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344 results for “Neuroimaging”
A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
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Naturalistic Neuroimaging Database
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Neural correlates of the LSD experience revealed by multimodal neuroimaging
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Neuroimaging predictors of creativity in healthy adults
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Comparing Perturbation Modes for Evaluating Instabilities in Neuroimaging: Processed NKI-RS Subset (08/2019)
<p>The processed subset of the NKI-RS dataset for evaluation of various perturbation modes when studying instabilities. Linked to the <a href="https://arxiv.org/abs/1908.10922">pre-print found here</a>, can be visualized using the <a href="https://github.com/gkiar/stability-mca/blob/master/code/dipy_exploratory/mca_dipy_exploratory_analysis.ipynb">plotting code here</a>, and generated with various <a href="https://github.com/gkiar/stability/tree/master/code/experiments/paper0_comparing_perturbation_modes">scripts and launch configurations found here</a>.</p>
Princeton Handbook for Reproducible Neuroimaging: Sample Output
<p>This archive contains sample output files for the <a href="http://doi.org/10.5281/zenodo.3677090">sample data</a> accompanying the <a href="https://brainhack-princeton.github.io/handbook/">Princeton Handbook for Reproducible Neuroimaging</a>. Outputs include the NIfTI images converted using <a href="https://github.com/nipy/heudiconv">HeuDiConv</a> (v0.8.0) and organized according to the <a href="https://bids.neuroimaging.io/">BIDS</a> standard, quality control evaluation using <a href="https://mriqc.readthedocs.io/">MRIQC</a> (v0.15.1), data preprocessed using <a href="https://fmriprep.readthedocs.io/en/stable/">fMRIPrep</a> (v20.2.0), and other auxiliary files. All outputs were created according to the procedures outlined in the handbook, and are intended to serve as a didactic reference for use with the handbook. The sample data from which the outputs are derived were acquired (with informed consent) using the<a href="https://github.com/ReproNim/reproin"> ReproIn</a> naming convention on a Siemens Skyra 3T MRI scanner. The sample data include a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by <a href="https://doi.org/10.1177%2F0956797616682029">Yeshurun et al., 2017</a> and is available as part of the<a href="https://openneuro.org/datasets/ds002345"> Narratives</a> collection, and the emotional faces task is similar to<a href="https://doi.org/10.1016/j.nicl.2015.05.004"> Chai et al., 2015</a>. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
Survey on Open Science Practices in Functional Neuroimaging. Dataset and Materials
<p>Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. A PubMed search with the search terms ("fMRI" OR "functional magnetic resonance imaging" OR "functional Magnetic Resonance Imaging") was done to collect email addresses from corresponding authors of scientific articles published between 2010/01/01 and 2020/08/28. An email was sent to 14,690 addresses on 2020/01/12 with an invitation to participate, including a personalized link to the survey. If the recipients did not click the link or did not complete the survey after 14 days, they received a single reminder email. The questionnaire was composed of five building blocks. The Blocks 1-3 focused on three areas of open science practices: data structure, preregistration and data sharing. The fourth block asked about technical expertise with software and the fifth part assessed sociodemographic data.</p>
Supplementary material: Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium
<p>The ridge plots attached here accompany the supplement to the manuscript <em>Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium</em> (Dzinalija et al., 2024)<em>. </em>These are full results of Figures 1, 2C, and 3C of the manuscript and Figures S3, S5, S7 and S9 of the supplement depicting whole-brain Bayesian multilevel models run using the Regional Bayesian Analysis toolbox (RBA; Chen et al., 2019). Whole-brain analyses were parcelated into the Schaefer-Yeo 7-network 200-parcel cortical atlas (Schaefer et al., 2018) and the Melbourne 32-region subcortical atlas (Tian et al., 2020). Results are presented according to contrast of interests: [Negative > Neutral], [OCD > Neutral], [Threat > Neutral], and [OCD > Threat], and effects of interest: [Diagnosis = OCD or HC], [MED = medication], [AO = age of onset], [YBOCS = OCD severity], and [Intercept = task effect]. </p> <p>P+ values denote the probability that there is increased brain activation in a given region of the Schaefer 200-parcel 7-network cortical atlas and Melbourne 32-region subcortical atlas. We used the guidelines proposed by Chen et al. (2019) to infer credibility of evidence, namely taking a positive posterior probability (P+) of <0.10 or >0.90 as indication of moderate evidence and, <0.05 or >0.95 or <0.025 or >0.975 as strong or very strong evidence, respectively. To interpret the pairwise comparisons presented as Group1-vs-Group2, posterior distributions to the right of the green no-effect line represent regions in which individuals in Group 1 show credible evidence for higher activation than individuals in Group 2. Regions with posterior distributions to the left of this line show credible evidence for higher activity in Group 2 than in Group 1. (Darker) red color represents regions in which individuals in Group 1 show moderate-to-very-strong evidence for higher activation than Group 2. (Darker) blue color represents regions in which Group 2 show moderate-to-very-strong evidence for higher activation than Group 1. Grey color represents regions in which there is no strong evidence of a difference between Group 1 and Group 2. Schaefer-Yeo 200-parcel atlas name abbreviations can be retrieved <a href="https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations">via this link.</a></p>
Raw and processed autofluorescence neuroimaging data
<p>Raw and processed <em>in vivo</em> mouse whole-cortex autofluorescence imaging data.</p> <p>This release accompanies the pub <a href="https://doi.org/10.57844/arcadia-b963-15ac">"Label-free neuroimaging in mice captures sensory activity in response to tactile stimuli and acute pain</a>.</p> <p>The raw data is split into five zipfiles because it is quite large (70GB total). Each of these five zipfiles corresponds to one of the five subdirectories in the single zipfile of processed data.</p>
An Automatic Neuroimaging Infrastructure For Synthesis and Analysis of Structural MRI Data
<p>We have designed, implemented and distributed a fully automatic neuroimaging infrastructure for the synthesis and analysis of structural magnetic resonance imaging (MRI) data. The framework provides a concrete environment for the quantitative validation of various methods for the analysis of brain asymmetries, for comparisons of methods and measures of brain shape asymmetry, and possibly for clarifying contradicting neuroimaging findings of brain lateralizations.</p> <p>See <a href="https://sites.google.com/site/brainmorphorg/home">https://sites.google.com/site/brainmorphorg/home </a></p> <p>and </p> <p>A. Pepe, I. Dinov, and J. Tohka . An Automatic Framework for Quantitative Validation of Voxel Based Morphometry Measures of Anatomical Brain Asymmetry. <a href="http://dx.doi.org/10.1016/j.neuroimage.2014.06.029">NeuroImage , 100: 444 - 459, 2014</a><a href="https://doi.org/10.1016/j.neuroimage.2014.06.029"> </a></p> <p>for more information. </p>
Dataset of the study: Explaining recovery from coma with multimodal neuroimaging
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Multicenter dataset of neuroimaging features (part II)
<p>The CSV file contains the cortical thickness (CT) and fractal dimension (FD) estimated from the brain MR T<sub>1</sub>-weighted images contained in the multicenter online repository <em>Information eXtraction from Images (<a href="https://brain-development.org/ixi-dataset/">IXI</a>)</em> study.</p> <p>Each CSV file contains the following columns:</p> <ul> <li>Subject: id of each subject</li> <li>SITE: imaging site label (ABIDEI or ABIDEII followed by the institution name that collected the images; ICBM; NKI2)</li> <li>Age: each subject's age, expressed in years</li> <li>Sex: 0=male; 1=female.</li> <li>cortex_CT (FD): CT (or FD) of the cerebral cortical gray matter (GM).</li> <li>lh_cortex_CT (FD), rh_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM.</li> <li>lh_frontal_cortex_CT (FD), rh_frontal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the frontal lobe.</li> <li>lh_temporal_cortex_CT (FD), rh_temporal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the temporal lobe.</li> <li>lh_parietal_cortex_CT (FD), rh_parietal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the parietal lobe.</li> <li>lh_occipital_cortex_CT (FD), rh_occipital_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the occipital lobe.</li> </ul>
Emotional imagination of negative situations: functional neuroimaging in anorexia and bulimia
<p>Clinical dataset (SPSS 20 file) 64 subjects.</p> <p>fMRI con images 64 subjects described in the clinical dataset.</p> <p> </p>
Proactive Control Strategies for Overt and Covert Go/NoGo Tasks: an Electrical Neuroimaging Study
<p>ERPs dataset (15 subjects) for session A and session B preparatory phase (from 200 ms before cue onset to 1000 ms after cue onset).</p> <p>GSN Hydrocel Sensor Net 110 channels array is included.</p>
Supplementary material 1: Animation of BrainBox's workflow from: Open Neuroimaging Laboratory - Research Ideas and Outcomes 2: e9113 (08 May 2016) https://doi.org/10.3897/rio.2.e9113
Animation showing the different functionalities of BrainBox: opening a Magnetic Resonance Imaging volume, viewing it, and editing it collaboratively online.
Supplementary material 2: BrainBox Cartoon from: Open Neuroimaging Laboratory - Research Ideas and Outcomes 2: e9113 (08 May 2016) https://doi.org/10.3897/rio.2.e9113
Cartoon showing the idea of distributed collaboration on Magnetic Resonance Imaging data.
Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnosis of Depression: Data and code
<p><strong>In the archive are the datasets and code used to create the results in article "Application of Mass Multivariate Analysis on Neuroimaging Data Sets fo Precision Diagnosis of Depression". The methods is based on the Multivariate Linear toolbox mad F. Kherif’s lab and available on GitHub. <a href="https://github.com/LREN-CHUV/MLM">https://github.com/LREN-CHUV/MLM</a>. </strong></p> <p><strong>A total of 44 patients with a current psychotic (n=19) or depressive (n=25) episode were analyzed by MLM (details can be found in the article itself) utilizing resting sate fMRI, tasks fMRI, and anatomical T1w images. </strong></p> <p><strong>The data results are provided in MATLAB format, in a matrix called MLM.mat. The matrix includes the canonical variables for each of the three imaging modalities, chi-square statistics, and p-values for each brain region. Additionally, we provided a function MLM_plot_fun that allows mapping the statistics results to a canonical three-dimensional brain as per the article. </strong></p>
raw behavioral data, raw questionnaire data, and raw neuroimaging data for a genuine-and-pretended disgust task
<p>Raw behavioral data, raw questionnaire data, and raw neuroimaging data for a genuine-and-pretended disgust task. Particularly, a document indicating all parameters used in the datasets and file names is attached as well.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 4. Histogram age distributions of the 100 Schizophrenia patients illustrating clusters of patients at 20, 30, 40, and 50 years old.
<p>The figure below is a histogram distribution of the ages of one hundred Schizophrenia<br> patients in this study. There appears to be a cyclical peak every ten-years cycles at 20, 30, 40, and<br> 50-year-old patients. This may suggest a recent finding that CD8 T cells, which play a pivotal role<br> in mediating long-term immunity to Toxoplasma, are down-regulated in schizophrenia patients<br> (Bhadra et al., 2013). Based on the aforementioned statements, a vaccine for Toxoplasmosis may be<br> difficult to achieve, but the treatment and screening possibilities in patients with psychosis are<br> available today.</p>
BRAIN Journal-Isolating the Norepinephrine Pathway Comparing Lithium in Bipolar Patients to SSRIs in Depressive Patients-Figure 2. Resting state neuroimaging findings illustrating the action of Lithium following whole brain
<p>The axial, saggital, and coronal MRI activation maps illustrate increased delta frequency band neuronal activity in the 46 patients<br> diagnosed with Bipolar Affective Disorder compared to 32 female patients diagnosed with Major Depressive Disorder of Depressive<br> Episode. The Yellow/Orange shades indicate increased neuronal activity in the right Superior Frontal Gyrus (t=0.920, p=0.05060,<br> BA 6, MNI X=20, Y=0, Z=70) and in the right Cingulate Gyrus (t=0.0846, BA 24, MNI X= 5, Y=0, Z=51). Structural anatomy is<br> shown in grey scale (A – anterior; S – superior; P – posterior; L – left; R – right).</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.