From neurotransmitters to dynamic connectivity: A statistical mechanics approach to modelling cortical interactions.

点击翻译按钮获取中文摘要

The human brain constantly reorganises its connections in response to the events that we experience every day. Our ability to modify the connections in our brain is called neuroplasticity and it underlies our capacity to learn and develop as well as to heal after experiencing physical or psychological trauma or injury. The precise biological mechanisms underlying plasticity still elude us. However, a clear understanding of how plasticity works will have great implications for clinical neuroscience. Furthering our knowledge of how healthy brains develop and age will directly benefit people who have suffered from a form of brain injury and are trying to stimulate plasticity in order to recover, or are simply stimulating plasticity to prevent decline or enhance cognitive performance.A number of different techniques are currently being used by clinicians to stimulate plasticity. Essentially, most techniques involve electrical, magnetic or sensory stimulation of a target brain region. If neurons are activated at the same time many times over, then the connections between them get stronger. Conversely, if neurons are not used then their connections lose strength and eventually stop working all together. This is the basis of Hebbian learning and plasticity. Stimulating large brain areas at the same time is a way of activating large populations of neurons so that the connection strengths within the network grow. However, as we do not fully understand the mechanisms underlying the changes we see in network plasticity, we are unable to optimise these methods so that people can get the maximum benefit from their treatment.Applying mathematical and computational techniques to biological problems is a very powerful tool for exploring how these complicated systems operate. The brain is now recognised as a highly dynamic and complex organ and data analysis methods designed to cope with its' complexity are constantly evolving. Mathematical modelling work at the microscopic level of individual cells has provided great insight into the biological processes underlying chemical reactions within a cell or between small numbers of cells. So far, these models have not been scaled up to include network interactions spanning over whole brain regions in response to different kinds of stimulation. We have recently developed a model of plasticity in the brain that was able to predict behavioural changes in people responding to two different kinds of stimulation. However, the model could not account for the chemical changes happening within the brain and so we were unable to understand how these were related to the behavioural observations. I am proposing to develop a mathematical model that is able to account for changes in activity, connectivity and chemical concentrations within the brain in response to different kinds of external stimuli. Studying the response of the model to different kinds of stimulation will allow me to to make predictions about how the brain will react to therapies used to boost plasticity.