Biology subjects

Amores, J.

Publications and source records attributed to Amores, J..

2 recordsLinked to original sources

bioRxiv2025

Reactivating a relaxation exercise during sleep to influence cortical hyperarousal in people with frequent nightmares - a randomized crossover trial

Study ObjectivesHigh-frequency EEG activity during sleep (cortical hyperarousal), is a transdiagnostic feature across psychiatric disorders, including nightmare disorder. It is discussed as a target of intervention; however, specific treatment options are yet unavailable. We tested whether exposure to relaxation-associated odor cues during sleep would reduce cortical hyperarousal, i.e. beta (16.25 - 31 Hz), gamma (31.25 - 45 Hz), spindle activity and nightmare occurrence in participants with frequent nightmares. MethodsTwenty-five (21 female, mean age (SD) = 24.94(5.01)) participants, recruited from undergraduate students at University of Luebeck, with [≥]1 nightmare / week received a deep breathing relaxation intervention for one week coupled with an odor. On two subsequent nights in the sleep laboratory, the associated odor (A), or control odor (B) were presented in randomized order in a crossover design with randomization at baseline; participants were blinded to intervention. ResultsN = 11 participants were allocated to AB and n = 14 to BA sequence. Exposure to relaxation-associated odor cues during sleep did not affect beta or gamma activity while spindle count and density were significantly reduced. Reduction in spindle count during reactivation nights correlated with reduced subjective wake-after-sleep-onset. There was no additional impact on nightmare symptoms. There were no adverse events or side effects. ConclusionsThe reactivation of relaxation-associated states with odor cues during sleep may be associated with changes in spectral activity, specifically spindle activity. Future studies should implement multiple nights of reactivation and include different patient groups with cortical hyperarousal to test the transdiagnostic potential of this new intervention.

neuroscience↗

bioRxiv2024

Mapping the combinatorial coding between olfactory receptors and perception with deep learning

The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules activate a subset of our olfactory receptors and combinations of unique receptor activations code for unique odors. Understanding this relationship is crucial for unraveling the mysteries of human olfaction and its potential therapeutic applications. Despite this, predicting molecule-OR interactions remains incredibly difficult. Here, we develop a novel, biologically-inspired approach denoted MolOR that first maps odorant molecules to their respective olfactory receptor (OR) activation profiles and subsequently predicts their odor percepts. Despite a lack of overlap between molecules with OR activation data and percept annotations, our joint model improves percept prediction by leveraging the OR activation profile of each odorant as auxiliary features in predicting its percepts. We extend this cross receptor-percept approach, showing that sets of molecules with very different structures but similar percepts, a common challenge for chemosensory prediction, have similar predicted OR activation profiles. Lastly, we further probe the odorant-OR models predictive ability, showing it can distinguish binding patterns across unique OR families, as well as between protein-coding genes or frequently occuring pseudogenes in the human olfactory subgenome. This work may aid in the potential discovery of novel odorant ligands targeting functions of orphan ORs, and in further characterizing the relationship between chemical structures and percepts. In doing so, we hope to advance our understanding of olfactory perception and the design of new odorants with desired perceptual qualities.

molecular biology↗