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65 results for “Virtual Brain”

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zenodo44/100

Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"

<p>Dataset belonging to the behavioral and neurophysiological data of the publication: &quot;Providing task instructions during motor training enhances performance and modulates attentional brain networks&quot;. The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>

opencc-by-4.0Jul 2021View details →
zenodo44/100

Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics

<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years).&nbsp; The cohort was 68% male and 32% female.&nbsp; In terms of education, 16% had attended only high school, while 32% had a bachelor&#39;s degree, 42% a master&#39;s degree, and 11% a doctorate. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p><strong>Experiment Description:</strong></p> <p>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>&mdash;a VR environment that renders virtual arms from a first-person perspective&mdash;while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals have been recorded synchronously in a bipolar montage, and connected to the EEG amplifier&rsquo;s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> &nbsp;</p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p>&nbsp;</p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand&nbsp;</td> </tr> <tr> <td>S08</td> <td>class2, Right hand&nbsp;</td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p>&nbsp;</p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- USER #<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---SESSION #<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---TASK #<br> |&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; +---MI<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMO<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMOHP<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMOVR<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMOHPVR<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; +---ME<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk</p> <p>&nbsp;</p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2022View details →
zenodo40/100

The Virtual Macaque Brain: A macaque connectome for large-scale network simulations in TheVirtualBrain

<p>A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing&nbsp;and diffusion-weighted imaging&nbsp;data. Created for modeling&nbsp;brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org</p>

opencc-by-4.0Oct 2018View details →
zenodo40/100

BRAIN Journal - Lamport's algorithm - Figure 2 from paper "Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time"

<p>Figure 2. Lamport&rsquo;s algorithm</p> <p>In order to synchronize logical clocks, Lamport [3] defined the relationship &ldquo;happened before&rdquo; (preceded) which implies that the expression 1 2 a &rarr; a means &ldquo; 1 a occurred before 2 a &rdquo;, and it means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took place. This relation can be directly observed in two situations (figure 2): 1. If two events happen during the same process, the order of the happening is indicated by the common clock; 2. When two processes communicate through a message, the event that corresponds to sending the precise message always happens before the event of receiving it (i.e. the message). If two events, 1 a and 2 a , are produced in different processes that do not exchange messages (neither directly nor indirectly), then it is not certain if 1 2 a &rarr; a or 2 1 a &rarr; a . In this case it is said that these events are competitive, which means that it is not known which one happened first (and it is not a must-know thing either).</p>

opencc-by-4.0Dec 2017View details →
zenodo40/100

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 19. AnyLogic Implementation of Decision-Making Architechture in an Virtual Autonomous Agent Environment

<p>Figure 19 shows a screenshot of the test implementation. The picture in the middle shows the modules and interfaces<br> of the decision-making architecture which were realized by so-called &ldquo;active objects&rdquo; and &ldquo;ports&rdquo;.<br> On the left side, the implemented modules are listed. In the right lower corner of the figure, the<br> agents and the virtual environment are displayed. The environment comprises different &ldquo;objects&rdquo;<br> (food sources, obstacles, predators, other agents, etc.). In order to survive, the agents have to access<br> food sources. However, the accessing of food sources bears difficulties and risks.</p>

opencc-by-4.0Oct 2013View details →
zenodo40/100

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 6. Virtual Space on Panoramio+ with the experiments carried in Vădastra village

<p>The fourth stage was the use of social media to build a social learning network based on content and experience sharing and creation. The following web 2.0 services were used as a distributed platform able to support our experimental learning system: a) Panoramio (http://www.panoramio.com/user/7606828) (Figure 6) as a geo-referenced photo-sharing service over Google Maps and Google Earth for sharing project&rsquo;s essential results; b) Twitter (https://twitter.com/maps_of_time) (Figure 7), as a social network and micro-blogging service for short announcements and comments; c) Google+ (https://plus.google.com/114705936110835992130?hl=en#114705936110835992130/posts?hl=en) (Figure 8), as a platform for sharing and tagging multiple content (photo, video), blogging and&nbsp;video chatting service with the recent Google Hangout, for sharing educational content; d) Google Drive (Levin, 2013) for cloud storage and collaborative document editing. We also created a YouTube channel for public distribution of video content (https://www.youtube.com/TimemapsNet), (Rusu et al., 2013) and a Facebook page of Vădastra School (https://www.facebook.com/scoalaVădastra)</p>

opencc-by-4.0Jun 2016View details →
zenodo40/100

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 8. Virtual social space on Google+

<p>For micro-blogging we hashtagged the main topic as #maps_of_time and created keywords related to three ancient technologies specific for the studied contexts (textiles, glass, ceramics) to facilitate a categorization of the topics and their retrieval. To achieve a unified and coherent platform, the personal spaces of the social networks were customized with logos and landing pages, designed by Associate Professor Marina Theodorescu (NUA).&nbsp;&nbsp;</p>

opencc-by-4.0Jun 2016View details →
dryad36/100

Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology

<p><span>H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we provided deep convolutional neural networks, called MRH-Nets, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. </span><span> </span>The dataset contains multi-contrast MRI and histology used for the training and testing and the acquisition parameters. The datasets have been carefully registered to mouse brain images from the Allen Mouse Brain Atlas (https://mouse.brain-map.org). The source codes for MRH-Nets can be found at <a href="https://github.com/liangzifei/MRH-Net">https://github.com/liangzifei/MRH-Net</a>.</p>

opencc-zeroJan 2022View details →
ClinicalTrials.gov36/100

Immersive Functional Virtual Reality in People With Acquired Brain Injury and Unilateral Spatial Neglect

ClinicalTrials.gov study NCT07017140. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Immersive Virtual Reality (VR) at the Time of Clinical Evaluation to Improve Psychological Distress and Anxiety in Primary Brain Tumor (PBT) Patients

ClinicalTrials.gov study NCT04301089. IPD Sharing: YES. Countries: 1. Publications: 7.

controlledIPD-YESFeb 2026View details →
dryad36/100

Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology

Open the record for dataset details and reuse information.

publicJan 2022View details →
dryad32/100

First virtual endocast description of an early Miocene representative Pan-Octodontoidea (Caviomorpha, Hystricognathi) and considerations on the early brain evolution in South American rodents

<p><span>The study of the cranial endocast provides valuable information to understand the behavior of an organism since it coordinates sensory information and motor functions. In this work, we describe for the first time the anatomy of the encephalon of an early Miocene pan-octodontoid caviomorph rodent (<em>Prospaniomys</em> <em>priscus</em>) found in the Argentinean Patagonia, based on virtual 3D endocast. This fossil rodent has an endocast morphology here considered ancestral for Pan-Octodontoidea and also other South American caviomorph lineages, such as an encephalon with anteroposteriorly aligned elements, mesencephalon dorsally exposed, well-developed vermis of the cerebellum, rhombic cerebral hemispheres with well-developed temporal lobes. <em>Prospaniomys</em> also has relatively small olfactory bulbs, large paraflocculi of the cerebellum, low endocranial volume, and a degree of neocorticalization. Its EQ is lower compared with Paleogene North American and European non-caviomorph rodents, but slightly higher than several late early and late Miocene caviomorphs. The paleoneurological anatomical information supports the hypothesis that <em>Prospaniomys</em> was a generalist caviomorph rodent with terrestrial habits, and enhanced low-frequency auditory specializations. The scarce paleoneurological information indicates that several endocast characters in caviomorph rodents could change with ecological pressures. This work sheds light on the anatomy and evolution of several paleoneurological aspects of this particular group of South American rodents. </span></p>

opencc-zeroNov 2022View details →
ClinicalTrials.gov32/100

Neurocognitive Driving Rehabilitation in Virtual Environments (NeuroDRIVE) as an Adjunctive Intervention for Traumatic Brain Injury

ClinicalTrials.gov study NCT02411227. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Differences in Brain Activity in the Application of Upper Limb Exercise Tasks Through Virtual Reality

ClinicalTrials.gov study NCT05743296. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Virtual Reality (VR) Treatment for Balance Problems in Traumatic Brain Injury (TBI)

ClinicalTrials.gov study NCT01794585. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Virtual Reality (VR) -Directed Brain Gut Behavioral Treatment (BGBT) for Inflammatory Bowel Disease (IBD) Inpatients

ClinicalTrials.gov study NCT06188793. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Evaluation of the Impact of Laterality on Brain Activation During a Virtual Mirror Therapy Task in Healthy Subjects

ClinicalTrials.gov study NCT05793762. IPD Sharing: Not stated. Countries: 1. Publications: 10.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Virtual Reality vs Traditional Cognitive Training in Patients With Severe Acquired Brain Injury

ClinicalTrials.gov study NCT06474871. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Using Virtual Technologies to Prevent Injuries in Adolescents With Acquired Brain Injury

ClinicalTrials.gov study NCT04768946. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Feasibility and Acceptability of a Virtual 'Coping With Brain Fog' Intervention for Young Adults With Cancer

ClinicalTrials.gov study NCT05115422. IPD Sharing: NO. Countries: 1. Publications: 12.

closedIPD-NOFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record