Back

Active Suppression of the Nigrostriatal Pathway during Optogenetic Stimulation Revealed by Simultaneous fPET/fMRI

Haas, S.; Bravo, F.; Ionescu, T. M.; Gonzalez-Menendez, I.; Quintanilla-Martinez, L.; Dunkel, G.; Kuebler, L.; Hahn, A.; Lanzenberger, R.; Weigelin, B.; Reischl, G.; Pichler, B. J.; Herfert, K.

2023-10-20 neuroscience
10.1101/2023.10.19.556049 bioRxiv
Show abstract

The dopaminergic system is a central component of the brains neurobiological framework, governing motor control, reward responses, and playing an essential role in various brain disorders such as Parkinsons disease and schizophrenia. Within this complex network, the nigrostriatal pathway represents a critical circuit for dopamine transmission from the substantia nigra to the striatum, a connection that is vital to understanding many of the disease-related dysfunctions. However, stand-alone functional magnetic resonance imaging (fMRI) is unable to study the intricate interplay between brain activation and its molecular underpinnings. In our study, the simultaneous use of [18F]FDG functional positron emission tomography (fPET)/BOLD-fMRI provided a new insight that allowed us to demonstrate an active suppression of the nigrostriatal activity during optogenetic stimulation via presynaptic autoinhibition. Our in vivo observation emphasizes that the observed BOLD signal depression during neuronal stimulation does not correlate with neuronal inactivity, but results from an active suppression of neuronal firing as shown by the high [18F]FDG signal increase. This result not only illustrates the potential of simultaneous fPET/fMRI to understand the molecular mechanisms of brain function but also provides a new perspective on how neurotransmitters such as dopamine influence hemodynamic responses in the brain.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.