Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n = 30), while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affects decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that 1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, 2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain versus loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.
Due to ethical restrictions on data sharing, we are unable to share raw data for this manuscript to preserve participant anonymity. However, anonymized iEEG data in BIDS format can be made available upon request to the corresponding author (JB) and source data files with anonymized regression estimates are available for download.The custom codes used to (i) extract the different frequency envelopes, and in particular the broadband gamma activity (BGA), from the raw intracranial data, (ii) perform the regression analyses at recording site level, and (iii) compute the second level statistics (across all recording sites of a ROI) are available at: https://gitlab.com/romane-cecchi/publications-code/2022-ieeg-mood-and-risky-choice (Cecchi, 2022; copy archived at https://archive.softwareheritage.org/swh:1:dir:ec43d71a97bf2e3c56b38688eaea2dfde19ad573).
- Julien Bastin
- Julien Bastin
- Philippe Kahane
- Julien Bastin
- Jiri Hammer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: All patients gave written, informed consent before their inclusion in the present study, which received approval from the local ethics committees (CPP 09-CHUG-12, study 0907; CPP18-001b / 2017-A03248-45; IRB00003888; CER No. 47-0913).
- Thorsten Kahnt, National Institute on Drug Abuse Intramural Research Program, United States
© 2022, Cecchi et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.
Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell–cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip–Stalk cell patterning can undergo an order–disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.