scholarly journals Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition

2021 ◽  
Author(s):  
Pedro Mediano ◽  
Aleksi Ikkala ◽  
Rogier A. Kievit ◽  
Sridhar R. Jagannathan ◽  
Thomas F. Varley ◽  
...  

There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

2021 ◽  
Vol 38 (4) ◽  
pp. 1410-1429
Author(s):  
Claire Wilson ◽  
Tommy van Steen ◽  
Christabel Akinyode ◽  
Zara P. Brodie ◽  
Graham G. Scott

Technology has given rise to online behaviors such as sexting. It is important that we examine predictors of such behavior in order to understand who is more likely to sext and thus inform intervention aimed at sexting awareness. We used the Theory of Planned Behavior (TPB) to examine sexting beliefs and behavior. Participants (n = 418; 70.3% women) completed questionnaires assessing attitudes (instrumental and affective), subjective norms (injunctive and descriptive), control perceptions (self-efficacy and controllability) and intentions toward sexting. Specific sexting beliefs (fun/carefree beliefs, perceived risks and relational expectations) were also measured and sexting behavior reported. Relationship status, instrumental attitude, injunctive norm, descriptive norm and self-efficacy were associated with sexting intentions. Relationship status, intentions and self-efficacy related to sexting behavior. Results provide insight into the social-cognitive factors related to individuals’ sexting behavior and bring us closer to understanding what beliefs predict the behavior.


Author(s):  
Srikanth Ponnada ◽  
Maryam Sadat Kiai ◽  
Demudu Babu Gorle ◽  
Saravanakumar Rajagopal ◽  
Swetha Andra ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura Bechtold ◽  
Christian Bellebaum ◽  
Paul Hoffman ◽  
Marta Ghio

AbstractThis study aimed to replicate and validate concreteness and context effects on semantic word processing. In Experiment 1, we replicated the behavioral findings of Hoffman et al. (Cortex 63,250–266, https://doi.org/10.1016/j.cortex.2014.09.001, 2015) by applying their cueing paradigm with their original stimuli translated into German. We found concreteness and contextual cues to facilitate word processing in a semantic judgment task with 55 healthy adults. The two factors interacted in their effect on reaction times: abstract word processing profited more strongly from a contextual cue, while the concrete words’ processing advantage was reduced but still present. For accuracy, the descriptive pattern of results suggested an interaction, which was, however, not significant. In Experiment 2, we reformulated the contextual cues to avoid repetition of the to-be-processed word. In 83 healthy adults, the same pattern of results emerged, further validating the findings. Our corroborating evidence supports theories integrating representational richness and semantic control mechanisms as complementary mechanisms in semantic word processing.


2016 ◽  
Vol 45 (6) ◽  
pp. 2554-2561 ◽  
Author(s):  
Nicolas A. McLeod ◽  
Lyudmila G. Kuzmina ◽  
Ilia Korobkov ◽  
Judith A. K. Howard ◽  
Georgii I. Nikonov

The β-SiH agostic complex (ArN)2Mo{η3-N(tBu)SiMe2–H}H is a pre-catalyst for hydrosilylation of carbonyls. Mechanistic studies revealed a non-hydride mechanism, with the benzoxy complex 8 being the resting state.


2009 ◽  
Vol 120 (4) ◽  
pp. 719-729 ◽  
Author(s):  
Claudio Babiloni ◽  
Marco Sarà ◽  
Fabrizio Vecchio ◽  
Francesca Pistoia ◽  
Fabio Sebastiano ◽  
...  

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.


Author(s):  
Mikhail V. Pletnikov ◽  
Christopher A. Ross

Despite the recent advances in research into schizophrenia and bipolar disorder, the neurobiology of these maladies remains poorly understood. Animal models can be instrumental in elucidating the underlying mechanisms of neuropsychiatric disorders. Early animal models of schizophrenia and bipolar disorder used lesion methods, pharmacologic challenges or environmental interventions to mimic pathogenic features of the diseases. The recent progress in genetics has stimulated the development of etiological models that have begun to provide insight into pathogenesis. In this review, we evaluate the strengths and weaknesses of the existing genetic mouse models of schizophrenia and discuss potential developments for the future.


2017 ◽  
Vol 8 (4) ◽  
pp. 51-71
Author(s):  
Sanjay Misra ◽  
Adewole Adewumi ◽  
Robertas Damasevicius ◽  
Rytis Maskeliunas

In order to maintain the quality of software, it is important to measure it complexity. This provides an insight into the degree of comprehensibility and maintainability of the software. Measurement can be carried out using cognitive measures which are based on cognitive informatics. A number of such measures have been proposed in literature. The goal of this article is to identify the features and advantages of the existing measures. In addition, a comparative analysis is done based on some selected criteria. The results show that there is a similar trend in the output obtained from the different measures when they are applied to different examples. This makes it easy for adopting organisations to readily choose from the options based on the availability of tool support.


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