Back

Diurnal and seasonal regulation of brain mu-opioid and dopamine D2 receptor signalling in humans

Zareba, M. R.; Rinne, J.; Hietala, J.; Nuutila, P.; Nummenmaa, L.; Sun, L.

2026-09-04 neuroscience
10.64898/2026.09.01.748534 bioRxiv
Show abstract

Purpose: Rhythmic regulation of human neurotransmission systems, particularly the mu-opioid receptor (MOR) and dopamine D2 receptor (D2R) signaling, remains poorly understood. Although prior studies indicate possible diurnal fluctuations in D2R signaling, potential existence of diurnal rhythms in MOR signaling has yet to be investigated. Furthermore, whether and how seasonal factors may override or interact with diurnal rhythms in either system remains unexplored. Decoding these temporal dynamics and their possible links to behavioral traits, such as anxiety, may improve mood regulation models and inspire rhythm-tailored interventions. Methods: Based on a historical PET database of healthy humans scanned with [11C]carfentanil (n = 188) and [11C]raclopride (n = 184), we investigated the diurnal patterns of MOR and D2R availability in the brain, using time-of-day (TOD) of scanning as predictor. Furthermore, we examined whether the TOD differences in receptor availability were modulated by seasonal differences in the daylength (D2R and MOR) and trait-anxiety (MOR). Results: The data revealed increased MOR and decreased D2R availability in the afternoon compared to the morning scans. Interaction with the seasonal effect was, however, observed only for MOR availability, spanning brain areas including anterior cingulate, dorsomedial and dorsolateral prefrontal cortex, insula and striatum. Besides, the diurnal variation of MOR availability in these regions was also sensitive to trait-anxiety levels. Conclusions: The current results reveal the diurnal patterns in opioidergic and dopaminergic neurotransmission with an opposing trajectory. Furthermore, the data highlight a unique role of the MOR system, mediating both types of rhythmic regulations, as well as behavioral traits.

Matching journals

The top 10 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.