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8,854 results for “Cognition”
Data from: Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children
<p>The gastrointestinal tract, its resident microorganisms, and the central nervous system are connected by biochemical signaling, also known as the "microbiome-gut-brain-axis." Both the human brain and the gut microbiome have critical developmental windows in the first years of life, raising the possibility that their development is co-occurring and likely co-dependent. Emerging evidence implicates gut microorganisms and microbiota composition in cognitive outcomes and neurodevelopmental disorders (e.g., autism and anxiety), but the influence of gut microbial metabolism on typical neurodevelopment has not been explored in detail. We investigated the relationship of the microbiome with the neuroanatomy and cognitive function of 381 healthy children, demonstrating that differences in gut microbial taxa and gene functions are associated with overall cognitive function and with differences in the size of multiple brain regions. Using a combination of multivariate linear and machine learning (ML) models, we showed that many species, including <em>Alistipes obesi</em> and <em>Blautia wexlerae</em>, were associated with higher cognitive function, while some species such as <em>Ruminococcus gnavus</em> were more commonly found in children with low cognitive scores after controlling for sociodemographic factors. Microbial genes for enzymes involved in the metabolism of neuroactive compounds, particularly short-chain fatty acids such as acetate and propionate, were also associated with cognitive function. In addition, ML models were able to use microbial taxa to predict the volume of brain regions, and many taxa that were identified as important in predicting cognitive function also dominated the feature importance metric for individual brain regions, and for specific subscales of cognitive function. For example, <em>B. wexlerae</em> was the most important species in models predicting the size of the parahippocampal region in both the left and right hemispheres and was among the top predictors of gross motor and expressive language performance. Several species from the phylum Bacteroidetes, including GABA-producing <em>Bacteroides ovatus</em>, were important for predicting the size of the left accumbens area, but not the right. These findings provide potential biomarkers of neurocognition and brain development and may lead to the future development of targets for early detection and early intervention.</p>
Figure 4 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping
Figure 4. – Cognitive maps focused on a minimum common overview (gray concepts and edges (incoming arrows)) and village-specific overviews (white concepts and blue (5 Amerindian villages) or brown (2 Aluku villages) edges) of threats to the fish resource and environment.
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 1. The 10-20 international system electrode placement showing the EEG electrode placement
<p>For the EEG analysis, the average power in the theta band (4 – 8 Hz), and alpha band (8 – 12<br> Hz) were computed at the frontal and parietal electrodes Fz and Pz respectively. Next, the ratio of<br> these two powers was determined, and named the ‘cognitive ratio’ as several researchers found that<br> the fronto-parietal network play important roles in cognitive activities.</p>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Figure 1.Flowchart of the CoDOA.-Realizing an Optimization Approach Inspired from Piaget's Theory on Cognitive Development
<p>The objective of this paper is to introduce an artificial intelligence based optimization<br> approach, which is inspired from Piaget’s theory on cognitive development. The approach has been<br> designed according to essential processes that an individual may experience while learning<br> something new or improving his / her knowledge. These processes are associated with the Piaget’s<br> ideas on an individual’s cognitive development. The approach expressed in this paper is a simple<br> algorithm employing swarm intelligence oriented tasks in order to overcome single-objective<br> optimization problems. For evaluating effectiveness of this early version of the algorithm, test<br> operations have been done via some benchmark functions. The obtained results show that the<br> approach / algorithm can be an alternative to the literature in terms of single-objective optimization.<br> The authors have suggested the name: Cognitive Development Optimization Algorithm (CoDOA)<br> for the related intelligent optimization approach.</p>
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
Figure 3. A brief schema of the CoDOA-SVM approach-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In the Equation 23, TP stands for true classified diabetes positive individuals; TN stands for true classified diabetes negative individuals; FP stands for false classified diabetes positive individuals and finally, FN stands for false classified diabetes negative individuals. • After determining good (optimum) particles, default CoDOA steps are run. • After achieving the total iteration number, it is allowed to train the SVM via optimum Gauss (RBF) kernel function parameters, by using the optimum particle value [sigma (σ) value]. • The trained SVM is now ready for the classification and so is diabetes determination process.<br> A brief schema of the CoDOA-SVM approach is also provided in Figure 3.</p>
Figure 1. Maximum margin hyper-plane (Cortes & Vapnik, 1995).-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In other words, it aims to find the state in which the distance between the two classes is the maximum. The hallmarks of this classification reasoning are the support vectors chosen from the training set, and they are located on the closest points of both classes (Javed, Ayyaz, & Mehmood, 2007). In Figure 1, an example of support vectors and a maximum margin hyper-plane (in other words, an optimum separating hyper- plane) is shown (Cortes & Vapnik, 1995).</p>
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
Data archive for 'Cognitive bias in relation to developmental history and stress response in European starlings (Sturnus vulgaris)'
<p>Data files and R code for Gott et al. 'Cognitive bias in relation to developmental history and stress response in European starlings (Sturnus vulgaris)'.</p> <p>One .csv file gives the trial-by-trial data from the cognitive bias experiment. The other gives individual-level summary variables. Both are used by the R script.</p> <p>Uploaded 9th September 2018</p>
Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized EEG Device
<p>This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, & Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).</p>
Extended data of the article "Lockbox enrichment facilitates manipulative and cognitive activities for mice": Supplementary Figure
<p><span>Figure and results of the distance traveled in the Free Exploratory Paradigm, Open Field Test, and Elevated Plus Maze Test during habituation are shown</span>.</p>
EWAS associations between DNAm and general cognitive score, perceptive performance score, and verbal score
<p><strong><span>Evaluating the association between placenta DNA methylation and cognitive functions in the offspring </span></strong></p> <p><a name="_Hlk141095040"></a><span>Placenta plays a crucial role protecting the foetus from environmental harm and supports the development of its brain. In fact, compromised placental function could predispose an individual to neurodevelopmental disorders</span><span><span>. Placental epigenetic modifications, including DNA methylation, could be considered a proxy of placental function and thus plausible mediators of the association between intrauterine environmental exposures and genetics, and childhood and adult mental health. Although neurodevelopmental disorders such as autism spectrum disorder have been investigated in relation to placenta DNA methylation, no studies have addressed the association between placenta DNA methylation and child’s cognitive functions. </span><span>Thus, our goal here was to investigate whether placental DNA methylation profile measured using the Illumina EPIC array is associated with three different cognitive domains (namely verbal score, perceptive performance score, and general cognitive score) assessed by the McCarthy Scales of Children’s functions in childhood at age 4. To this end, we conducted epigenome-wide association analyses including data from 255 mother-child pairs within the INMA project and performed a follow-up functional analysis to help the interpretation of the findings. After multiple-testing correction, we found that methylation at 4 CpGs (cg1548200, cg02986379, cg00866476 and cg14113931) was significantly associated with the general cognitive score, and 2 distinct differentially methylated regions (DMRs) (including 27 CpGs) were significantly associated with each cognitive dimension. <a name="_Hlk168395284"></a><a name="_Hlk168394861"></a><span>Interestingly, the genes annotated to these CpGs, <span>such as <em>DAB2, CEP76</em>, <em>PSMG2,</em> or <em>MECOM,</em> </span>are involved in placenta, foetal, and brain development</span><span>. </span>Moreover, functional enrichment analyses of suggestive CpGs (<em>p</em><1x10<sup>-4</sup>) revealed gene-sets involved in placenta development, foetus formation and brain growth. These findings suggest that placental DNAm could be a mechanism contributing to the alteration of important pathways in the placenta that have a consequence on the offspring’s brain development and cognitive function. </span></span></p>
Assessing Student Sustainable Learning Engagement in Mobile Learning through Social Cognitive Theory and Social Learning Theory
<p><span>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em>.</span><span> the data was collected as part of ODDEA WP2.</span></p>
The dataset of article "Early Detection of Cognitive Impairment in End-Stage Renal Disease Patients Undergoing Hemodialysis: Insights from Resting-State Functional Connectivity Analysis"
<p>This is a file as dataset of the article "Early Detection of Cognitive Impairment in End-Stage Renal Disease Patients Undergoing Hemodialysis: Insights from Resting-State Functional Connectivity Analysis".</p> <p>It includes fMRI brain imaging data of subjects included in the case group (ESRD group) and healthy control group (HC group).</p>
Cognitive and neural state dynamics of narrative comprehension
<p>Functional MRI data during temporally scrambled movie watching</p>
Research on Cognition in Software Engineering
<p>This dataset includes the primary studies selected for literature review on cognition in software engineering. </p>
Belief, Affect, and Cognitive Dissonance in a Simulated Election - Main Study Data Set
<p>Data gathered using Amazon's Mechanical Turk (MTurk) task platform and the Qualtrics survey platform for a simulated election experiment focusing on belief change and affect in response to repeated counterattitudinal information exposure. </p>
An Exploratory Study on the Effect of Virtual Environments on Cognitive Performances and Psychophysiological Responses
<p>This is the dataset of the following study: </p> <p>Frigione, I., Massetti, G., Girondini, M., Etzi, R., Scurati, G. W., Ferrise, F., Chirico, A., Gaggioli, A., & Gallace, A. (2022). An Exploratory Study on the Effect of Virtual Environments on Cognitive Performances and Psychophysiological Responses. <em>Cyberpsychology, behavior and social networking</em>, <em>25</em>(10), 666–671. https://doi.org/10.1089/cyber.2021.0162</p> <p>Abstract: Research shows that reduced exposure to natural contexts is associated with an increase in psychophysical disorders. Recent evidence suggests that even a brief experience in natural scenarios can positively affect people's health and well-being. However, natural contexts are not always easily accessible. This study investigates the effects of natural and indoor virtual environments (VREs) on psychophysiological and cognitive responses. Following a within-subject design, 34 healthy participants were exposed to two VREs (i.e., a forest and a living room) in a counterbalanced order through a head-mounted display (Oculus Rift). Participants were asked to explore the scenarios and execute a modified version of the Paced Auditory Serial Addition Test. Physiological parameters (heart rate, skin conductance level [SCL], and respiration rate) were recorded during the whole session. After the exposure to VREs, participants filled a set of visual analog scales to rate their subjective experience of presence, relaxation, and stress. Participants reported a higher perceived sense of relaxation in the virtual forest. Moreover, their SCLs were significantly higher in this environment, showing that the forest elicited higher physiological arousal than the living room. Furthermore, their SCLs were significantly higher during the attentional task in the virtual living room. The results suggest that a natural virtual environment can make people feel more relaxed and physiologically engaged than an indoor scenario. The latter instead can be linked to a performing venue, as reported for real contexts. However, these changes were not related to modulations of attentional performance.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.