Abstract
In this chapter, scalp electrophysiological measurements using electroencephalographs or EEGs are examined in light of new developments in complex systems theory. At the most fundamental level, brain function is electrical. The neural network that comprises the brain and peripheral nervous system, along with all the specialized cellular structures for propagating electrical impulses, is designed to support exquisitely fine control over the electrical patterns that determine all thought and behavior. It is not an exaggeration to say that the most fundamental medium of the mind is an electric field. Measurements of brain electrical activity may thus in principle contain information about cognitive phenotypes, if recurring patterns can be found that reliably correlate with them. The brain meets the mathematical definition of a complex dynamical system and EEG measurements are time series or signals produced by local clusters of neurons in this system. This chapter presents a methodology for discovering patterns in the complex systems parameters that can be derived from EEG measurements that is based on machine learning or pattern recognition algorithms. Without attempting to describe these complex features in neurobiological terms, machine learning algorithms can be used to find significant mappings from these measurable features to cognitive phenotypes, thus creating neuropsychiatric biomarkers that may be clinically useful. Experimental results from research to find early biomarkers for autism illustrate the approach.
Keywords
- Electroencephalograph (EEG)
- Complex dynamical systems
- Nonlinear dynamical systems
- Neuronal spiking rate
- Phase trajectories
- Recurrence plot analysis
- Recurrence quantitative analysis
- Autism spectrum disorder
- Epilepsy
- Data-driven discovery
- Global mental health
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References
Acharya UR, Sree SV, Chattopadhyay S et al (2011) Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int J Neural Syst 21(3):199–211
Antonsson EK, Cagan J (eds) (2001) Formal engineering design synthesis. Cambridge University Press, Cambridge
Bakare MO, Munir KM, Bello-Mojeed M (2014) Public health and research funding for childhood neurodevelopmental disorders in Sub-Saharan Africa: a time to balance priorities. Healthc Low-Resour Settings 2:1559
Becker AE, Kleinman A (2012) An agenda for closing resource gaps in global mental health: innovation, capacity building, and partnerships. Harv Rev Psychiatry 20(1):3–5
Bosl WJ, Bates A, DeGregorio G et al (in prep) EEG recurrence plot analysis for early detection of autism spectrum disorder (Manuscript in preparation)
Bosl WJ, Bates A, Fernandez IS et al (in prep) EEG biomarkers for early identification of children with epileptic encephalopathies (Manuscript in preparation)
Bosl WJ, Tager-Flusberg H, Tierney A, Nelson CA (2011) EEG complexity as a biomarker for autism spectrum disorder. BMC Medicine 9:18
Castagli M, Eubank S, Farmer JD et al (1991) State space reconstruction in the presence of noise. Physica D 51:52–98
Charman T, Jones CR, Pickles A et al (2010) Defining the cognitive phenotype of autism. Brain Res 1380:10–21
Collins PY, Patel V, Joestl SS et al (2011) Grand challenges in global mental health. Nature 475(7354):27–30
Daniusis P, Vaitkus P (2008) Kernel regression on matrix patterns. Lith Math J 48(49):191–195
Eckmann J, Kaphorst SO, Ruelle D (1987) Recurrence plots of dynamical systems. Europhys Lett 5:973–977
Fletcher R, Loschen E, Stavrakaki C et al (eds) (2007) A textbook of diagnosis of mental disorders in persons with intellectual disability (DM-ID). NADD Press, Kingston
Hartwell LF, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402:C47–52
Idro R, Newton C, Kiguli S et al (2010) Child neurology practice and neurological disorders in East Africa. J Child Neurol 25(4):518–524
Juarrero A (2010) Complex dynamical systems theory. Available via Cognitive Edge. http://cognitive-edge.com/articles/complex-dynamical-systems-theory/
Kello CT, Rodny J, Warlaumont AS et al (2012) Plasticity, learning, and complexity in spiking networks. Crit Rev Biomed Eng 40(6):501–518
Komalapriya C, Thiel M, Romano MC et al (2008) Reconstruction of a system’s dynamics from short trajectories. Phys Rev E: Stat, Nonlin, Soft Matter Phys 78(6 Pt 2):066217
Lu J, Tapia JC, White OL et al (2009) The interscutularis muscle connectome. PLoS Biol 7(2):e32
Marwan, N (2012) Recurrence plots and cross recurrence plots. http://www.recurrence-plot.tk/
Marwan N, Romano MC, Thiel M et al (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438:237–329
McGregor C (2013) Big data in Neonatal intensive care. Computer 46(6):54–59
Niedermeyer E (2003) The clinical relevance of EEG interpretation. Clin Electroencephalogr 34(3):93–98
Niedermeyer E, Lopes da Silva FH (2005) Electroencephalography: basic principles, clinical applications, and related fields, 5th edn. Lippincott Williams & Wilkins, Philadelphia
NIMH (2008) National institute of mental health strategic plan. National Institutes of Health, Bethesda
Noonan SK, Haist F, Muller RA (2009) Aberrant functional connectivity in autism: evidence from low-frequency BOLD signal fluctuations. Brain Res 1262:48–63
Norman KA, Polyn SM, Detre GJ et al (2006) Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 10(9):424–430
Pascanu R, Jaeger H (2011) A neurodynamical model for working memory. Neural Netw 24(2):199–207
Prince M, Patel V, Saxena S et al (2007) No health without mental health. Lancet 370(9590):859–877
Rigotti M, Barak O, Warden M et al (2013) The importance of mixed selectivity in complex cognitive tasks. Nature 497:585–590
Rong PJ, Fang JL, Wang LP et al (2012) Transcutaneous vagus nerve stimulation for the treatment of depression: a study protocol for a double blinded randomized clinical trial. BMC Complement Altern Med 12:255
Schinkel S, Marwan N, Kurths J (2007) Order patterns recurrence plots in the analysis of ERP data. Cogn Neurodyn 1(4):317–325
Schinkel S, Marwan N, Kurths J (2009) Brain signal analysis based on recurrences. J Physiol Paris 103(6):315–323
Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(5923):81–85
Tager-Flusberg H, Joseph RM (2003) Identifying neurocognitive phenotypes in autism. Philos Trans R Soc Lond B Biol Sci 358(1430):303–314
Trulla LL, Giuliani AL, Zbilut JP, Jr, CLW (1996) Recurrence quantification analysis of the logistic equation with transients. Physics Letters A 267:255–260
Webber CL, Marwan N (eds) (2015) Recurrence quantification analysis. Understanding complex systems. Springer, New York
Webber CL Jr, Zbilut JP (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76(2):965–973
Webber CL, Zbilut JP (2005) Recurrence quantitative analysis of nonlinear dynamical systems. In: Riley MA, Van Orden G (eds) Tutorials in contemporary nonlinear methods for the behavioral sciences. National Science Foundation, Virginia
Yamazaki M, Tucker DM, Terrill M et al (2013) Dense array EEG source estimation in neocortical epilepsy. Front Neurol 4:42
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Bosl, W. (2016). EEG-Derived Neurophenotypes. In: Jagaroo, V., Santangelo, S. (eds) Neurophenotypes. Innovations in Cognitive Neuroscience. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3846-5_14
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DOI: https://doi.org/10.1007/978-1-4614-3846-5_14
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