Graphic by Eilidh McNaughton by rebekah wattsIt has been more than a century since ‘plasticity’ was first used to describe the brain as modifiable (Berlucchi and Buchtel, 2009; as cited in von Bernhardi et al., 2017). Neural plasticity, also known as neuroplasticity or brain plasticity, is defined by Cramer et al. (2011; as cited in von Bernhardi et al., 2017) as the nervous system’s ability to respond to stimuli by altering its structure, connections, and functions. This psychological phenomenon is a crucial part of neural development and the functioning of the nervous system when responding to environmental changes, ageing and injuries (von Bernhardi et al., 2017). Galván (2010) suggests that different variations of neural plasticity that can be clearly seen between young infants and adults. Generally, infants experience a variation of plasticity that influences how the neural system will be organised, at a foundational level, whereas the variation of plasticity experienced by adults modifies and reorganises the existing structure of the brain. Psychological research has allowed an expansion of our knowledge surrounding neural plasticity, with the 1980s introducing neuroscientific animal studies that demonstrated functional plasticity in the cerebral cortex of the brain (responsible for functions such as perception, memory, and voluntary physical movement) (Nudo, 2003). There are two approaches to studying neural plasticity: a cross-sectional approach and a longitudinal approach. Elbert et al. (1995; as cited in Galván, 2010) studied neural plasticity using a cross-sectional approach and found that musicians presented larger cortical structures (areas in the cerebral cortex) correlated to the age at which they began to play their instrument (and consequently how long they had been playing for). Similar studies of neural plasticity have been conducted with animals and the learning of motor skills; Nudo (2003) found that the structure of the motor cortex of animals changed following the learning of a new motor skill – these newly learned skills were also represented by larger cortical areas in these animals’ brains. Alternatively, Greenough et al. (1979; as cited in Galván, 2010) used a longitudinal approach to study the changes in brain structure of rats trained in Hebb-Williams maze[i] over a few weeks. It was found that the area of the brain responsible for processing visual information in the trained rats differed from the nontrained rats, specifically in two neurons that had developed more dendrites (tree-like figures that receives chemical signals from the cell body, changing them into small electrical impulses to be transferred into the cell body), compared to the same neurons present in the brains of the control rats. Park and Bischof (2013) emphasise the importance of neural plasticity on improving the cognitive function ageing brains; it is suggested that younger adults are more susceptible to neural plasticity – showing an increased intrinsic neural capacity with training – when compared to older adults. In a study conducted by Boyke et al (2008; as cited in Park & Bischof, 2013) the areas of the brain responsible for processing auditory information, memory and the region that mediates reward behaviour in older adults increased more after spending 90 days learning to juggle, compared to a control group. However, in this example the neural plasticity present was not maintained after the 90 days of training had ended, highlighting the limitations and, unfortunately, fickle nature of some neural plasticity. Research into neural plasticity can be hugely influential on our understanding of the ageing brain, for example, in the exploration of neural plasticity as an aid to neurodegenerative diseases such as Alzheimer’s disease and the possibility of retraining declining cognitive abilities. Additionally, Wieloch and Nikolich (2006) report the immense importance of neural plasticity in functional recovery following brain injury. Following brain injury rapid cell death occurs in the affected regions resulting in the disruption of functional circuits; one common type of brain injury is ischemic strokes – in which there is a decreased flow of blood to the brain causing cells to die. Recovery of function within the brain occurs in three phases: the activation of cell repair, functional plasticity and neuroanatomical plasticity. Wieloch and Nikolich (2006) explain that functional plasticity changes the properties of neuronal pathways that already exist within the brain, whilst neuroanatomical plasticity forms new connections – these two types of neural plasticity also occur in normal learning. In a study on injured animals, it was found that enriched environments are an efficient way of stimulating functional recovery, and that brain tissue can be activated by conditioning the neurons involved in plasticity. Nudo (2003) reports the development of theories surrounding synaptic mechanisms for functional plasticity that follow injury, these are supported by studies like that of small lesions in somatosensory cortexes (responsible for processing sensory information on touch, position, pain, and temperature) of rats resulting in changes in remote brain areas (showing some form of change to the neural pathways in the brain). Furthermore, neural plasticity and the vast range of changes that can occur at different levels in the organisation of the nervous system can be observed through the effects of learning and experience on the brain. Doupe and Kuhl (2008; as cited in Galván, 2010) argued that the learning of language acts as an important example of the shift between experience-expectant mechanisms (where the normal environment provides the necessary input for neural connections to be made) causing plasticity and experience-dependent mechanisms (where individual experiences cause the formation of neural connections) shaping plasticity. Arguably, one of the most popular studies on neural plasticity and the effects of experience was conducted by Maguire et al. (2000; as cited in Galván, 2010), when studying the brain images of London taxi drivers, it was found that they had larger posterior hippocampi (responsible for memory and spatial awareness) compared to controls, proportionate to the length of driving experience. Experience-dependent plasticity can also be seen in the synaptic changes that occur due to long-term enhancement and long-term depression (Draganski & May, 2008; as cited in Galván, 2010). Finally, Simos et al. (2002; as cited in Galván, 2010) found that dyslexic children experienced a significant increase in reading skill and activation of the left posterior superior temporal gyrus (which mediates language processing), when completing a phonological task, following a long training session/intervention. [i] A behavioural task used to study the spatial memory of animals. References
Galván, A. (2010). Neural plasticity of development and learning. Human Brain Mapping, 31(6), 879-890. DOI: https://doi-org.ezproxy.is.ed.ac.uk/10.1002/hbm.21029 Nudo, R. (2003). Adaptive plasticity in motor cortex: implications for rehabilitation after brain injury. Journal of Rehabilitation Medicine-Supplements, 41, 7-10. DOI: 10.1080/16501960310010070 Park, D. C., & Bischof, G. N. (2013). The aging mind: neuroplasticity in response to cognitive training. Dialogues in Clinical Neuroscience, 15(1), 109–119. DOI: https://doi.org/10.31887/DCNS.2013.15.1/dpark von Bernhardi, R., Bernhardi, L.E., & Eugenín, J. (2017) What is neural plasticity?. In: von Bernhardi, R., Eugenín, J., & Muller, K. (Eds) The Plastic Brain. Advances in Experimental Medicine and Biology (vol 15, 1-15). Wieloch, T., & Nikolich, K. (2006). Mechanisms of neural plasticity following brain injury. Current Opinion in Neurobiology, 16(3), 258-264. DOI: https://doi.org/10.1016/j.conb.2006.05.011
0 Comments
Leave a Reply. |