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48 results for “mental models”
DATA to support Dyrk1a function in glutamatergic neurons in mouse models of Mental Retardation Disease 7 (MRD7) and Down syndrome (or trisomy 21)
<p>Four datasets are provided here to support the function of Dyrk1a in glutamatergic neurons in mouse models of Mental Retardation Disease 7 (MRD7) and Down syndrome (or trisomy 21):</p> <p>- RNAseq data to compare hippocampal expressed genes at postnatal day 30, in the complete inactivation of Dyrk1a in glutamatergic neurons using a Dyrk1a floxed-allele and the Camk2:Cre transgene</p> <p>- data from all the figures</p> <p>-data from all the supplementary figures </p> <p>-data from the quantitative proteomic analysis made from hippocampal extract of wt, Dyrk1a heterozygote, Dp(16)1Yey and Dp(16)1Yey with only two functional copies of Dyrk1a</p> <p>Detailed information are available in the article by Brault et al 2021, deposited in Biorachiv https://doi.org/10.1101/2021.05.01.442242 </p>
Mental Models
<p>Raw survey data from 3 studies of academic teachers regarding Mental Models towards online teaching</p>
Data from: Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning
<p>How the brain representation of conceptual knowledge vary as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. Here we asked how the brain representation of semantic categories is shaped by the depth of processing during mental simulation. Participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models - associated with the image referents of the words - and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.</p>
Dataset: A Study on the Mental Models of Users Concerning Existing Software
<p>In 2022, we conducted a study on the mental models of users concerning existing software.</p> <p>Information on the execution of the study are presented in the paper linked below:</p> <p>https://doi.org/10.1007/978-3-030-98464-9_18</p>
Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling
<p>This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: "Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621". This manuscript has been published in Plos One journal.</p> <p>This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy.</p> <p>Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.</p>
Do Large Language Models Have a Personality? A Psychometric Evaluation with Implications for Clinical Medicine and Mental Health AI Dataset
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Implementing a Blended Care Model That Integrates Mental Healthcare and Primary Care Using Telemedicine and Care Management for Patients With Depression or Alcohol Use Disorder in Small Primary Care C
ClinicalTrials.gov study NCT02713217. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling
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Data from: Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning
Open the record for dataset details and reuse information.
Hyperbrain features of team mental models within a juggling paradigm: a proof of concept
<p>To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers' cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 Hz and 8-12 Hz). Graph metrics were used to typify the topology and efficiency of the functional networks.</p> <p>The datasets uploaded are the two dataset of both jugglers used for this study.</p>
Developers' Visuo-spatial Mental Model and Program Comprehension
<p>Dataset for the paper entitled: "Developers' Visuo-spatial Mental Model and Program Comprehension"</p>
Effects of an Integrative Treatment Model to Reduce Anxiety and Depression in Minor Mental Health Problems and Medically Unexplained Symptoms
ClinicalTrials.gov study NCT01631500. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effectiveness and Cost-effectiveness of the New Orleans Intervention Model for Infant Mental Health
ClinicalTrials.gov study NCT02653716. IPD Sharing: Not stated. Countries: 1. Publications: 2.
An Innovative Model of Pediatric Acute Mental Health and Addictions Care
ClinicalTrials.gov study NCT04292379. IPD Sharing: NO. Countries: 1. Publications: 2.
Development of a Model for Digital Monitoring of the Mental State of the Hospitalized Patient
ClinicalTrials.gov study NCT06182787. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Evaluation of a Cross-sectional Coordinated, Severity Stepped, Evidence-based Care Model for Mental Disorders
ClinicalTrials.gov study NCT03459664. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Collaborative Mental Health Care Model: an Evaluation of the Implementation of a Pilot in Four Primary Care Organization
ClinicalTrials.gov study NCT05385666. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Shared Mental Models and Technical Assessment in Video Assisted Thoracoscopy Surgery Lobectomies
ClinicalTrials.gov study NCT02999113. IPD Sharing: NO. Countries: 1. Publications: 1.
Establishment of Sleep Quality, Physical and Mental Health and Occupational Burnout Management Model for Shift Nursing Staff and Evaluation of Its Effectiveness
ClinicalTrials.gov study NCT04423328. IPD Sharing: NO. Countries: 1. Publications: 1.
Integrating the Quit and Stay Quit Monday Model Into Smoking Cessation Services for Smokers With Mental Health Conditions
ClinicalTrials.gov study NCT04512248. IPD Sharing: YES. Countries: 1. Publications: 1.
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.