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Dataset results
38 results for “network attention”
Posterior default mode network activity underlies distracted interoceptive attention and provides an objective basis for low mindfulness and depression
<p>Interoceptive attention to internal sensory signals is fundamental to mindfulness. However, it is difficult to measure objectively, and the vast majority of investigations of the neural correlates of mindfulness-based attention rely on subjective and retrospective measures of distraction. In this study, we measured consistency of response times on a breath-monitoring task (instruction to respond every 2 breath cycles) to objectively measure interoceptive attention in individuals across the lifespan (15–91 years of age, n=324). Consistently timed responses were indicative of on-task attention, whereas variable delayed responses indicated distraction or mind-wandering. Signifying the importance of interoceptive attention consistency, we observed that this measure was positively correlated across subjects with performance across several exteroceptive cognitive tasks. Electroencephalographic (EEG) source reconstruction demonstrated that distraction on the eyes-closed task was associated with elevated alpha band (8–12 Hz) brain activity, particularly in posterior default mode network (pDMN) brain regions. The neural data revealed four important findings: 1) pDMN brain activity was inversely linked with functional connectivity to the fronto-parietal network (FPN), suggesting that pDMN-FPN interactions play a key role in the regulation of pDMN activity during interoceptive attention. 2) pDMN activity on distracted trials was inversely correlated with trait-level mindfulness. 3) A key hub of the pDMN, the isthmus region of the posterior cingulate cortex (ICC), showed elevated activity on distracted trials in individuals with self-reported depressive symptoms. 4) In an independent second experiment, pDMN activity was adaptively modulated by task difficulty that invoked different levels of distraction. These results identify an EEG-based neurophysiological marker (elevated pDMN alpha activity) underlying interoceptive distraction, which is correlated with subjective markers of mindfulness and depression and that can be adaptively modulated. Our findings, thus, provide a direct link between a neurophysiological substrate of interoceptive attention, and subjective ratings of mindfulness and depressed mood.</p>
Our processed EndoVis2017 dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed EndoVis2017 dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Our processed Kvasir-Instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed Kvasir-Instrument dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Social Cognition,Attentional Network and Nicotine Drug Dependency - A Pharmacological Clinical Trail
ClinicalTrials.gov study NCT00618280. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Interactions Between Attentional Networks and Their Influence on Perception
ClinicalTrials.gov study NCT02467114. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Investigation of Intranasal Ketamine Related Changes in Attentional Brain Networks With Functional and Structural MRI
ClinicalTrials.gov study NCT05320120. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neuroplasticity in an Extended Amygdala Network as a Target Mechanism for Attention Bias Modification Outcome
ClinicalTrials.gov study NCT03092609. IPD Sharing: YES. Countries: 1. Publications: 2.
Modulation of posterior default mode network activity during interoceptive attention and relation to mindfulness
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Double attention recurrent convolution neural network for answer selection
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A brief exposure to rightward prismatic adaptation changes resting-state network characteristics of the ventral attentional system
<p>A brief session of rightward prismatic adaptation (R-PA) has been shown to alleviate neglect symptoms in patients with right hemispheric damage, very likely by switching hemispheric dominance of the ventral attentional network (VAN) from the right to the left and by changing task-related activity within the dorsal attentional network (DAN). We have investigated this very rapid change in functional organisation with a network approach by comparing resting-state connectivity before and after a brief exposure i) to R-PA (14 normal subjects; experimental condition) or ii) to plain glasses (12 normal subjects; control condition). A whole brain analysis (comprising all 129 regions of interest) highlighted R-PA-induced changes within a bilateral, fronto-temporal network, which consisted of 13 nodes and 11 edges; all edges involved one of 4 frontal nodes, which were part of VAN. The analysis of network characteristics within VAN and DAN revealed a R-PA-induced decrease in connectivity strength between nodes and a decrease in local efficiency within VAN but not within DAN. These results indicate that the resting-state connectivity configuration of VAN is modulated by R-PA, possibly by decreasing its modularity.</p>
Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism
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Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2.</p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 1
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 1. Part 2 accessible here: <a href="https://doi.org/10.5281/zenodo.3719225">https://doi.org/10.5281/zenodo.3719225</a></p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Effects of High-intensity Interval Training and Moderate-intensity Continuous Training on Functioning of Attentional Networks and Heart Rate Variability in Healthy Adults
ClinicalTrials.gov study NCT06602700. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of Stability of Behavioral and Neural Measures of Attention Networks Across Multiple Sessions
ClinicalTrials.gov study NCT04033731. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification datasets of OPM- MEG signal artifacts based on channel attention convolution network
<p>In order to realize the automatic identification and removal of OPM-MEG artifacts, we created this data set based on OPM magnetometer reference signal and OPM-MEG signal itself. If you need to use this data set, you need to contact the author for permission. Contact email: by2017335@buaa.edu.cn.</p>
Structure classification of glass-forming liquids by graph neural networks: Explaining predictions with the Self-Attention mechanism
<p>This repository includes the dataset and Python scripts used in the article, "Structure classification of glass-forming liquids by graph neural networks: Explaining predictions with the Self-Attention mechanism". The repository also includes source data of figures in the article.</p>
Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder
<p>Here is the pre-processed EEG dataset for ADHD and the Healthy control group while they were facing four facial emotions (Angry, Happy, Neutral, and Sad). They saw 60 images of facial expression for each emotion.</p><p>Each participant's data is put in a folder named from P1 to P51.</p><p>P31 and P43 have been removed from the data list due to noisy data.</p><p>P15 to P17, P22 to P43, and P51 are labeled as ADHD group and the rest are healthy group.</p><p>In each folder (e.g., P1) there are five folders. A refers to Angry epochs, H refers to happy epochs, N refers to neutral epochs, and S refers to Sad epoch, as well as the channel location.</p><p>The matrix for each emotion is with the size of 62*1536*60 referring to channels*samples*epochs.</p><p>Cite:</p><p>Dini, H., Farnaz.Ghassemi & Sendi, M.S.E. Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder. <i>Brain Topogr</i> <strong>33</strong>, 733–750 (2020). https://doi.org/10.1007/s10548-020-00794-1</p>
ScienceDex guides
Understand access before you commit
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.