Abstract
This work explores a feature of brain dynamics, metastability, by which transients are observed in functional brain data. Metastability is a balance between static (stable) and dynamic (unstable) tendencies in electrophysiological brain activity. Furthermore, metastability is a theoretical mechanism underlying the rapid synchronization of cell assemblies that serve as neural substrates for cognitive states, and it has been associated with cognitive flexibility. While much previous research has sought to characterize metastability in the adult human brain, few studies have examined metastability in early development, in part because of the challenges of acquiring adequate, noise free continuous data in young children. To accomplish this endeavor, we studied a new method for characterizing the stability of EEG frequency in early childhood, as inspired by prior approaches for describing cortical phase resets in the scalp EEG of healthy adults. Specifically, we quantified the variance of the rate of change of the signal phase (i.e., frequency) as a proxy for phase resets (signal instability), given that phase resets occur almost simultaneously across large portions of the scalp. We tested our method in a cohort of 39 preschool age children (age =53 ± 13.6 months). We found that our outcome variable of interest, frequency variance, was a promising marker of signal stability, as it increased with the number of phase resets in surrogate (artificial) signals. In our cohort of children, frequency variance decreased cross-sectionally with age (r = −0.47, p = 0.0028). EEG signal stability, as quantified by frequency variance, increases with age in preschool age children. Future studies will relate this biomarker with the development of executive function and cognitive flexibility in children, with the overarching goal of understanding metastability in atypical development.
References
Amari, S.-I., Cichocki, A., & Yang, H.H. (1996). A new learning algorithm for blind source seperation. Adv Neural Informaton Process Syst 757–763.
Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: an explanation of 1/f noise. Physical Review Letters, 59, 381–384.
Bak, P., Tang, C., & Wiesenfeld, K. (1988). Self-organized criticality. Physical Review A, 38, 364–374.
Beggs, J. M., & Plenz, D. (2003). Neuronal avalanches in neocortical circuits. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23, 11167–11177.
Beggs, J. M., & Plenz, D. (2004). Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 24, 5216–5229.
Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129–1159.
Bianchi, S., Stimpson, C. D., Duka, T., Larsen, M. D., Janssen, W. G. M., Collins, Z., et al. (2013). Synaptogenesis and development of pyramidal neuron dendritic morphology in the chimpanzee neocortex resembles humans. Proceedings of the National Academy of Sciences of the United States of America, 110(Suppl 2), 10395–10401.
Bliss, T. V., & Collingridge, G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361, 31–39.
Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine, 9, 18.
Catarino, A., Churches, O., Baron-Cohen, S., Andrade, A., & Ring, H. (2011). Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 122, 2375–2383.
Coffey, D. S. (1998). Self-organization, complexity and chaos: the new biology for medicine. Nature Medicine, 4, 882–885.
Cox, A., Klein, K., Charman, T., Baird, G., Baron-Cohen, S., Swettenham, J., et al. (1999). Autism spectrum disorders at 20 and 42 months of age: stability of clinical and ADI-R diagnosis. Journal of Child Psychology and Psychiatry, 40, 719–732.
Luca C De, Leventer, R. (2010). Developmental trajectories of executive funtions across the lifespan. Exec Funct Front Lobes Lifesp Perspect 23–56.
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21.
Eldridge, J., Lane, A. E., Belkin, M., & Dennis, S. (2014). Robust features for the automatic identification of autism spectrum disorder in children. Journal of Neurodevelopmental Disorders, 6, 12.
Elliot, C. D. (2007). Differential abilitity scales (2nd ed.). San Antonio: Harcourt Assessment.
Fatemi, S. H., Reutiman, T. J., Folsom, T. D., Rooney, R. J., Patel, D. H., & Thuras, P. D. (2010). mRNA and protein levels for GABAAalpha4, alpha5, beta1 and GABABR1 receptors are altered in brains from subjects with autism. Journal of Autism and Developmental Disorders, 40, 743–750.
Fatemi, S. H., Folsom, T. D., Kneeland, R. E., & Liesch, S. B. (2011). Metabotropic glutamate receptor 5 upregulation in children with autism is associated with underexpression of both Fragile X mental retardation protein and GABAA receptor beta 3 in adults with autism. Anatomical Record Hoboken NJ 2007, 294, 1635–1645.
Feinberg, I. (1982). Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? Journal of Psychiatric Research, 17, 319–334.
Fell, J., Röschke, J., & Beckmann, P. (1993). Deterministic chaos and the first positive Lyapunov exponent: a nonlinear analysis of the human electroencephalogram during sleep. Biological Cybernetics, 69, 139–146.
Fountain, C., King, M. D., & Bearman, P. S. (2011). Age of diagnosis for autism: individual and community factors across 10 birth cohorts. Journal of Epidemiology and Community Health, 65, 503–510.
Freeman, W. J. (2003). Evidence from human scalp electroencephalograms of global chaotic itinerancy. Chaos Woodbury N, 13, 1067–1077.
Freeman, W. J. (2004a). Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 115, 2077–2088.
Freeman WJ (2004b). Origin, Structure, and Role of Background EEG Activity. Part 2: Analytic Phase. Clin Neurophysiol 2089–2107.
Freeman, W. J., & Holmes, M. D. (2005). Metastability, instability, and state transition in neocortex. Neural Network: the Official Journal of the International Neural Network Society, 18, 497–504.
Freeman, W. J., & Kozma, R. (2010). Freeman’s mass action. Scholarpedia, 5, 8040.
Freeman, W. J., Burke, B. C., & Holmes, M. D. (2003). Aperidoic phase re-setting in scalp EEG of beta-gamma oscillations by state transitions at alpha-theta rates. Human Brain Mapping, 19, 248–272.
Freeman, W. J., Holmes, M. D., West, G. A., & Vanhatalo, S. (2006). Fine spatiotemporal structure of phase in human intracranial EEG. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 117, 1228–1243.
Friston, K. J. (1996). Theoretical neurobiology and schizophrenia. British Medical Bulletin, 52, 644–655.
Friston, K. J. (2000). The labile brain. I. Neuronal transients and nonlinear coupling. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 355, 215–236.
Frohlich, J., & van Horn, J. D. (2014). Reviewing the ketamine model for schizophrenia. Journal of Psychopharmacology Oxford England, 28, 287–302.
Ghanbari, Y., Bloy, L., Christopher Edgar, J., Blaskey, L., Verma, R., & Roberts, T. P. L. (2013). Joint analysis of band-specific functional connectivity and signal complexity in autism. Journal of Autism and Developmental Disorders. doi:10.1007/s10803-013-1915-7.
Granger, B. (1996). [Synaptogenesis and synaptic pruning: role in triggering schizophrenia]. Presse Médicale Paris France 1983, 25, 1595–1598.
He, B. J. (2014). Scale-free brain activity: past, present, and future. Trends in Cognitive Science. doi:10.1016/j.tics.2014.04.003.
Hebb, D. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
Hertz-Picciotto, I., & Delwiche, L. (2009). The rise in autism and the role of age at diagnosis. Epidemiology (Cambridge, Mass), 20, 84–90.
Hofstadter, D. (1979). Gödel, Escher, Bach: An eternal golden braid. New York: Basic Books.
Hughes, J. R. (1958). Post-tetanic potentiation. Physiological Reviews, 38, 91–113.
Huttenlocher, P. R. (1979). Synaptic density in human frontal cortex - developmental changes and effects of aging. Brain Research, 163, 195–205.
Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology, 387, 167–178.
Janjarasjitt, S., Scher, M. S., & Loparo, K. A. (2008). Nonlinear dynamical analysis of the neonatal EEG time series: the relationship between neurodevelopment and complexity. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 119, 822–836.
Kang, J.-Q., & Barnes, G. (2013). A common susceptibility factor of both autism and epilepsy: functional deficiency of GABA A receptors. Journal of Autism and Developmental Disorders, 43, 68–79.
Kaplan, T., Fingelkurts, A., Fingelkurts, A., Borisov, S. V., & Darkhovsky, B. (2005). Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges. Signal Processing, 85, 2190–2212.
Kehrer, C., Maziashvili, N., Dugladze, T., & Gloveli, T. (2008). Altered excitatory-inhibitory balance in the NMDA-hypofunction model of schizophrenia. Frontiers in Molecular Neuroscience, 1, 6.
Koenig, T., Prichep, L., Lehmann, D., Sosa, P. V., Braeker, E., Kleinlogel, H., et al. (2002). Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. NeuroImage, 16, 41–48.
Lee, T. W., Girolami, M., & Sejnowski, T. J. (1999). Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 11, 417–441.
Letellier, C., & Rössler, O. (2007). Hyperchaos. Scholarpedia, 2, 1936.
Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001). Long-range temporal correlations and scaling behavior in human brain oscillations. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 21, 1370–1377.
Lippé, S., Kovacevic, N., & McIntosh, A. R. (2009). Differential maturation of brain signal complexity in the human auditory and visual system. Frontiers in Human Neuroscience, 3, 48.
Little, M. A., McSharry, P. E., Roberts, S. J., Costello, D. A. E., & Moroz, I. M. (2007). Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomedical Engineering Online, 6, 23.
Lowen, S. B., Cash, S. S., Poo, M., & Teich, M. C. (1997). Quantal neurotransmitter secretion rate exhibits fractal behavior. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 17, 5666–5677.
Manor, B., & Lipsitz, L. A. (2012). Physiologic complexity and aging: implications for physical function and rehabilitation. Progress in Neuropsychopharmacology and Biological Psychiatry. doi:10.1016/j.pnpbp.2012.08.020.
McCauley, J. L., Olson, L. M., Delahanty, R., Amin, T., Nurmi, E. L., Organ, E. L., et al. (2004). A linkage disequilibrium map of the 1-Mb 15q12 GABA(A) receptor subunit cluster and association to autism. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics : the Official Publication of the International Society of Psychiatric Genetics, 131B, 51–59.
McIntosh, A. R., Kovacevic, N., & Itier, R. J. (2008). Increased brain signal variability accompanies lower behavioral variability in development. PLoS Computational Biology, 4, e1000106.
Menold, M. M., Shao, Y., Wolpert, C. M., Donnelly, S. L., Raiford, K. L., Martin, E. R., et al. (2001). Association analysis of chromosome 15 GABAA receptor subunit genes in autistic disorder. Journal of Neurogenetics, 15, 245–259.
Meyer-Lindenberg, A. (1996). The evolution of complexity in human brain development: an EEG study. Electroencephalography and Clinical Neurophysiology, 99, 405–411.
Mullen, E. M. (1995). Mullen scales of early learning: AGS edition. Circle Pines, MN: American Guidance Service.
Olney, J. W., & Farber, N. B. (1997). Discussion of Bogerts’ temporolimbic system theory of paranoid schizophrenia. Schizophrenia Bulletin, 23, 533–536.
Onton, J., Westerfield, M., Townsend, J., & Makeig, S. (2006). Imaging human EEG dynamics using independent component analysis. Neuroscience and Biobehavioral Reviews, 30, 808–822.
Philips, B. (2009). CircStat: a MATLAB toolbox for circular statistics. Journal of Statistical Software, 31, 1–21.
Plenz, D., & Thiagarajan, T. C. (2007). The organizing principles of neuronal avalanches: cell assemblies in the cortex? Trends in Neurosciences, 30, 101–110.
Pritchard, W. S. (1992). The brain in fractal time: 1/f-like power spectrum scaling of the human electroencephalogram. International Journal of Neuroscience, 66, 119–129.
Rabinovich, M. I., Huerta, R., Varona, P., & Afraimovich, V. S. (2008). Transient cognitive dynamics, metastability, and decision making. PLoS Computational Biology, 4, e1000072.
Rössler, O. (1979). An equation for hyperchaos. Physics Letters A, 71, 155–157.
Roux, F., Wibral, M., Singer, W., Aru, J., & Uhlhaas, P. J. (2013). The phase of thalamic alpha activity modulates cortical gamma-band activity: evidence from resting-state MEG recordings. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33, 17827–17835.
Soong, A. C., & Stuart, C. I. (1989). Evidence of chaotic dynamics underlying the human alpha-rhythm electroencephalogram. Biological Cybernetics, 62, 55–62.
Sporns, O. (2011). Networks of the brain. Cambridge, MA: MIT Press.
Stam, C. J. (2005). Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 116, 2266–2301.
Teich, M. C., Heneghan, C., Lowen, S. B., Ozaki, T., & Kaplan, E. (1997). Fractal character of the neural spike train in the visual system of the cat. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 14, 529–546.
Thatcher, R. W., North, D. M., & Biver, C. J. (2008). Intelligence and EEG phase reset: a two compartmental model of phase shift and lock. NeuroImage, 42, 1639–1653.
Thatcher, R. W., North, D. M., & Biver, C. J. (2009a). Self-organized criticality and the development of EEG phase reset. Human Brain Mapping, 30, 553–574.
Thatcher, R. W., North, D. M., Neubrander, J., Biver, C. J., Cutler, S., & Defina, P. (2009b). Autism and EEG phase reset: deficient GABA mediated inhibition in thalamo-cortical circuits. Developmental Neuropsychology, 34, 780–800.
Toib, A., Lyakhov, V., & Marom, S. (1998). Interaction between duration of activity and time course of recovery from slow inactivation in mammalian brain Na + channels. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 18, 1893–1903.
Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282, 1846–1851.
Tsuda, I. (2013). Chaotic itinerancy. Scholarpedia, 8, 4459.
Varela, F. J. (1995). Resonant cell assemblies: a new approach to cognitive functions and neuronal synchrony. Biological Research, 28, 81–95.
Wang, X., Meng, J., Tan, G., & Zou, L. (2010). Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain. Nonlinear Biomedical Physics, 4, 2.
Werner, G. (2007). Metastability, criticality and phase transitions in brain and its models. Biosystems, 90, 496–508.
Acknowledgments
The authors are deeply grateful to all children and parents who volunteered their time to advance our knowledge of typical development. We thank Dr. Mikhail Rabinovich for his comments on theoretical aspects of this work, as well as Dr. Ted Hutman and Dr. Carrie Bearden for their much appreciated feedback on the manuscript. A warm thank you is also extended to Nima Chenari for his kind help producing illustrations and to Christina Shimizu, Andrew Sanders, and Amanda Noroña for their patience and professionalism in assisting with data collection. This work was supported by NIMH K23MH094517-01.
Informed consent
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study
Conflict of interest
Joel Frohlich, Andrei Irimia, and Shafali S. Jeste declare that they have no conflicts of interest.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Frohlich, J., Irimia, A. & Jeste, S.S. Trajectory of frequency stability in typical development. Brain Imaging and Behavior 9, 5–18 (2015). https://doi.org/10.1007/s11682-014-9339-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-014-9339-3