We have developed a nonparametric approach to the analysis of dynamic positron emission tomography (PET) data for extracting temporal characteristics of the change in endogenous neurotransmitter concentration in the brain. An algebraic method based on singular value decomposition (SVD) was applied to simulated data under both rest (neurotransmitter at baseline) and activated (transient neurotransmitter release) conditions. The resulting signals are related to the integral of the change in free neurotransmitter concentration in the tissue. Therefore, a specially designed minimum mean-square error (MMSE) filter must be applied to the signals to recover the desired temporal pattern of neurotransmitter change. To test the method, we simulated sets of realistic time activity curves representing uptake of [11C]raclopride, a dopamine (DA) receptor antagonist, in brain regions, under baseline and dopamine-release conditions. Our tests considered two scenarios: 1) a spatially homogeneous pattern with all voxels in the activated state presenting an identical DA signal; 2) a spatially heterogeneous pattern in which different DA signals were contained in different families of voxels. In the first case, we demonstrated that the timing of a single DA peak can be accurately identified to within 1 min and that two distinct neurotransmitter peaks can be distinguished. In the second case, separate peaks of activation separated by as little as 5 min can be distinguished. A decrease in blood flow during activation could not account for our findings. We applied the method to human PET data acquired with [11C]raclopride in the presence of transiently elevated DA due to intravenous (IV) alcohol. Our results for an area of the nucleus accumbens-a region relevant to alcohol consumption-agreed with a model-based method for estimating the DA response. SVD-based analysis of dynamic PET data promises a completely noninvasive and model-independent technique for determining the dynamics of a neurotransmitter response to cognitive or pharmacological stimuli. Our results indicate that the method is robust enough for application to voxel-by-voxel data.