R. Cognition in healthy aging. Int. J. Environ. Res. Public. Health 18, 962 (2021).
pubmed: 33499254
pmcid: 7908458
doi: 10.3390/ijerph18030962
Zimmermann, P. & Fimm, V. Testbatterie zur Aufmerksamkeitsprüfung (TAP) (Psytest, 2012).
Reitan, R. M. Trail Making Test: Manual for Administration and Scoring (Reitan Neuropsychology Laboratory, 1992).
Niemann, H., Sturm, W., Thöne-Otto, A. I. T. & Willmes, K. CVLT California Verbal Learning Test. German Adaptation. Manual. (2008).
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: A user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716 (2011).
pubmed: 21584256
pmcid: 3090754
doi: 10.1155/2011/879716
Pascual-Marqui, R. D. Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl D), 5–12 (2002).
pubmed: 12575463
Merkin, A. et al. Age differences in aperiodic neural activity measured with resting EEG. Preprint https://doi.org/10.1101/2021.08.31.458328 (2021).
Tröndle, M. et al. Decomposing age effects in EEG alpha power. Cortex 161, 116–144 (2023).
pubmed: 36933455
doi: 10.1016/j.cortex.2023.02.002
Iemi, L. et al. Multiple mechanisms link prestimulus neural oscillations to sensory responses. eLife 8, e43620 (2019).
pubmed: 31188126
pmcid: 6561703
doi: 10.7554/eLife.43620
van Nifterick, A. M. et al. Resting-state oscillations reveal disturbed excitation–inhibition ratio in Alzheimer’s disease patients. Sci. Rep. 13, 7419 (2023).
pubmed: 37150756
pmcid: 10164744
doi: 10.1038/s41598-023-33973-8
Katyal, S., He, S., He, B. & Engel, S. A. Frequency of alpha oscillation predicts individual differences in perceptual stability during binocular rivalry. Hum. Brain Mapp. 40, 2422–2433 (2019).
pubmed: 30702190
pmcid: 6865672
doi: 10.1002/hbm.24533
R Core Team. R: A Language and Environment for Statistical Computing (2022).