Sleep restriction alters the integration of multiple information sources in probabilistic decision-making

The detrimental effects of sleep loss on overall decision-making have been well described. Due to the complex nature of decisions, there remains a need for studies to identify specific mechanisms of decision-making vulnerable to sleep loss. Bayesian perspectives of decision-making posit judgement formation during decision-making occurs via a process of integrating knowledge gleaned from past experiences (priors) with new information from current observations (likelihoods). We investigated the effects of sleep loss on the ability to integrate multiple sources of information during decision-making by reporting results from two experiments: the first implementing both sleep restriction (SR) and total sleep deprivation (TSD) protocols, and the second implementing an SR protocol. In both experiments, participants were administered the Bayes Decisions Task on which optimal performance requires the integration of Bayesian prior and likelihood information. Participants in Experiment 1 showed reduced reliance on both information sources after SR, while no significant change was observed after TSD. Participants in Experiment 2 showed reduced reliance on likelihood after SR, especially during morning testing sessions. No accuracy-related impairments resulting from SR and TSD were observed in both experiments. Our findings show SR affects decision-making through altering the way individuals integrate available sources of information. Additionally, the ability to integrate information during SR may be influenced by time of day. Broadly, our findings carry implications for working professionals who are required to make high-stakes decisions on the job, yet consistently receive insufficient sleep due to work schedule demands.

Whole-brain analyses indicate the impairment of posterior integration and thalamo-frontotemporal broadcasting in disorders of consciousness

The study of the brain's dynamical activity is opening a window to help the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and the dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI data, fitted to a linear model of activity propagation. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to severe reduction of glucose metabolism as measured with [¹⁸ F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results shed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior.