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1,039 results for “brains stimulation”
The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex
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Modeling an auditory stimulated brain under altered states of consciousness using the generalized ising model
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Effects of ON/OFF deep brain stimulation on cognitive control in treatment-resistant depression (EEG)
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Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
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Finite element method (FEM) models for translational research in non-invasive brain stimulation
<p>Finite element method (FEM) models for non-invasive brain stimulation modeling using SimNIBS or other compatible software.<br> The mouse and monkey models are described in detail in Alekseichuk et al., Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human, NeuroImage 2019.<br> The Petri dish model follows a typical experimental setup for in-vitro TMS, similar to what is described in Lenz et al. Repetitive magnetic stimulation induces plasticity of inhibitory synapses, Nature Communications 2016.<br> <br> The following files are included:<br> 1. Brain tissue slice in a Petri dish.<br> 2. Normal adult male nude mouse "Digimouse" (brain volume of 0.38 cm3).<br> 3. Normal adult male capuchin monkey "S" (brain volume of 68.31 cm3).<br> <br> The models include the following tissues (coded with numbers):<br> 1. White matter volume<br> 2. Grey matter volume<br> 3. CSF volume<br> 4. Skull volume<br> 5. Soft tissues volume<br> 8. Eyeballs volume<br> 1001. White matter outer surfaces<br> 1002. Grey matter outer surfaces<br> 1003. CSF outer surfaces<br> 1004. Skull outer surfaces<br> 1005. Soft tissues outer surfaces<br> 1008. Eyeballs outer surfaces<br> <br> With any questions, please, contact the corresponding authors of the relevant papers or <a href="mailto:aopitz@umn.edu">aopitz@umn.edu</a> (Alexander Opitz).</p>
Micro-CT images of deep brain stimulation leads
<p>The dataset contain micro-CT images of leads used in deep brain stimulation. A lead comprises multiple electrodes and enables the delivery of electrical pulses to the brain to treat medical conditions such as Parkinson's disease, essential tremor or epilepsy. Images were acquired with a Skyscan 1276 micro-CT system from Bruker. Each image is provided in Nifti format (.nii) along with its corresponding log file (.log) generated by the scanner. The file names indicate the manufacturer and sample model. 'BS' denotes Boston Scientific.<br><br>Images can be visualized at:<br>https://activgroup.github.io/DBS-lead-microCT/<br><br>To contribute, please contact thomas.billoud@uniklinik-freiburg.de</p>
Dataset related to article"Globus Pallidus Internus Deep Brain Stimulation Using Frame-Based vs. Frameless Stereotaxy in Dystonia: A Single-Center Experience"
<p>Demographic, clinical (BFMDRS at baseline and in follow up), implantation (frame and frameless) data for the sample considered in the study.</p>
Transcranial direct current stimulation (tDCS) over the left prefrontal cortex does not affect time-trial self-paced cycling performance: Evidence from oscillatory brain activity and power output.
<p>This research will shed new light into the bidirectional relationship between acute aerobic exercise, brain and cognition. This is based on the particular role of executive (cognitive) function during exercise. The rationale of our study is that stimulation of the prefrontal cortex that has been repeatedly associated with executive function, would facilitate or impair self-paced aerobic exercise. This would also affect cognitive performance immediately after exercise. We will use a modified flanker’s task as a form of assessing executive function (see below for further details). The flanker’s task implies two different stimuli, one congruent and one incongruent. Relative to “congruent” stimuli, these “incongruent” stimuli are usually accompanied by increased response times (RTs) and decreased accuracy. To stimulate the prefrontal cortex, we use transcranial direct-current stimulation (tDCS). tDCS is able to induce cortical changes by hyperpolarizing (anodal) or depolarizing (cathodal) neuron’s resting membrane potential.<br> Therefore, the hypotheses of this research are:<br> 1) Anodal stimulation (relative to sham and cathodal stimulation) will improve self-paced aerobic exercise and, consequently it will also improve subsequent cognitive performance.<br> 2) Cathodal stimulation (relative to sham and anodal stimulation) will impair self-paced aerobic exercise and subsequent cognitive performance.<br> </p>
Data from: Spatial localization of anterior precuneus for bodily self validated with brain stimulation
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>The posteromedial cortex (PMC), comprising the precuneus, the posterior cingulate, and the retrosplenial regions, is known to be engaged in various self-referential functions. Recent observations have also suggested a link between PMC dysfunction and self-dissociation. To test the causal relevance of specific PMC locations for self- referential processing, we recruited nine neurosurgical participants with bilaterally implanted PMC electrodes. We applied focal electrical stimulation within discrete PMC sites while probing changes in the participants' subjective states. We found that, in all nine participants, the electrical perturbation of the anterior precuneus (aPCu), but not the other PMC sites, caused apparent dissociative changes in the physical and spatial bodily domain involving head, trunk, and legs. The responsive aPCu sites did not exhibit event-related activation during a cued autobiographical memory recall task. Furthermore, resting-state functional connectivity with functional Magnetic Resonance Imaging data and effective connectivity with single-pulse electrical stimulation procedures confirmed that the responsive aPCu sites were distinct from, while closely connected with, the adjacent PMC nodes of the default mode network (DMN). Based on these data, we conclude that the aPCu is a distinct functional unit within the PMC and causally important for processing self-referential information in the physical and spatial domains. Future larger-scale experimental studies are needed to explore how operations of distinct neuronal populations within the PMC are integral to various cognitive processes that require a reference to self in its various dimension.</p> </div> </div> </div> </div>
Glucose and Non-Invasive Brain Stimulation
ClinicalTrials.gov study NCT04031404. IPD Sharing: YES. Countries: 1. Publications: 1.
Follow Up Study for Treatment of Parkinson's Disease With Deep Brain Stimulation
ClinicalTrials.gov study NCT01022073. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Spatial localization of anterior precuneus for bodily self validated with brain stimulation
Open the record for dataset details and reuse information.
Datasets related to the manuscript 'Hypothalamic deep brain stimulation augments walking after spinal cord injury'
<p>The deposited datasets consist in the following:<br>Supplementary Table 1: All kinematics data and statistical analyses</p> <p>Supplementary Table 2: All differential analyses and statistics for the quantification of transcriptional activity (cFos) and spinal cord-projecting neurons (Rabies) from the contralesional (right) lateral hypothalamus (n = 3 per group).</p> <p> </p>
Human head models and populational framework for simulating brain stimulations: part 6
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 6. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 4
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 4. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 3
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 3. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 5
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 5. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 2
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 2. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 1
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 1. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p> <p> </p>
A high-resolution finite element method (FEM) human head model for non-invasive brain stimulation
<p>High-resolution finite element method (FEM) model of a human head for non-invasive brain stimulation modeling using SimNIBS or other compatible software. The original head model (Ernie) was downloaded from the tutorial dataset of <a href="http://simnibs.org">www.simnibs.org</a> and further refined in grey matter and white matter regions.</p> <p>This supplementary dataset is released as part of the NeMo-TMS toolbox (<a href="https://github.com/OpitzLab/NeMo-TMS">https://github.com/OpitzLab/NeMo-TMS</a>). Please refer to the corresponding article for more information:</p> <p>Shirinpour, S., Hananeia, N., Rosado, J., Galanis, C., Vlachos, A., Jedlicka, P., Queisser, G., & Opitz, A. (2020). Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation. <em>BioRxiv</em>, 2020.09.23.310219. <a href="https://doi.org/10.1101/2020.09.23.310219">https://doi.org/10.1101/2020.09.23.310219</a></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.