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Eggerstorfer, B.

Publications and source records attributed to Eggerstorfer, B..

2 recordsLinked to original sources

Hedonic experiences emerge from an orchestrated balance of synergistic and redundant information processing

Ketamine exerts rapid-acting, pro-hedonic effects, yet its precise mechanism remains elusive. Here, we present behavioral and fMRI data from a randomized, placebo-controlled crossover study in 38 healthy participants investigating ketamines sub-acute effects on multivariate information-processing during music-evoked peak hedonic experiences. Leveraging information-theoretical measures, our findings indicate that hedonic experiences depend on a distinct global (as measured by O-Information) and local (as measured by integrated information) balance between redundant - information shared across nodes - and synergistic - information emerging from joint interactions - processes. As hedonic intensity rises, neural dynamics shift toward greater synergy; with the one exception of a deliberate increase in redundancy particularly for key sensory information to ensure reliable transmission of and access to critical external information for subsequent hedonic processing. In contrast, ketamines sub-acute pro-hedonic effects arise potentially from enhancing redundant dynamics at rest, boosting the brains capability to robustly represent and access critical internal information, and thus, fostering an environment optimized to amplify the phenomenological hedonic experience, while simultaneously allowing for more efficient information integration.

neuroscience↗

High-temporal resolution metabolic connectivity resolved by component-based noise correction

Recent advances in functional PET (fPET) allow for accurate modelling of metabolic processes with a temporal resolution in the range of seconds. This enables new applications such as imaging molecular connectivity at temporal resolutions comparable to fMRI. However, high-temporal resolution fPET data are more sensitive to noise and the extraction of a meaningful signal remains a challenge. We developed a component-based preprocessing approach adapted from fMRI, which models structured noise using tissue-specific regressors and removes low-frequency uptake trends from the fPET signal (CompCor). We applied this method to 20 high-temporal [18F]FDG fPET scans from a next-generation long-axial field of view PET/CT system (1s frames) and 16 scans from a conventional PET/MR scanner (3s frames). We compared filtering methods across frequency bands and examined their effects on metabolic connectivity (M-MC) estimates. Metabolic connectivity was markedly influenced by filtering strategy and scanner type. The CompCor filter produced more consistent and structured networks than standard bandpass filters. Intermediate frequency bands (0.01-0.1 Hz) yielded the most reliable connectivity patterns between PET/CT and PET/MR data (r=0.89). High sensitivity PET/CT data revealed structured connectivity patterns also at a higher frequency band (0.1-0.2 Hz). Compared to fMRI functional connectivity, fPET-derived networks were more spatially cohesive but less differentiated. High-temporal [18F]FDG fPET enables reliable estimation of individual resting-state M-MC when paired with appropriate denoising. Scanner choice and preprocessing significantly affect signal quality and interpretation, whereas the proposed physiologically informed pipeline improves comparability across systems and studies.

neuroscience↗