Direct estimation of neurotransmitter response in awake and freely moving animals

The temporal characterisation of endogenous neurotransmitter release during a cognitive task or drug intervention is an important capability for studying the role of neuro-transmitters in normal and aberrant brain function, including disease. Advanced kinetic models, such as the linear parametric neurotransmitter PET (lp-ntPET) have been developed to appropriately model the transient changes in the model parameters, such as the radiotracer efflux from the target tissue, during endogenous neurotransmitter release. Incorporation of the kinetic model within the tomographic reconstruction algorithm may lead to improved parameter estimates, both in terms of precision and accuracy, compared to the conventional two-step post reconstruction approach. In this study, we evaluate a direct reconstruction approach that uses an expectation maximisation framework to transfer the 4D spatiotemporal maximum likelihood problem into an image-based weighted least squares problem. This framework allows the use of well established kinetic models, such as the lp-ntPET model, to estimate the endogenous neurotransmitter response directly from the dynamic PET data. Dynamic GATE simulations using a realistic digital rat brain phantom showed that the proposed direct reconstruction method can provide higher temporal accuracy and precision for the estimated neurotransmitter response at the voxel level, compared to the conventional post reconstruction modelling. In addition, we applied this methodology to a [
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C]raclopride displacement study in an awake and freely moving rat and generated voxel-wise parametric maps illustrating ligand displacement from striatum.