Program of environmental neuromodulation for the treatment of developmental disorders

The developmental disorders, in any of its facets and diagnoses, such as autism spectrum disorders, Deficit attention disorder with or without hyperactivity, pervasive developmental disorders, generalized language disorders, or development disorders No defined, impact on learning and the way in which children attend to stimuli from the environment.

Usually specialized care programs focus on achieving as much as possible behaviors of self-care in the best of the cases and even sometimes parents receive the comments there is a little to do for their children because they are not able to learn.

Although the subject of learning has been widely studied from different perspectives, and in recent years thanks to technological progress, it has been part of several studies with artificial neural networks, which have a point of gear with the evolution of complex systems. The development of these enabled to understand how nature has provided systems with the ability to adapt to the environment that is the line that allows the survival of the species, which can be key to work with children with developmental disorders, because allows to understand development as a flexible system.

This is nurtured by other studies such as the plasticity and applications of these principles whereas since the beginnings of neurogenesis that are in the early stages of understanding, to idea of the connectome and proteome that allow to understand the modeling environment achieved on cognitive systems.

So the work with children with developmental disorders opens a door to explore possibilities for psych educational treatment, leaving behind the myth that "some children will never be able to learn".

Breaking myths

The widespread belief that a complex nervous system is required to make possible adaptation to the environment either a change that triggers a new reaction in an organism has been left behind and has begun to recognize that chemical networks can evolve in simple systems that allow analyzing the ability to operate routine by a body. Examples of this are the studies on chemical samples created in silico or maybe  the study of prions, which have opened the door to understanding the mechanisms of adaptation and learning at the protein level.

They are a physiological lesson and this is simple: adaptation to the environment is not a mechanism created from a flexible brain learning, but a created evolutionary inheritance so that species develop and survive because that possibly this same adaptive response was inherited giving rise to other processes more complex as neural networks that are currently studied as a whole with the name of connectome.

The connectome is a map of the neural connections, and which seeks to describe the brain structure, as well as the genome is more than just a juxtaposition of genes, the set of neural connections is much more than the sum of its individual components.

The connectome contains millions of times more connections than the letters of the genome, but also every who is creating specific connections from interactions with the environment, so Sebastian Seung explains that everyone is our connectome, which is formed based on 4 principles: reweighting which means changes in the strength of the synapses; reconnection , which is the creation and elimination of synapses; rewired which is the creation and elimination of neuronal branches and feedback which is the creation and elimination of neurons.

To this you have join the thousands of years of evolution in which these processes are developing, because as explains Dehaene (2011) the brain represents the response of the slow evolution of species governed by the principle of the same natural selection that has been perfected over the years optimizing brain the way it handles the huge flow of sensory information received to suit the reactions of the organism to a competitive and sometimes hostile environment.

This adaptation to the environment is the key to the survival of the species; however, there are still changes that gestate from the available elements. 

In a world made for man, wirings and neural functions would surely be regulated by processes in perfect order and functionality from desirable patterns, but nature is still experimenting with that has resources. 

In this sense the natural systems continue to experience and making adaptations in search of improvements, one of the first attempts to explain this on a large scale was the so-called Baldwin effect, also known as the ontogenic evolution which is a theory of the probable evolutionary process of learning, which was published by first time in 1896. The theory proposes a mechanism for learning ability in general, selected descendants of a group, can have greater capacity to learn new skills rather than simply  the abilities granted by the genetic code which is relatively rigid.

However, I must say the ontogenetic evolution theory has received diverse criticisms, in part because it is very difficult to monitor environmental changes in the superior species. But I prefer to see the biological entities from a different perspective, so under the assumption that the brain can adapt and learn from past experience, as the specific evolution not only inherited behavior but that adds goals inherited which are used to guide the learning under the orders of a genetic code that has two components in the species. 

In this respect is that the evolution of neural networks contains information not only in genetic terms, but also a collection of behaviors developed by the ancestors can be understood as the culture.

It is then that culture has a major role since the adaptations in the environment are not always determined by closed code and therefore cannot be stronger than those set by the selection (including changes in the social environment). 

This idea has generated several lines of research, and one of them is precisely the assisted environmental neuromodulation.

The process of Neuromodulation

The process of neuromodulation is not new, arises from the observation that different kinds of neurotransmitters in the nervous system regulate different groups of neurons. In contrast to direct synaptic transmission in which a process is required pre synaptic and other synaptic, the neuromodulatory transmitters secreted by a small group of neurons diffuse through large areas of the nervous system. Some neuromodulators are dopamine, serotonin, acetylcholine, histamine, and others.

Neuromodulators are segregated in a natural way as a response to environmental contexts or it can be applied in specific way that is the line that the neuromodulation has developed mostly. 

However this article seeks to focus on the implementation of contextual programs for the acquisition of simple learning from environmental interactions for the treatment on the developmental disorders, unless they require implants or clinical procedures.

How does it work?, breaking environmental habits, creating personal habits

Unlike assisted therapies, for example behavioral or cognitive-behavioral, neuromodulation process applies in the environments in which the child is integrated; this is your home, school, or anywhere where you are visiting.

The first analysis of the context consists of the family habits, which are often laden with frustration and disorder. The process begin redrawing environments and creating systems of habits in which all members of the family can feel good.

Once it is designed the set of habits explores what however is able to do as opposed to traditional therapies that focus on the dis ability, this model seeks to observe the cognitive framework that will allow the creation of new tasks and processes.

Sensory processes

Parents or caregivers take care of a program designed exclusively for each child that has as its goal the shaping of specific tasks required within the environment, for example, it is common to find delays in the acquisition of speech, but understanding of language in children, partly because parents, noting the delay in the development, won't stimulate the children become translators of children by what is taught to parents to encourage the child, starting with simple words.

One of the first words that develops easily is water, provided to the person in charge whenever the child is facing the stimulus say water, and occurs during diverse occasions and contexts during the day, for example when children are washing their  hands, to drinking it, at the time of daily hygiene, at the beginning only says the word slowly without having to finish the child repeat it nor whether this puts all his attention to the stimulus, the goal is that is heard in the context of the word.

Soon, children begin to either try to use the word or you are able to use it properly. If the goal is achieved, then begins with adjectives like water cold, hot, delicious, fresh.

One of the mistakes is that I forced children to perform tasks that the adult may be simple, but that for a nervous system that does not yet integrate stimuli becomes extremely complex, therefore it modulates the task, dividing it into subtasks on the assumption of sensory integration planned. 

Returning the example of water, this concept is a succession of 3 sounds arranged in a certain way, it is an object, form, texture and temperature, by what is heard, looks, feels, so for the apprehension of the same, is first exposed to the small to the sound, then to the view and then to the touch, so provided the sensory system is able to recognize this differently and if any area of the brain is affected, it is possible to acquire it by any other sensory input.

So the division of tasks is important for understanding and consolidation of stimuli, which are located in the immediate environment and allow actor learned, because it is common that therapies clinic learn concepts that are not able to reproduce in other environments.

In this case it is possible to use different objects that approach clearly and relaxed the child into the world that surrounds him.

What is different about this model? 

This way to work with children begin with the  assumption that all brains, no matter the degree of physio-anatomic damage are able to learn under the right conditions, and controlled, leaving you free to explore modeling.

There is no right or wrong answers, if the child is not able to accomplish the task, it doesn't matter, because it will longer be able to do it tomorrow, since there are ideal, every child is unique.

The child adapts to the environment and not the environment to the child. When it is taught to adapt to the environment, there is less tension, frustration and calm allows much more than the traditional behavioral therapies.

Attention is molded in minimum time, beginning with 5 seconds, 6 seconds, and 7 seconds and on, do not force the child to keep the attention for long periods it is known that this does not always work and is frustrating. This way  attention is sustained and effective without forcing the sensations.

The guidance of the therapist is important because along with weekly reports are allowed to build the program to follow, because this is not a rigid model, but a flexible framework that allows learning modeling and accompanied. The goals  are short, medium, and long term. There are no magic formulas, only motivation to prevent cognitive Kickback.

No scolding, no punishments, children and parents learn to appreciate the achievements by small that are and is constructed with a solid base of learning, from habits that give confidence to the child, as the center of this therapeutic modality is the idea that if you are able to learn, from a natural endowment which looks for the survival of the species.

References:

Ackley, D., and Littman, M. (1991) Interactions between learning and evolution. In CG Langton, C Taylor, JD Farmer & S, Rasmussen (1991) Artificial life II, SFI studies in the sciences of complexity. Vol X.  United States. Addison –Wesley.

Baldwin, JA. (1896) A New Factor in Evolution. American Naturalist 30, 441-451, 536-553. Disponible en http://www.brocku.ca/MeadProject/Baldwin/Baldwin_1896_h.html

Barret, HC. (2012) A hierarchical model of the evolution of brain specializations. Proceedings of the National Academy of Science of the United States of America. 19 (Supl 1). 10733- 10740.

Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith, SM, Beckmann, CF, Adelstein, JS, Buckner RL, Colcombe S, et al (2010) Toward discovery science of human brain function. Proceedings of the National Academy of Sciences 107 (10) 4734-4740.

Conrad, M. (2004) Evolutionary learning circuits. Journal of theoretical Biology. 46 (1) 167-188.

Dehaene, S. (2004) Evolution of human cortical circuits for Reading and arithmetic: the neuronal recycling hypothesis. In S. Dehaene, J. R. Duhamel, M. Hauser & G. Rizzolatti (Eds.), From monkey brain to human brain (2004). Cambridge, Massachusetts: MIT Press.

Dehaene, S., and Cohen, L. (2007) Cultural Recycling of cortical maps. Neuron. 56 (2) 384-398.

Dehaene, S. (2009) Reading in the brain:The science and evolution of a human invention. USA. Viking: Penguin Group.

Dehaene, S., Cohen, L., Sigman, M. y Vinckier, F. (2005) The neural code for written words: a proposal. Trends in Cognitive Sciences. 9, 335-341.

Dzib, A. (2010) Alteraciones del desarrollo por dificultades perinatales y la confusión con los trastornos del espectro autista. Revista de Neuropsicología. 5 (1) 4-9.

Dzib Goodin, A. (2012) El virus HHV-6 y sus efectos en el neurodesarrollo: un estudio de caso. Revista Mexicana de Neurociencia.13 (3) 150-153.

Ehn, M., & Laland, K. (2012) Adaptative strategies for cumulative cultural learning. Journal of Theoretical Biology. 301. 103-111.

Fernando, C., and Szathmáry, E. (2010) Natural selection in the brain. In B., Glatzeder, V. Goel, and A. Muller (Eds) Towards a theory of thinking: building blocks for a conceptual framework. Springer. Germany.

Fernando, C., Szathmáry, F., and Husbands, P. (2012) Selectionist and evolutionary approaches to brain function. A critical appraisal. Frontiers in Computational Neuroscience. 6 (Art. 24). Disponible en: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337445/pdf/fncom-06-00024.pdf.

Gauci, J., and Stanley, K.O. (2010) Autonomous evolution of topographic regularities in artificial neural networks. Neural Computation 22 (7) 1860-1898.

Haesler, S. (2007) Programmed for speech. Scientific American Mind. 18 (3) 66-71.

Hinton, GE., and Nowlan, SJ. (1987) How learning can guide evolution. Complex Systems. 1. 495-502.

Ionescu, T. (2011) Exploring the nature of cognitive flexibility. New Ideas in Psychology. 30 (2) 190-200.

Iriki, A., and Taoka, M. (2012) Triadic (ecological, neural, cognitive) niche construction: a scenario of human brain evolution extrapolating tool use and language from the control of reaching actions. Philosophical Transactions of the Royal Society Biological Science. 367. 10-23.

Kong, Q., Goldfarb, L., Gabizon, R., Montagna, P., Lugaresi, E., Piccardo, P., Petersen, RB., Parchi, P., Chen, SG., Capellari, S., Ghetti, B. (2004) Inherited Prion Diseases. In Prion Biology and Diseases. Cold Springs Harbor Laboratory Press.

Krushner, D. (2011) The man who builds brains. The Brain, Discovery Magazine. Disponible en red: http://discovermagazine.com/2009/dec/05-discover-interview-the-man-who-builds-brains

Leonard, L. (2002) Children with specific language impairment. Boston: MIT Press.

Li, J., Browning, S., Mahal, SP., Oelschlegel, AM., Weissmannm, C. (2010) Darwinian evolution of prions in cell culture. Science. 327 (5967) 869-872.

Lieberman, P. (2002) On the nature and evolution of the neural bases of human language, American Journal of Physiology and Anthropology. 45. 36-62.

Liu, Q., Zhou, H., Cui, J., Cao, Z., & Xu, Y. (2012) Reconsieraction of In-Silico SiRNA design based on feature selection: A cross-plattform data integration perspective. Plos One.7 (5) e37879.doi:10.1371/journal.pone.0037879

Lock, A., and Gers, M. (2012) The cultural evolution of written language and its effects: A Darwinian process from prehistory to the modern day. In ELL, Grigorenko, E. Mambrino, DD, Preis (Eds) (2012) Writing: A mosaic of new perspectives. New York. Psychology Press: Taylor & Francis Group.

Lotem, A., & Halpern, JY. (2012) Coevolution of learning and data-adquisition mechanisms: a model for cognitive evolution.  Philosophical transactions the Royal Society: Biological Science. 367 (1063) 2686-2694.

Marin Padilla, M. (2001) Evolución de la estructura de la neocorteza del mamífero: nueva teoría citoarquitectónica. Rev Neurol. 33 (9): 843-853.

McGilchrist, I. (2011) The divided brain. RSA Animated. Disponible en red: http://www.youtube.com/watch?v=dFs9WO2B8uI.

McGregor, S. Vasas, V., Husbands, Evolution of associative learning in chemical networks. Plos Computational Biology. Disponible en http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002739

Mennes, M., Kelly, C., Colcombe, S., Castellanos, FX., y Milham, MP. (2012) The extrinsic and intrinsic functional architectures of the human brain are not equivalent. Cerebral Cortex. Disponible en red: http://cercor.oxfordjournals.org/content/early/2012/01/31/cercor.bhs010.full.pdf+html

Miikkulainen, R., Feasly, E., Hohnson, L. Karpov, I., Rajagopalan, P., Rawal, A., and Tansey, W. (2012) Multiagent learning through neuroevolution. Advances in Computational Intelligence. 7311. 24-46.

Nolfi, S., and Parisi, D (1994). Good teaching inputs do not correspond to desired responses in ecological neural networks. Neural Processing Letters 1 no. 2 (11/94) pp. 1-4.

Nolfi, S., y Parisi, D. (1996) Learning to adapt to changing enviroments in evolving neural networks. Adaptative Behavior. 5 (1) 75-88.

Nolfi, S., Elman, J., and Parisi, D. (1994). Learning and evolution in neural networks. Adaptive Behavior 2 (1994): 5-28.

Olarreta, A. (2005) Orígenes del lenguaje y selección natural. Sirius. Madrid.

Seung, HS. (2012) Connectome: How the Brain's Wiring Makes Us Who We Are. New York: Houghton Mifflin Harcout.

Seung, S. (2010) Sebastian Seung: I am connectome. TED Talks. Disponible en http://www.ted.com/talks/sebastian_seung.html

Tubino, M. (2004) Plasticidad y evolución: papel de la interacción cerebro – entorno. Revista de estudios neurolingüsticos. 2 (1) 16-25.

Valerio, LG., and Choudhuri, S. (2012) Chemoinformatics and chemical genomics: potential utility of in silico methods. Journal of Applied Toxicology. 32 (11) 880-889.

TEDxCaltech (2013) The remarkable Neuron: Erin Shuman at TEDxCaltech. Disponible en red: http://www.youtube.com/watch?v=yr6kh_QOk0s