Temporal coupling of metabolic and hemodynamic signals

An essential cornerstone for our understanding of human brain function is given by the investigation of neuronal activation. State-of-the-art brain imaging such as functional magnetic resonance imaging (fMRI) allow to image neuronal activation at a temporal scale of seconds. fMRI is a widely used but indirect approach that reflects the coupling of neuronal activation with the corresponding blood flow. Recent developments from our research group in positron emission tomography (PET) now also enable to assess metabolic demands related to neuronal activation, however, at lower temporal scales. PET is still considered a more direct marker of neuronal activation as energy metabolism occurs at the neuron. The research project aims to investigate the coupling mechanisms that link neuronal activation with the corresponding metabolic demands and blood flow. This will be realized by combining several recent technical advancements, which enable fully synchronous acquisition of fMRI and PET data, both at the same temporal scale of 2 seconds. As such, the project will allow a direct comparison of these signals for the first time in the human brain. Furthermore, specific investigation of the different neurotransmitter systems that drive neuronal activation will provide a causal understanding of these coupling mechanisms. Healthy volunteers will undergo three brain imaging sessions, one at baseline and two after specifically targeting different neurotransmitter systems. Neuronal activation will be elicited by visual stimulation, a motor coordination task and autobiographical memory in order to map various different neuronal networks, with a specific focus on the so-called default mode network which regulates brain network interactions. The project is expected to yield new insights into the coupling of two major aspects of neuronal activation, namely energy metabolism and blood flow. Investigating these neurophysiological underpinnings of the default mode network will represent an important step to improve our understanding of cognitive processing. This may also provide relevant clinical insight, due to the pivotal involvement of the default mode network in numerous brain disorders, such as major depression and Alzheimers disease.