Developing Novel Methodologies for Neurotransmitter Release Detection with Positron Emission Tomography

Abstract:

Neurotransmitters are endogenous signaling molecules that mediate neural activity: they are released from presynaptic neurons into the synaptic cleft, where they bind to receptor sites on postsynaptic neurons. This process underlies communication between neurons and supports the complex functions of the brain. When alterations to neurotransmitter systems occur, for instance in neurodegenerative diseases like Parkinson’s disease, it is expected that abnormal release of neurotransmitters—along with related changes to neural activity—contribute to the observed symptoms. Positron Emission Tomography (PET) is an imaging modality widely used to assess neurotransmitter systems, owing to its ability to employ radiolabeled ligands that bind selectively to specific receptors or transporters. 

This thesis is devoted to developing methods for detecting and characterizing neurotransmitter release using dynamic PET imaging, with a focus on the neurotransmitter dopamine given its essential role in motor control, cognition, and reward-related processes. The initial developed methodology targets dopamine release elicited by simple physiological tasks, such as finger or foot tapping performed during the scan. Dopamine release responses from such tasks are expected to be small in amplitude and restricted to localized brain regions, therefore the modeling approach emphasizes robustness, using minimal degrees of freedom to ensure reliable estimation of dopamine release-related PET signals. While effective for subtle physiological release, this method is less suited for more general scenarios, such as the spatially extended and high-amplitude release induced by pharmacological challenges. To address this, we introduce a generalized framework that accommodates a wide range of release patterns. Simulation studies demonstrate that this approach consistently outperforms existing frameworks, while also yielding an interpretable metric that scales with the magnitude of underlying release. As proof-of-concept, we apply the method to both healthy control subjects and Parkinson’s disease cohorts, identifying clear disease-related changes in task-related dopamine release distributions. Finally, by integrating PET with functional magnetic resonance imaging (fMRI)-based measures of blood oxygenation, we examine relationships between dopamine release and whole-brain activity, further validating the framework and offering insight into disease mechanisms.