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A dynamic model of neurovascular coupling: Implications for blood vessel dilation and constriction

Zheng, Y. ORCID:, Pan, Y., Harris, S., Billings, S., Coca, D., Berwick, J., Jones, M., Kennerley, A., Johnston, D., Martin, C., Devonshire, I. M. and Mayhew, J. (2010) A dynamic model of neurovascular coupling: Implications for blood vessel dilation and constriction. NeuroImage, 52 (3). pp. 1135-1147. ISSN 1053-8119

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To link to this item DOI: 10.1016/j.neuroimage.2010.01.102


Neurovascular coupling in response to stimulation of the rat barrel cortex was investigated using concurrent multichannel electrophysiology and laser Doppler flowmetry. The data were used to build a linear dynamic model relating neural activity to blood flow. Local field potential time series were subject to current source density analysis, and the time series of a layer IV sink of the barrel cortex was used as the input to the model. The model output was the time series of the changes in regional cerebral blood flow (CBF). We show that this model can provide excellent fit of the CBF responses for stimulus durations of up to 16 s. The structure of the model consisted of two coupled components representing vascular dilation and constriction. The complex temporal characteristics of the CBF time series were reproduced by the relatively simple balance of these two components. We show that the impulse response obtained under the 16-s duration stimulation condition generalised to provide a good prediction to the data from the shorter duration stimulation conditions. Furthermore, by optimising three out of the total of nine model parameters, the variability in the data can be well accounted for over a wide range of stimulus conditions. By establishing linearity, classic system analysis methods can be used to generate and explore a range of equivalent model structures (e.g., feed-forward or feedback) to guide the experimental investigation of the control of vascular dilation and constriction following stimulation.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:33477

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