Abstract
Building computational models of brain parts at realistic scale is critical for prediction and treatment of neurological and psychiatric disorders and trauma. The contribution of this paper is the use of the Runge–Kutta–Fehlberg method to incorporate a kinetic model of synaptic transmission mediated by the neurotransmitter Acetylcholine (ACh) into a neural mass model of the thalamocortical circuitry, thereby imitating brainstem inputs to the thalamus. The result is a model of the thalamocortical oscillations as observed in electroencephalogram (EEG), firstly by introducing ACh into the existing model and secondly by varying synaptic parameters of the cholinergic pathway. Results show that embedding cholinergic input to the existing thalamocortical model leads to a change in postsynaptic voltages in the Lateral Geniculate Nucleus, as compared to the postsynaptic voltages without cholinergic input. In addition, it is observed that varying the cholinergic input to the Thalamic Reticular Nucleus (TRN) cell population around basal values deals with bifurcation in the model behaviour, implicating the crucial role of brainstem inputs to the TRN. This may underpin neural correlates of visuomotor deficiencies, which is a potential biomarker for early detection of Alzheimer’s disease (AD).
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We would like to convey our sincere thanks and gratitude to Dr Neil Buckley of Department of Mathematics and Computer Science, Liverpool Hope University, UK, for his active help during the preparation of this manuscript.
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Dasgupta, M., Bhattacharya, B.S., Nagar, A. (2019). Linking Brainstem Cholinergic Input to Thalamocortical Circuitry. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_29
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