Detection of Transient Neurotransmitter Response Using Personalized Neural Networks

Abstract

AO_SCPLOWBSTRACTC_SCPLOWMeasurement of stimulus-induced dopamine release and other types of transient neurotransmitter response (TNR) from dynamic PET images typically suffers from limited detection sensitivity and high false positive rates. Measurement of TNR of a voxel-level can be particularly problematic due to high image noise. In this work, we perform voxel-level TNR detection using artificial neural networks (ANN) and compare their performance to previously used standard statistical tests. Different ANN architectures were trained and tested using simulated and real human PET imaging data, obtained with the tracer [11C]raclopride (a D2 receptor antagonist). A distinguishing feature of our approach is the use of "personalized" ANNs that are designed to operate on the image from a specific subject and scan. Training of personalized ANNs was performed using simulated images that have been matched with the acquired image in terms of the signal and noise. In our tests of TNR detection performance, the F-test of the linear parametric neurotransmitter PET (lp-ntPET) model fit residuals was used as the reference method. For a moderate TNR magnitude, the areas under the receiver operating characteristic curves in simulated tests were 0.64 for the F-test and 0.77-0.79 for the best ANNs. At a fixed false positive rate of 0.01, the true positive rates were 0.6 for the F-test and 0.8-0.9 for the ANNs. When applied to a real image, the ANNs identified a TNR cluster missed by the F-test. The newly found cluster was verified to contain TNR by direct lp-ntPET model fitting. These results demonstrate that personalized ANNs may offer a greater detection sensitivity of dopamine release and other types of TNR compared to previously used methods.