Clarity and accuracy of reporting are fundamental to the scientific process. Readability formulas can estimate how difficult a text is to read. Here, in a corpus consisting of 709,577 abstracts published between 1881 and 2015 from 123 scientific journals, we show that the readability of science is steadily decreasing. Our analyses show that this trend is indicative of a growing use of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings.

BackgroundIn the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified.Methodology/Principal FindingsWe quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network.Conclusions/SignificanceConnectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency.

The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual, sensorimotor and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, several subnetworks, including the visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

Post-error slowing (PES) is consistently observed in decision-making tasks after negative feedback. Yet, findings are inconclusive as to whether PES supports performance accuracy. We addressed the role of PES by employing drift diffusion modeling which enabled us to investigate latent processes of reaction times and accuracy on a large-scale dataset (>5,800 participants) of a visual search experiment with emotional face stimuli. In our experiment, post-error trials were characterized by both adaptive and non-adaptive decision processes. An adaptive increase in participants’ response threshold was sustained over several trials post-error. Contrarily, an initial decrease in evidence accumulation rate, followed by an increase on the subsequent trials, indicates a momentary distraction of task-relevant attention and resulted in an initial accuracy drop. Higher values of decision threshold and evidence accumulation on the post-error trial were associated with higher accuracy on subsequent trials which further gives credence to these parameters’ role in post-error adaptation. Finally, the evidence accumulation rate post-error decreased when the error trial presented angry faces, a finding suggesting that the post-error decision can be influenced by the error context. In conclusion, we demonstrate that error-related response adaptations are multi-component processes that change dynamically over several trials post-error.

The present study aimed to determine changes in brain network integration/segregation during thermal pain using methods optimized for network connectivity events with high temporal resolution. Participants (n = 33) actively judged whether thermal stimuli applied to the volar forearm were painful or not and then rated the warmth/pain intensity after each trial. We show that the temporal evolution of integration/segregation within trials correlates with the subjective ratings of pain. Specifically, the brain shifts from a segregated state to an integrated state when processing painful stimuli. The association with subjective pain ratings occurred at different time points for all networks. However, the degree of association between ratings and integration/segregation vanished for several brain networks when time-varying functional connectivity was measured at lower temporal resolution. Moreover, the increased integration associated with pain is explained to some degree by relative increases in between-network connectivity. Our results highlight the importance of investigating the relationship between pain and brain network connectivity at a single time point scale, since commonly used temporal aggregations of connectivity data may result in that fine-scale changes in network connectivity may go unnoticed. The interplay between integration/segregation reflects shifting demands of information processing between brain networks and this adaptation occurs both for cognitive tasks and nociceptive processing.

The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0-and 2-back epochs. During the interspersed epochs of baseline, many subnetworks, including the default mode, visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fastevolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

Clarity and accuracy of reporting are fundamental to the scientific process. The understandability of written language can be estimated using readability formulae. Here, in a corpus consisting of 707 452 scientific abstracts published between 1881 and 2015 from 122 influential biomedical journals, we show that the readability of science is steadily decreasing. Further, we demonstrate that this trend is indicative of a growing usage of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings.

Abstract■ Negative feedback after an action in a cognitive task can lead to devaluing that action on future trials as well as to more cautious responding when encountering that same choice again. These phenomena have been explored in the past by reinforcement learning theories and cognitive control accounts, respectively. Yet, how cognitive control interacts with value updating to give rise to adequate adaptations under uncertainty is less clear. In this fMRI study, we investigated cognitive control-based behavioral adjustments during a probabilistic reinforcement learning task and studied their influence on performance in a later test phase in which the learned value of items is tested. We provide support for the idea that functionally relevant and memory-reliant behavioral adjustments in the form of post-error slowing during reinforcement learning are associated with test performance. Adjusting response speed after negative feedback was correlated with BOLD activity in right inferior frontal gyrus and bilateral middle occipital cortex during the event of receiving the feedback. Bilateral middle occipital cortex activity overlapped partly with activity reflecting feedback deviance from expectations as measured by unsigned prediction error. These results suggest that cognitive control and feature processing cortical regions interact to implement feedback-congruent adaptations beneficial to learning. ■