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When learning the value of actions in volatile environments, humans often make seemingly irrational decisions that fail to maximize expected value. We reasoned that these ‘non-greedy’ decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here using reinforcement learning models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stem from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by blood oxygen level-dependent responses to obtained rewards in the dorsal anterior cingulate cortex and by phasic pupillary dilation, suggestive of neuromodulatory fluctuations driven by the locus coeruleus–norepinephrine system. Together, these findings indicate that most behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

Increased mental-health symptoms as a reaction to stressful life events, such as the Covid-19 pandemic, are common. Critically, successful adaptation helps to reduce such symptoms to baseline, preventing long-term psychiatric disorders. It is thus important to understand whether and which psychiatric symptoms show transient elevations, and which persist long-term and become chronically heightened. At particular risk for the latter trajectory are symptom dimensions directly affected by the pandemic, such as obsessive–compulsive (OC) symptoms. In this longitudinal large-scale study (N = 406), we assessed how OC, anxiety and depression symptoms changed throughout the first pandemic wave in a sample of the general UK public. We further examined how these symptoms affected pandemic-related information seeking and adherence to governmental guidelines. We show that scores in all psychiatric domains were initially elevated, but showed distinct longitudinal change patterns. Depression scores decreased, and anxiety plateaued during the first pandemic wave, while OC symptoms further increased, even after the ease of Covid-19 restrictions. These OC symptoms were directly linked to Covid-related information seeking, which gave rise to higher adherence to government guidelines. This increase of OC symptoms in this non-clinical sample shows that the domain is disproportionately affected by the pandemic. We discuss the long-term impact of the Covid-19 pandemic on public mental health, which calls for continued close observation of symptom development.

Neural variability, i.e. random fluctuations in neural activity, is a ubiquitous and sizable brain feature that impacts behavior. Its functional role however remains unclear and neural variability is commonly viewed as a nuisance factor degrading behavioral efficiency. Using functional magnetic resonance imaging in humans and computational modeling, we show here that neural variability provides a solution to the open issue regarding how the brain produces efficient adaptive behavior in uncertain and changing environments without facing computational complexity problems. We found that neural variability in the medial prefrontal cortex (mPFC) enables decision-making processes in the mPFC to produce near-optimal behavior in uncertain and ever-changing environments without involving complex computations known in such environments to rapidly become computationally intractable. The results thus suggest that in the same way as genetic variability contributes to adaptive evolution, neural variability contributes to efficient adaptive behavior in real-life environments. Neural variability is a ubiquitous brain feature, but its functional role remains unclear. Here the authors show that neural variability observed in the human prefrontal cortex through fMRI accounts for how the brain produces efficient adaptive behavior in uncertain and changing environments.

Informational cues such as the price of a wine can trigger expectations about its taste quality and thereby modulate the sensory experience on a reported and neural level. Yet it is unclear how the brain translates such expectations into sensory pleasantness. We used a whole-brain multilevel mediation approach with healthy participants who tasted identical wines cued with different prices while their brains were scanned using fMRI. We found that the brain’s valuation system (BVS) in concert with the anterior prefrontal cortex played a key role in implementing the effect of price cues on taste pleasantness ratings. The sensitivity of the BVS to monetary rewards outside the taste domain moderated the strength of these effects. These findings provide novel evidence for the fundamental role that neural pathways linked to motivation and affective regulation play for the effect of informational cues on sensory experiences.

Humans learn which actions yield the highest rewards through trial and error, gradually forming expectations about outcomes. Yet, people differ substantially in how quickly and precisely they learn. Such individual variability may partly be explained by differences in the brain's microstructural organisation. In this large-scale study, 248 participants completed a gamified reward-learning task and underwent quantitative MRI to assess whole-brain microstructural indices of myelination (R1) and cortical iron (R2*). Using computational modelling, we quantified participants' learning rates and learning noise, reflecting variability in how reward information is updated over time. Whole-brain voxel-based quantification analyses revealed that increased myelination in the cerebellum was associated with a higher learning rate, whereas learning noise was linked to increased myelination and iron concentration in the precentral gyrus. Together, these findings show that reward learning is not a unitary process but is instead shaped by distinct neurobiological pathways that support learning precision and noise. This work highlights how microstructural variation in sensorimotor and associative cortices contributes to stable versus variable reward learning behaviour across individuals.Competing Interest StatementThe authors have declared no competing interest.Funder Information DeclaredEuropean Research Council, ERC-2020-StG-948788European Research Council, 946055Wellcome Trust, 316955/Z/24/Z

Informational cues such as the price of a wine can trigger expectations about its taste quality and thereby modulate the sensory experience on a reported and neural level. Yet it is unclear how the brain translates such expectations into sensory pleasantness. We used multilevel mediation analysis of neural and behavioral data obtained in participants who tasted identical wines cued with different prices. We found that the brain's valuation system (BVS) in concert with the anterior prefrontal cortex explained the effect of price cues on taste pleasantness ratings. The sensitivity of the BVS to rewards outside the taste domain moderated the strength of these effects. Moreover, brain mediators of price cue effects overlapped with brain regions previously found to be involved in placebo analgesia. These findings provide novel evidence for the fundamental role that neural pathways linked to motivation and affective regulation play for the effect of informational cues on sensory experiences.

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions which fail to maximize expected value. We reasoned that these 'non-greedy' decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here, using reinforcement learning (RL) models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stems from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by BOLD responses to obtained rewards in the dorsal anterior cingulate cortex (dACC) and by phasic pupillary dilation - suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine (LC-NE) system. Together, these findings indicate that most of behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

Aging is associated with a high prevalence of insomnia, which is linked to somatic and neuropsychiatric diseases, as well as metabolic and immunological dysfunction. This study aims to identify alterations in the transcriptome profiles and functional metabolic pathways in older adults with different types of sleep disorders. This cross-sectional study included 1002 participants (60–90 years) who were screened for sleep disorders using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Two types of sleep disorders were identified in the study cohort, i.e., sleep onset insomnia and sleep maintenance insomnia. Both types of insomnia were further analyzed for associations with clinical characteristics, laboratory testing results, and socioeconomic backgrounds. The transcriptomic profiles of peripheral blood samples were examined in 236 individuals, supplemented with differential gene and dsRNA expression analyses (DESeq2). Both sleep onset insomnia and middle insomnia were associated with depression, chronic pain syndrome, and osteoarthritis, while only middle insomnia was associated with cardiometabolic diseases. No associations were observed between sleep onset insomnia or reduced sleep duration and transcriptomic profiles. In contrast, 244 genes were differentially expressed in patients with middle insomnia, indicating the activation of pathways related to viral infection response and inhibition of protein synthesis. Additionally, differential expression analysis of double-stranded RNA (dsRNA) identified 2139 significant changes. Middle insomnia in older adults is associated with transcriptomic changes indicative of an activated antiviral immune response, likely resulting from changes in dsRNA expression levels. The chronic inflammation arising from these transcriptomic alterations may underlie the observed association between middle insomnia and cardiometabolic conditions.

Minimally invasive diagnostics based on liquid biopsy makes it possible early detection of lung cancer (LC). The blood plasma circulating cell-free DNA (cfDNA) fragments reflect the genome and chromatin status and are considered as integral cancer biomarkers and the biological entities for 'cancer-of-origin' prediction. The aim of this work is to create a method for processing next-generation sequencing (NGS) data and an interpretable binary classification model (CM), which analyzed cfDNA fragmentation features for distinguishing healthy subjects and subjects with LC. 148 healthy subjects and 138 subjects with LC were included in the study. cfDNA fractions, isolated from blood plasma biospecimens, were used for DNA libraries preparations and NGS on the NovaSeq 6,000 Illumina system with a coverage of 100 million reads/sample. Twelve variables, describing the abundance and length distribution of cfDNA fragments within each genomic interval, and 40 variables based on the values of position-weight matrices, describing combinations of 5-bp-long terminal motifs of cfDNA fragments, were used to characterize genomic fragmentation. Classification models of the first phase of machine learning were based either on logistic regression with L1- and L2-regularization or were probabilistic CMs based on Gaussian processes. The second phase CM was based on kernel logistic regression. The final CM can distinguish healthy subjects and subjects with LC with AUC values of 0.872-0.875. The performance of developed CM was evaluated using datum and testing sets for each LC stage category. Sensitivity values ranged from 66.7 to 85.7%, from 77.8 to 100%, and from 70 to 80% for LC stages I, II, and III, respectively. Specificity values ranged from 79.3 to 90.0%. Thus, the CM has a good diagnostic value and does not require clinical or other data on tumor-associated biomarkers. The current method for LC detection has some advantages for future clinical implementation as a decision-making support system due to the performance of the CM requires data exclusively from NGS-analysis of blood plasma cfDNA fragmentation; the accuracy of the CM does not depend on any additional clinical data; the CM is highly interpretable and traceable; CM has appropriate modular architecture.