Abstract
The aim of this study was to define the pattern of reduction in absolute power spectral density (PSD) of magnetoencephalography (MEG) signals throughout development. Specifically, we wanted to explore whether the human skull’s high permeability for electromagnetic fields would allow us to question whether the pattern of absolute PSD reduction observed in the human electroencephalogram is due to an increase in the skull’s resistive properties with age. Furthermore, the topography of the MEG signals during maturation was explored, providing additional insights about the areas and brain rhythms related to late maturation in the human brain. To attain these goals, spontaneous MEG activity was recorded from 148 sensors in a sample of 59 subjects divided into three age groups: children/adolescents (7–14 years), young adults (17–20 years) and adults (21–26 years). Statistical testing was carried out by means of an analysis of variance (ANOVA), with “age group” as between-subject factor and “sensor group” as within-subject factor. Additionally, correlations of absolute PSD with age were computed to assess the influence of age on the spectral content of MEG signals. Results showed a broadband PSD decrease in frontal areas, which suggests the late maturation of this region, but also a mild increase in high frequency PSD with age in posterior areas. These findings suggest that the intensity of the neural sources during spontaneous brain activity decreases with age, which may be related to synaptic pruning.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Barnea-Goraly N, Menon V, Eckert M, TammL Bammer R, Karchemskiy A, Dant CC, Reiss AL (2005) White matter development during childhood and adolescence: across-sectional diffusion tensor imaging study. Cereb Cortex 15(12):1848–1854
Barriga-Paulino CI, Flores AB, Gómez CM (2011) Developmental changes in the EEG rhythms of children and young adults analyzed by means of correlational, brain topography and principal component analysis. J Psychophysiol 25(3):143–158
Beauchamp MS, Beurlot MR, Fava E, Nath AR, Parikh NA, Saad ZS, Bortfeld H, Oghalai JS (2011) The developmental trajectory of brain-scalp distance from birth through childhood: implications for functional neuroimaging. PLoS ONE 6(9):e24981
Bruña R, Poza J, Gómez C, García M, Fernández A, Hornero R (2012) Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer’s disease using spectral entropies and statistical complexity measures. J Neural Eng 9(3):036007
Chugani HT (1998) A critical period of brain development: studies of cerebral glucose utilization with PET. Prev Med 27:184–188
Fernández A, Zuluaga F, Abásolo D, Gómez C, Serra A, Méndez MA, Hornero R (2012) Brain oscillatory complexity across the life span. Clin Neurophysiol 123(11):2154–2162
Gómez C, Perez-Macías JM, Poza J, Fernández A, Hornero R (2013) Spectral changes in spontaneous MEG activity acrossthe lifespan. J Neural Eng 10(6):066006
Hämäläinen MS, Sarvas J (1989) Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE Trans Biomed Eng 36(2):165–171
Hämäläinen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography. Theory, instrumentations and applications to nonivasive studies of the working human brain. Rev Mod Phys 65(2):418–419
Hari R (1993) Magnetoencephalography as a tool of clinical neurophysiology. In: Niedermeyer E, Lopes Da Silva F (eds) Electroencephalography, 3rd edn. Williams and Wilkins, Baltimore, pp 1035–1061
Hoekema R, Wieneke GH, Leijten FSS, van Veelen CWM (2003) Measurement of the conductivity of skull, temporarily removed during epilepsy surgery. Brain Topogr 16(1):29–38
Huttenlocher PR, Dabholkar AS (1997) Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 387:167–178
Lew S, Sliva DD, Choe MS, Grant PE, Okada Y, Wolters CH, Hämäläinen MS (2013) Effects of sutures and fontanels on MEG and EEG source analysis in a realistic infant head model. Neuroimage 76:282–293. doi:10.1016/j.neuroimage.2013.03.017
Lewine JD (1990) Neuromagnetic techniques for the noninvasive analysis of brain function. In: Freeman SE, Fukushima E, Greene ER (eds) Noninvasive techniques in biology and medicine. San Francisco Press, San Francisco
Lüchinger R, Michels L, Martin E, Brandeis D (2011) EEG–BOLD correlations during post-adolescent brain maturation. Neuroimage 56(3):1493–1505
Matousek M, Petersen I (1973) Frequency analysis of the EEG in normal children and adolescents. In: Kellaway P, Petersen I (eds) Automation of clinical electroencephalography. Raven, New York, pp 75–102
Pant S, Te T, Tucker A, Sadleir RJ (2011) Conductivity of neonatal piglet skull. Physiol Meas 32(8):1275–1283
Puligheddu M, de Munck JC, Stam CJ, Verbunt J, de Jongh A, van Dijk BW, Marrosu F (2005) Age distribution of MEG spontaneous theta activity in healthy subjects. Brain Topogr 17(3):165–175
Rodríguez-Martínez EI, Barriga-Paulino CI, Zapata MI, Chinchilla C, López- Jiménez AM, Gómez CM (2012) Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period. BMC Neurosci 13:104
Rodríguez-Martínez EI, Barriga-Paulino CI, Rojas-Benjumea MA, Gómez CM (2015) Co-maturation of theta and low-beta rhythms during child development. Brain Topogr 28(2):250–260
Rojas-Benjumea MA, Barriga-Paulino CI, Rodríguez-Martínez EI, Gómez CM (2015) Development of behavioral parameters and ERPs in a novel-target visual detection paradigm in children, adolescents and young adults. Behav Brain Funct 11:22
Saby JN, Marshall PJ (2012) The utility of EEG band power analysisin the study of infancy and early childhood. Dev Neuropsychol 37(3):253–273
Schäfer CB, Morgan BR, Ye AX, Taylor MJ, Doesburg SM (2014) Oscillations, networks, and their development: mEG connectivity changes with age. Hum Brain Mapp 35(10):5249–5261
Segalowitz SJ, Santesso DL, Jetha MK (2010) Electrophysiological changes duringadolescence:a review. Brain Cognit 72(1):86–100
Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, Greenstein D, Clasen L, Evans A, Rapoport JL, Gieddand JN, Wise SP (2008) Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci 28(14):3586–3594
Smith K, Politte D, Reiker G, Nolan TS, Hildebolt C, Mattson C, Tucker D, Prior F, Turovets S, Larson-Prior LJ (2012) Automated measurement of pediatric cranial bone thickness and density from clinical computed tomography. Int Conf IEEE Proc Eng Med Biol Soc 2012:4462–4465. doi:10.1109/EMBC.2012.6346957
Somsen RJM, Klooster BJ, Van der Molen MW, Van Leeuwen HMP, Licht R (1997) Growth spurts in brain maturation during middle childhood as indexedby EEG power spectra. Biol Psychol 44(3):187–209
Taki Y, Hashizume H, Sassa Y, Takeuchi H, Wu K, Asano M et al (2011) Correlation between gray matter density-adjusted brain perfusion and age using brain MR images of 202 healthy children. Hum Brain Mapp 32(11):1973–1985. doi:10.1002/hbm.21163
Whitford TJ, Rennie CJ, Grieve SM, Clark CR, Gordon E, Williams LM (2007) Brainmaturation in adolescence: concurrent changes in neuroanatomy and neurophysiology. Hum Brain Mapp 28(3):228–237
Acknowledgments
This work was supported by the Spanish Ministry of Science and Innovation, Grant number PSI2013-47506-R, funded by the FEDER program of the UE and Junta de Andalucía, Grant number CTS-153.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gómez, C.M., Rodríguez-Martínez, E.I., Fernández, A. et al. Absolute Power Spectral Density Changes in the Magnetoencephalographic Activity During the Transition from Childhood to Adulthood. Brain Topogr 30, 87–97 (2017). https://doi.org/10.1007/s10548-016-0532-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10548-016-0532-0