Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
566
datasets available to search
ShareScore release 0.7.1
Dataset results
566 results for “fMRI”
Learning Naturalistic Structure: Processed fMRI dataset
<p>This data was obtained from OpenNeuro as ds001545. We would like to thank the authors for their generosity in sharing their data, and we point interested users towards their paper describing its acquisition:</p> <blockquote> <p>Aly M, Chen J, Turk-Browne NB, & Hasson U (2018). Learning naturalistic temporal structure in the posterior medial network. Journal of Cognitive Neuroscience, 30(9): 1345-1365.</p> </blockquote> <p><br> <strong>Experimental design</strong></p> <p>In this dataset, subjects were scanned while watching repeated presentations of intact and scrambled clips from Wes Anderson's 2014 film, <em>The Grand Budapest Hotel</em>. Scrambled clips were presented in either a 'fixed' (i.e., consistent scrambling from run to run) or 'random' (i.e., random scrambling from run to run) condition. An overview of the <a href="https://www.mitpressjournals.org/na101/home/literatum/publisher/mit/journals/content/jocn/2018/jocn.2018.30.issue-9/jocn_a_01308/20180730/images/large/01308f01c.jpeg">experimental design is shown in this figure</a> from Aly and colleagues (2018)</p> <p><strong>Preprocessing</strong></p> <p>After downloading from OpenNeuro using <a href="https://www.datalad.org/">DataLad</a>, data was preprocessed using <a href="https://fmriprep.readthedocs.io">fMRIPrep 1.5.0rc1</a>. A complete transcript of the fMRIPrep processing is available as a README file in the repository. Post-processing was performed using <a href="http://nilearn.github.io">Nilearn</a>. Briefly, functional files were masked with the fMRIPrep-derived brain mask and trimmed to discard non-steady state volumes. Please see the README for further details.</p>
Color_rivalry_fMRI
<p> </p> <p>Color rivalry fMRI dataset.</p> <p>color1 : Magenta - Green color pair rivalry data</p> <p>color2: Blue - Orange color pair rivalry data</p> <p>replay: Magenta - Green color pair replay data (without rivalry) </p> <p>train: 8 DKL color training data</p> <p># The number in front of each file denotes observer number.</p> <p> </p> <p> </p> <p> </p>
dEEG and fMRI_PLOS ONE
<p>subject and data information</p> <p>5_subjects</p> <p>self-paced motor movement task</p> <p>sampling frequency: 1000 Hz</p> <p>256 channels</p> <p>After processing files</p>
Raw fMRI data from 12 rats used in the manuscript "Mapping of hemodynamic responses to the sensorymotor stimulation in a rodent model: a BOLD fMRI study" submitted to PlosOne for publication
<p>Raw data are from twelve male adult Wistar rats (Charles River Laboratories, Paris-France) weighing 300 ± 20g.</p> <p>Rats were initially anesthetized (induction) with 3% isoflurane and were maintained under 0,7-0.8% (sedation along with a muscle relaxation) during fMRI session. </p> <p>Each rat was submitted to two fMRI sessions: one with TE of 30 ms and and other with TE of 40 ms.</p> <p>For fMRI acquisition, electrodes were inserted subcutaneously in the palmar surface of the right hindpaw of each rat and electrical stimulation (current pulses with a 1.7 mA amplitude, 10 ms duration and 8 Hz frequency) was applied in a block-design starting with a resting period of 25s as a baseline followed by 25s stimulation, repeated 8 times.</p> <p>Ten 1-mm thick contiguous axial slices, from -6.36 mm to +2.64 mm to Bregma, were acquired with a two-shot gradient echo planar imaging (GE EPI) pulse sequence (2.56 cm2 FOV; 64x64 matrix size; a TR of 1000 ms; a flip angle of 50°) resulting in the pixel size of 0.4 mm.</p> <p>All imaging experiments were performed on a 4.7T Bruker (Biospec 47/40, Bruker, GmbH, Ettlingen,Germany) with a horizontal bore magnet equipped with a 12 cm gradient coil (Bruker BGA12, 400 mT/m) and interfaced to AVANCE III console. Two actively decoupled RF coils were used: a 7.2-cm diameter volume coil for transmission and a 2-cm diameter surface coil (Rapid Biomedical, Rimpar, Germany) positioned on the top of the animal's head for reception.</p>
Adjudicating between face-coding models with individual-face fMRI responses: Data and analysis software
<p>Computational model fits to human neuroimaging data. Please see included readme.txt file.</p>
Dataset supporting "Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli."
<p>Here we provide fMRI data used for the following project (for details see data description file): Gertz, H., Hilger, M., *Hegele, M., & *Fiehler, K. (2016). Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli. Neuroimage, doi: 10.1016/j.neuroimage.2016.05.043. (*shared last authorship)</p> <p> </p> <p>Previous studies have shown that beliefs about the human origin of a stimulus are capable of modulating the coupling of perception and action. Such beliefs can be based on top-down recognition of the identity of an actor or bottom-up observation of the behavior of the stimulus. Instructed human agency has been shown to lead to superior tracking performance of a moving dot as compared to instructed computer agency, especially when the dot followed a biological velocity profile and thus matched the predicted movement, whereas a violation of instructed human agency by a nonbiological dot motion impaired oculomotor tracking (Zwickel et al., 2012). This suggests that the instructed agency biases the selection of predictive models on the movement trajectory of the dot motion. The aim of the present fMRI study was to examine the neural correlates of top-down and bottom-up modulations of perception–action couplings by manipulating the instructed agency (human action vs. computer-generated action) and the observable behavior of the stimulus (biological vs. nonbiological velocity profile). To this end, participants performed an oculomotor tracking task in an MRI environment. Oculomotor tracking activated areas of the eye movement network. A right-hemisphere occipito-temporal cluster comprising the motion-sensitive area V5 showed a preference for the biological as compared to the nonbiological velocity profile.Importantly,a mismatch between instructed human agency and a nonbiological velocity profile primarily activated medial-frontal areas comprising the frontal pole, the paracingulate gyrus, and the anterior cingulate gyrus, as well as the cerebellum and the supplementary eye field as part of the eye movement network. This mismatch effect was specific to the instructed human agency and did not occur in conditions with a mismatch between instructed computer agency and a biological velocity profile. Our results support the hypothesis that humans activate a specific predictive model for biological movements based on their own motor expertise. A violation of this predictive model causes costs as the movement needs to be corrected in accordance with incoming (nonbiological) sensory information.</p>
Dataset to 'Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy '
<p>Dataset to 'Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy '</p> <div> <div>For the linked publication see: <a href="https://doi.org/10.1162/netn_a_00362" target="_blank" rel="noopener">https://doi.org/10.1162/netn_a_00362</a></div> <div> </div> </div> <p><em>eeg-fmri_$dataset$_$group$_connectomes_desikan_scrubbed.mat</em><br>datasets with filename truncTo5min hold static connectivities based on timeseries truncated<br>to 5min. All other datasets are based on static connectivities derived from the total session<br>timeseries.</p> <p><br>subj: subject<br>subj.name: name of the subject<br>subj.sess: session<br>subj.sess.sess_name: name of the session<br>subj.sess.fMRI: vector of upper triangular of fMRI connectivity<br>subj.sess.EEG: EEG connectomes<br>subj.sess.EEG.name: name of connectivity measure used (corrected imaginary part of the coherency: iCoh)<br>subj.sess.EEG.bands: EEG frequency bands<br>subj.sess.EEG.bands.name: name of frequency band (delta, theta, alpha, beta, gamma)<br>subj.sess.EEG.bands.name.conn: vector of upper triangular of EEG connectivity<br>subj.atlas: Atlas<br>subj.atlas.name: name of atlas used (Desikan)<br>subj.atlas.regions: number of regions</p> <p><em>$dataset$_$group$_</em>particpants.tsv: Subject metadata</p> <p><em>$dataset$_$group$_spikes.mat: Interictal epileptoform discharges (IEDs) marked for each session of TLE patients</em></p> <p><em>aparc_aseg_yeoR7_68reg_eeg_nosubc_cmfg2dan.mat: mapping of Desikan regions to Yeo7-networks (Yeo et al. 2011, JNP)</em></p> <p><em>desi_coord_68.txt: MNI coordinates of region centers of the Desikan atlas<br></em></p> <p><em>For the related code to this dataset please clone: </em>https://github.com/jwirsich/eeg-fmri-tle or use the code provided in 'eeg-fmri-tle-code.zip'</p>
Data from: The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation
<p>Included are raw neuroimaging and preprocessed neural recording data from "The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation" (see Related Works section; citation will be updated after publication). Please cite this paper if you use any of these data. Refer to the linked github repository for associated code.</p> <p>Neuroimaging data is organized in BIDS format and saved as NIfTI files. We used MION (monocrystalline iron oxide nanoparticle) as a contrast agent. Functional resting state files can be found in the 'func' folder for each imaging session. The final six runs are resting state data (the first two/three are short EPI sequences used to test that MION is present in the brain; all resting state data used in our analyses consist of 300 volumes). The first three of these six runs consist of baseline data with no drug treatment. Four through six are resting state data recorded after I.M. injection of vehicle (2% DMSO in saline), dechloroclozapine (DCZ) or clozapine-N-oxide (CNO). </p> <p>Neural recording data is separated into LFP data, organized by folder, and putative single units, organized the 'Sorted neurons' folder. LFP data folders are named by subject's intial and date of recording. Single units are labeled according to this same system. All data are stored in .mat format and can be opened in MATLAB. KB2.mat files store timing information: the first event in the KBD2 file indicates the start of baseline, pre-injection data acquisition, and the second event indicates the start of post-injection treatment data. The KB3.mat files contains the timing information of the drug injection. As with the fMRI data, we treated animals with I.M. injection of vehicle, DCZ, or CNO. </p> <p>Treatment information for both modalities is as follows. Neuroimaging: 2020/03/16 Animal L DCZ 1; 2020/05/27 Animal H vehicle 1; 2020/06/01 Animal L vehicle 1; 2020/06/08 Animal H DCZ 1; 2020/06/22 Animal L DCZ 2; 2020/06/24 Animal H vehicle 2; 2020/07/06 Animal L vehicle 2; 2020/07/08 Animal H DCZ 2; 2021/10/25 Animal L CNO; 2022/01/13 Animal H CNO. Neural recordings: 2022/04/14 Animal H DCZ 1; 2022/04/21 Animal H vehicle 1; 2022/05/12 Animal H DCZ 2; 2022/05/24 Animal H vehicle 2; 2022/06/03 Animal H CNO; 2022/08/18 Animal L vehicle 1; 2022/08/25 Animal L DCZ 1; 2022/09/01 Animal L DCZ 2; 2022/09/08 Animal L vehicle 2; 2022/09/22 Animal L CNO.</p>
Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target – an fMRI study
<p>Background: Monoclonal antibodies (mAbs) against calcitonin gene-related peptides (CGRP) are novel treatments for migraine prevention. Based on a previous functional imaging study which investigated the CGRP receptor mAb (erenumab), we hypothesized that (i) the CGRP ligand mAb galcanezumab would alter central trigeminal pain processing; (ii) responders to galcanezumab treatment would show specific hypothalamic modulation in contrast to non-responders; and (iii) the ligand and the receptor antibody differ in brain responses.</p> <p>Methods: Using an established trigeminal nociceptive functional magnetic imaging paradigm, 26 migraine patients were subsequently scanned twice: before and 2–3 weeks after administration of galcanezumab.</p> <p>Results: We found that galcanezumab decreases hypothalamic activation in all patients and that the reduction was stronger in responders than in non-responders. Contrasting erenumab and galcanezumab showed that both antibodies activate a distinct network. We also found that pre-treatment activity of the spinal trigeminal nucleus (STN) and coupling between the STN and the hypothalamus covariates with the response to galcanezumab.</p> <p>Conclusions: These data suggest that despite relative impermeability of the blood-brain barrier for CGRP mAb, mAb treatment induces certain and highly specific brain effects which may be part of the mechanism of their efficacy in migraine treatment.</p> <p>Funding: This work was supported by the German Ministry of Education and Research (BMBF) of ERA-Net Neuron under the project code BIOMIGA (01EW2002 to AM) and by the German Research Foundation (SFB936-178316478-A5 to AM). The funding sources did not influence study conduction in any way. </p> <p>Clinical trial number: The basic science study was preregistered in the Open Science Framework (https://osf.io/m2rc6).</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, July 2018.</p> <p>This dataset provide the Betas for subcortical and a subset of the cortical voxels for three subjects in matlab format.</p> <p>The field bTest refers to the Beta coefficients for every voxel in the test data. The fields beta1 and beta2 refer to the split-half partitions of the bTest coefficients. The field varBparam refers to the parametric variances of the Beta coefficients and the field varBBootstrap refers to the variances of the Betas computed with bootstrap. </p>
Masked datasets from an fMRI experiment on the impact of semantic priming on the perception of ambivalent (male versus female) faces
<p>Twenty-four female native Dutch speakers participated in the fMRI experiment and gained monetary compensation for their participation. Only female participants were recruited for the study, in order to avoid gender-related confounding factors. The study was approved by the local ethics committee (CMO Arnhem-Nijmegen, Radboud University Medical Center, ethical approval for studies on healthy human subjects at the Donders Centre for Cognitive Neuroimaging, no ECG 2012-0910-058) and conducted in accordance with their guidelines. All participants signed informed consent forms before the experiment. The data from seven subjects were excluded from the analysis: 3 subjects failed to finish the task and 4 subjects exhibited head motion that exceeded the maximum acceptance rate of 2 [mm]. The remaining 17 subjects (females, age 18-29 years) reported no neurological diseases, and had normal or corrected-to-normal vision. </p> <p>A set of realistic 3D faces was morphed across gender (from extremely female to extremely male) using FaceGen Modeller 3.5 (Singular Inversions, www.facegen.com). The morphing procedure started from 40 distinct faces. For each face, we gradually modulated gender features in 5 steps with the same amount of feature transformation in each step. The face stimuli were presented frontally and cropped around the oval of the face. We controlled for luminance using SHINE toolbox for MATLAB. The perceptual boundary within gender continuum of faces was established in a separate behavioral experiment.</p> <p>Each trial started with priming: presentation of a gender-related word 'man' or 'vrouw' for 0.2 [s]. Then, after the fixation cross 0.25 [s]), a face was presented (0.5 [s]), followed by an inter-trial period of a randomized length of 5-7 [s]. Participants were asked to perform a matching task: respond 'yes' if a word and subsequent picture corresponded in gender, and 'no' otherwise. The experiment was carried out in Dutch. The buttons were counterbalanced across subjects. The experiment was divided into 6 blocks in order to avoid fatigue. Each block consisted of 50 trials. The order of stimuli was randomized across blocks and participants. We used Presentation software (version 17.1, www.neurobs.com) in order to screen the stimuli during the experiment.</p> <p>Functional images were acquired using 3T Skyra MRI system (Siemens Magnetom), T2* weighted echo-planar images (gradient-echo, repetition-time TR = 1760 [ms], echo-time TE = 32 [ms], 0.7 [ms] echo spacing, 1626 hz/Px bandwidth, generalized auto-calibrating partially parallel acquisition (GRAPPA), acceleration factor 3, 32 channel brain receiver coil). In total, 78 axial slices were acquired (2.0 [mm] thickness, 2.0*2.0 [mm] in plane resolution, 212 [mm] field of view (FOV) whole brain, anterior-to-posterior phase-encoding direction).</p> <p>The data reprocessing was performed using SPM12 (Welcome Trust Center for Neuroimaging, University College London, UK). Functional scans were realigned to the first scan of the first run with further realignment to the mean scan. We performed slice-time correction on realigned images to account for differences in image acquisition between slices. Motion-related components were removed from the data using a data-driven ICA-AROMA. Denoised functional scans were spatially normalized to the Montreal Neurological Institute (MNI) space without changing the voxel size. Normalized data were smoothed spatially with a Gaussian kernel of 6 [mm] full-width at half-maximum.</p> <p>We extracted region-of-interest (ROI) mask using Anatomical Automatic Labeling atlas (AAL). According to our a priori hypothesis, we preselected the bilateral SPL (4288 voxels) and the bilateral IPL (3792 voxels).</p>
Quantitative analyses help in choosing between simultaneous versus separate EEG and fMRI
<p>This dataset contains EEG and fMRI data of the same subjects in a simultaneous EEG-fMRI session and a separate EEG-fMRI session. The corresponding paper is published in Frontiers of Neuroscience - Brain Imaging Methods. </p> <p> </p>
Visual stimuli used in fMRI experiment on processing of real and illusory surfaces
<p>These videos contain samples of visual stimuli used in an fMRI experiment on the processing of real and illusory surfaces in human early visual cortex [the experiments are part of my PhD thesis, in preparation]. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the duration of rest blocks was much longer.</p>
PHIME Pilot resting state fMRI study
<p>Resting state functional Magnetic Resonance Imaging (fMRI) data for 14 adolescents (12-18 years) that were part of a pilot neuroimaging study on manganese exposure and brain development in Northern Italy. Participants are part of the Public Health Impact of Manganese Exposure (PHIME) study.</p> <p>Data include a 10-minute resting state fMRI scan (repetition time: 2500 ms) and an anatomical scan (3D MPRAGE) for each subject.</p>
The Krakow Paradigm - fMRI datasets in BIDS format
<p><strong>Participants</strong></p> <p>Forty-nine participants (mean age, 24.2 ± 3.7 years; 16 males) met the following experiment requirements: no contraindication for MRI scanning; normal or corrected-to-normal vision; no reported physical or psychiatric disorders; drug-free. To ensure sufficient experience in the environment, subjects had to be Krakow residents for at least one year. Subjects lived in Krakow on average 9.1 years (SD 8.1).</p> <p>Participants were informed about the procedure and goals of the study and they gave written consent. The study was approved by the bioethics commission at the Polish Military Institute of Aviation Medicine and was conducted in accordance with ethical standards described in the Declaration of Helsinki. The study was a part of a larger registered project (ISRCTN 18109340). </p> <p><strong>Experimental Task</strong></p> <p>A novel place recognition task, the Krakow Paradigm, was prepared and generated using E-Prime 2.0 (©Psychology Software Tools). The task comprised of two stages: the training session and the fMRI session. Before the training session, subjects were presented with a map of Krakow city on which a thick red line marked the city “center” area and were asked to familiarize with the borders. </p> <p>The trial comprised of the stimulus (4.5 sec duration) and two response screens (each 1.0 sec duration), all separated by the blank screens (each 0.5 sec duration). The stimulus was a photograph taken in the Krakow city (resolution 640 x 428), presenting either characteristic landmarks (e.g. an Old Square) or uncharacteristic outside places (e.g. a playground near an estate community). Photograph was presented centrally on the light-gray background and covered 60% of the screen. On the first response screen, the question “Krakow Center?” occurred with three possible answers (‘yes’, ‘no’, ‘I don’t know’) given by pressing a button on a key-pad with right-hand index, middle, or ring finger respectively. On the second response screen, the question “Have you seen it in real-life?” occurred with two possible answers (‘yes’, ‘no’) given by pressing a button using index or middle finger respectively. For both questions, responses were recorded for 1.5 sec. Between the stimuli, a fixation point (a hash sign) was presented for a varying interval between 2.4 and 6.6 sec every 0.7 sec (on average total trial length = 12 sec). Total scan time was less than 13 minutes.</p> <p>The training session was conducted to ensure timely responses. It was comprised of 7 trials, different than those used in the fMRI session, and was presented on regular computer screen. The fMRI session included 60 trials and was presented using the VisualSystem HD (NordicNeuroLab, Bergen, Norway) binocular apparatus. 50% of the photos were taken in the “center” and 50% outside of it. Characteristic and uncharacteristic places were counterbalanced across both location possibilities. At the end of the task, a feedback information was given to participants informing them the percentage of correctly classified places. Because participants were instructed to wait until the response screen appeared before making a response, reaction times are not informative and were not reported. The rationale for this procedure was to promote accuracy rather than speed, and to encourage response preparation, i.e. memory retrieval, while looking at a photo.</p> <p><strong>MRI Data Acquisition</strong></p> <p>MRI was performed using a 3T scanner (Magnetom Skyra, Siemens) with a 20-channel head/neck coil. High-resolution, whole-brain anatomical images were acquired using a T1-MPRAGE sequence. A total of 176 sagittal slices were obtained (voxel size 1×1×1.1 mm3; TR = 2300 ms, TE = 2.98 ms, flip angle = 9°) for co-registration with the fMRI data. Next, a B0 inhomogeneity gradient fieldmap (magnitude and phase images) was acquired with a dual-echo gradient-echo sequence, matched spatially with fMRI scans (TE1 = 4.92 ms, TE2 = 7.38 ms, TR = 400 ms).</p> <p>Functional T2*-weighted images were acquired using a whole-brain echo planar (EPI) pulse sequence with the following parameters: 3 mm isotropic voxel; TR = 2070 ms; TE = 30 ms; flip angle = 90°; FOV 224 × 224 mm2; GRAPPA acceleration factor 2; and phase encoding A/P. Due to magnetic saturation effects, the first four volumes (dummy scans) of each session were discarded instantly resulting in 360 volumes acquired for each participant.</p>
NFED-fmri
<p># A fMRI dataset in response to large number of short naturalistic facial expression videos<br>Naturalistic facial expressions dataset (NFED),a large-scale dataset of whole-brain functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.<br>The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data.<br>To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047</p> <p>## Preprocess procedure<br>The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.</p> <p>**code: ./volume_pre-process/**</p> <p>Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.<br>## GLM of main experiment<br>We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).<br>**code: ./GLMsingle-main-experiment/matlab/main.m**</p> <p>#### retinotopic mapping</p> <p>The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.</p> <p>**code: ./Functional-localizer-experiment-analysis/s4a_analysis_prf.m**</p> <p>#### fLoc experiment</p> <p>We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.<br>**code: ./Functional-localizer-experiment-analysis/s4a_analysis_floc.m**</p> <p><br>## Validation<br>### Basic quality control<br>**code: ./validation/FD/FD.py**<br>**code: ./validation/tSNR/tSNR.py**<br>### noise celling<br>The code are available at https://openneuro.org/datasets/ds005047."./validation/code/noise_celling/sub-xx" store the intermediate files required for running the program<br>**code: ./validation/noise_celling/Noise_Ceiling.py**</p> <p>### Correspondence between human brain and DCNN<br>The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.<br>**code: ./validation/dnnbrain/**</p> <p>### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain<br>The code are available at https://openneuro.org/datasets/ds005047."./validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.<br>**code: ./validation/semantic_metadata/**</p> <p>## results<br> The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".</p> <p>## Whole-brain mapping<br>The whole-brain data mapped to the cerebral cortex as obtained from the technical validation.<br>**code: ./show_results_allbrain/Showresults.m**</p> <p>## Mannually prepared environment<br>We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.</p> <p>## stimuli<br>The video stimuli used in the NFED experiment are saved in the "stimuli_1" and "stimuli_2" folders.</p>
ensemble_graphs_fmri_2024
<p>Correlation and ensemble graphs constructed from fMRI data of 100 individuals performing 7 different cognitive tasks. Original fMRI data were taken from the Human Connectome Project. More details on the ensemble graph construction method can be found on github (https://github.com/Daniil-Vlasenko/ensemble_graphs_fmri_2024.git) and our paper (to be published soon).</p> <p>Individuals with the following indices were selected for graph construction: 102109, 102614, 102715, 103212, 106824, 108020, 113316, 118831, 119025, 125222, 127226, 130114, 130720, 135629, 137532, 138332, 144933, 151324, 151930, 152225, 161832, 165436, 169545, 191235, 193845, 194443, 200513, 206525, 206727, 206828, 210112, 211619, 211821, 213017, 213522, 219231, 281135, 300719, 314225, 325129, 329844, 342129, 349244, 378756, 392447, 421226, 454140, 516742, 519647, 541640, 111211, 115724, 117021, 120414, 123723, 125424, 126426, 127832, 135124, 139435, 143224, 146735, 147636, 152427, 153126, 167440, 168947, 175136, 176845, 180230, 186545, 186848, 188145, 192237, 198047, 199352, 206323, 255740, 274542, 350330, 368753, 376247, 394956, 419239, 453542, 463040, 481042, 510225, 513130, 558960, 634748, 654552, 680452, 694362, 788674, 814548, 825654, 828862, 869472, 911849.</p>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<pre>#A fMRI dataset in response to large number of short natural dynamic facial expression videos<br>Natural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.<br>The dataset contains raw data, pre-processed volume data,pre-processed surface data and suface-based analyzed data.<br>To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047<br><br>## Preprocess procedure<br>The MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/**<br>Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.<br>## GLM of main experiment<br>We utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).<br><br>The regressors in the GLMsingle toolbox mainly include the following categories:<br>1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response.<br>2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation.<br>3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal.<br>4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement.<br>5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level.<br><br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m**<br><br>#### retinotopic mapping<br><br>The fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.<br><br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m**<br><br>#### fLoc experiment<br><br>We used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m**<br><br><br>## Validation<br>### Basic quality control<br>The fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py**<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py**<br>### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment.<br>In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py**<br>### noise celling<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py**<br><br>### Correspondence between human brain and DCNN<br>The code are available at https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/**<br><br>### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brain<br>The code are available at https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.<br>**code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/**<br><br>## results<br> The results can be viewed at "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".<br><br>## Whole-brain mapping<br>The whole-brain data mapped to the cerebral cortex as obtained from the technical validation.<br>**code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m**<br><br>## Mannually prepared environment<br>We provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.<br><br>## stimuli<br>The video stimuli used in the NFED experiment are saved in the "stimuli" folders.<br><br></pre>
Multi-echo resting-state fMRI networks of healthy volunteers
<p>The dataset contains the resting-state networks of 16 healthy volunteers following multi-echo combination methods: 1) optimal combination 2) tSNR-weighted combination 3) tCNR-weighted combination (PAID method) 4) second echo only (single-echo) After echo combination by one of the methods (or the second echo) 30 independent components were extracted using group ICA and dual regression.</p> <p>Image format: Gunzipped NIfTI (.nii.gz)</p> <p>Time-series format: text file (.txt)</p> <p>The structure of the uploaded folder:</p> <p>- Layer 1: PilmeyerEtAl_ICA_maps_and_timeseries - main folder</p> <p>- Layer 2 (combination method): OC - optimal combination, SE- second echo, tCNR - temporal contrast-to-noise, tSNR - temporal signal-to-noise</p> <p>- Layer 3: groupICA_desc-XX - contains the group ICA maps and time-series before dual regression, sub-YY - folders for each of the 16 subjects</p> <p>- Layer 4: sub-YY_desc_XX - contains the individual extracted ICA maps and time-series</p> <p> </p> <p> </p> <p> </p>
tRNS-fMRI Experiment
<p>Raw fMRI data collected during a trns-fMRI experiment. </p> <p>Each subject undewent two fMRI Session (Session1 and Session2). One anatomical and one resting state functional run were collected for each subject, and each session.</p> <p>Each subject folder (SnjN) contains data of one subject and it is subdived into two subfolder relative to each scanning session. 'SbjN_session1' includes raw data for pre-test session and is subdivided into one folder (SbjN_AnatomicalCNR') containing DICOM files for the anatomical CNR and for functional resting state data ('SbjN_RestingState'). Task related fMRI data are also included for each subject (5 runs per subject, "SbjN_Run1" etc). </p> <p>'Sbj_Session2' includes raw data collected during post-test session and is subdived into one foder ('SbjN_AnatomicalCNR) containing DICOM files for the anamotical CNR and one folder (SbjN_RestingState) containing DICOM files for the functional resting state data. </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.