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566 results for “fmri”
Raw data of 'He, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., & Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'
<p>This is the raw data for</p> <p>He, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., & Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.</p> <p>Both second level fMRI data and EEG data for single subjects are provided.</p> <p></p>
Medial prefrontal cortex and anteromedial thalamus interaction regulates motivation related behavior and dopaminergic neuron activity: fMRI: Rats
<p>Rat fMRI activation images supplementing for Fig. 3d and Suppl. Fig. 5.</p>
Medial prefrontal cortex and anteromedial thalamus interaction regulates motivation related behavior and dopaminergic neuron activity: fMRI: Human
<p>Human fMRI activation images supplementing for Fig. 8 and Suppl. Fig. 11.</p>
Dataset for: Spatial tuning of face part representations within face-selective areas revealed by high-field fMRI
<p>Regions sensitive to specific object categories as well as organized spatial patterns sensitive to different features have been found across the whole ventral temporal cortex (VTC). However, it is unclear that within each object category region, how specific feature representations are organized to support object identification. Would object features, such as object parts, be represented in fine-scale spatial tuning within object category-specific regions? Here we used high-field 7T fMRI to examine the spatial tuning to different face parts within each face-selective region. Our results show consistent spatial tuning of face parts across individuals that within right posterior fusiform face area (pFFA) and right occipital face area (OFA), the posterior portion of each region was biased to eyes, while the anterior portion was biased to mouth and chin stimuli. Our results demonstrate that within the occipital and fusiform face processing regions, there exist systematic spatial tuning to different face parts that support further computation combining them.</p>
High-Resolution TURBINE fMRI Dataset 1
<p>Isotropic 0.67 mm visual cortex-slab TURBINE raw dataset 1 (in ISMRMRD format) for "Ultra-High Resolution fMRI at 7T using Radial-Cartesian TURBINE sampling" published in Magnetic Resonance in Medicine.</p>
High-Resolution TURBINE fMRI Dataset 2
<p>Isotropic 0.67 mm visual cortex-slab TURBINE raw dataset 2 (in ISMRMRD format) for "Ultra-High Resolution fMRI at 7T using Radial-Cartesian TURBINE sampling" published in Magnetic Resonance in Medicine.</p> <p> </p>
GSDGM files generated by human connectome project s500 rs-fMRI (CONN atlas 132 ROIs) data
<p>Group Surrogate Data Generating Model (GSDGM) dataset generated by human connectome project s500 rs-fMRI (CONN atlas 132 ROIs) data.</p>
Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T
<p>This dataset includes the k-space data of two 2D sagittal slices from the conventional and seg-CAIPI(8,3) 3D multi-shot EPI datasets, as well as the coil sensitivity maps. The conventional sampling corresponds to seg-CAIPI(2,1). These two 3D multi-shot EPI datasets were acquired at 1.8mm isotropic resolution and acceleration factor R=2x2. Other protocol parameters are: matrix size=116x116x96, 40 volumes, TE/TR=23/55ms. </p>
Learning about Learning: Mining Human Brain Sub-Network Biomarkers from fMRI Data
<p>These are coherence matrices originally described in: Dynamic reconfiguration of human brain networks during learning. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. Proc Natl Acad Sci U S A. 2011 May 3;108(18):7641-6. doi:10.1073/pnas.1018985108. Epub 2011 Apr 18. Later the matrices were also studied in Cohesive network reconfiguration accompanies extended training. Telesford QK, Ashourvan A, Wymbs NF, Grafton ST, Vettel JM, BassettDS. Hum Brain Mapp. 2017 Sep;38(9):4744-4759. doi: 10.1002/hbm.23699. Epub 2017 Jun 24.</p>
resting state fMRI sample dataset
<p>This is a sample dataset, uploaded primarily to test the HRF retrieval and deconvolution toolbox https://github.com/guorongwu/rsHRF</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for the revised version of the 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, November 2018.</p> <p>The dataset (01.rar) contains the fMRI time series for one subject in Nifti format acquired on an actively shielded MAGNETOM 7T whole body system driven by a Siemens console at Scannexus (<a href="http://www.scannexus.nl)">www.scannexus.nl)</a>. Every folder (24 runs, one folder per run) contains 150 Nifti files, one Nifti file for each fMRI volume. Preprocessing consisted of slice scan-time correction (with sinc interpolation), 3-dimensional motion correction, and temporal high pass filtering (removing drifts of 4 cycles or less per run).</p> <p>The matlab file dmS01_24runs.mat contains a 24-length cell array of fMRI design matrices, one for every run. Every fMRI design matrix is size 150 volumes x 51 covariates. The first 42 columns correspond to the stimuli presented (42 sounds per run). Columns 43, and 44, correspond to the run mean and the linear trend covariates. The rest of the columns correspond to the covariates obtained with GLMdenoise. The matlab variable <em>stimulus</em> of size 24 x 42 contains the index of the sounds presented at every run. A total of 168 sounds were presented, each sound was presented 6 times across the 24 fMRI runs.</p> <p>The file SPMgls0.rar contains the Beta images in Nifti format for every column of the fMRI design matrix, including noise covariates, for every fMRI run. This model was estimated assuming i.i.d fMRI noise (OLS). The codes for computing the noise ceiling are available in the file nccodes.rar, together with a two of examples of their use. SPM is required.</p>
fMRI of rats under anesthesia
<p>Signals from fMRI of rats under anesthesia. </p> <p>Contains fMRI signals extracted on an atlas, systemic variables, baseline CBF signals and brain areas features.</p> <p>See publication for details and citations. </p>
T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data, Part 2
<p>Raw and processed MRI data of the study entiled: T1234-Part 2</p> <p>Authors: Chung (Kenny) Kan1, Rüdiger Stirnberg2, Marcela Montequin1, Omer Faruk Gulban3,4, A Tyler Morgan1, Peter Bandettini1, Laurentius (Renzo) Huber1</p> <ol> <li>NIMH, NIH, Bethesda, United States,</li> <li>German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</li> <li>CN, FPN, University of Maastricht, The Netherlands,</li> <li>Brain Innovation, Maastricht, The Netherlands</li> </ol> <p><strong>Purpose</strong>: High-resolution fMRI at 7T is limited by limited registration quality between functional data with structural scans. We aim to provide a fast acquisition method that provides distortion-matched, artifact mitigated structural reference data.</p> <p><strong>Methods</strong>: We developed an efficient sequence approach with adjustable distortions, termed T1234: T1-weighted 2-inversion 3D-EPI with 4 directions for high-resolution fMRI. A forward Bloch model is implemented for T1 quantification and protocol optimization. 20 participants were scanned on 7T with structural and functional protocols to evaluate the utility of T1234.</p> <p><strong>Results</strong>: We find that a fast protocol provides reliable data for whole-brain segmentations in EPI-space in 3:00-3:40 min). It is robust across sessions, participants, and three 7T SIEMENS scanners. T1234 allows layer fMRI signal analysis with higher laminar precision.</p> <p><strong>Conclusion: </strong>This structural mapping approach allows precise registration with fMRI data. T1234 is implemented, validated, and tested to serve users of our sequence (locally and >50 centers worldwide). </p> <p>Part 1 of the data set is here: https://zenodo.org/doi/10.5281/zenodo.13366783</p>
DHMC opto-fMRI Pilot Raw Bruker ParaVision Data
<p>Pilot data used to develop an opto-fMRI protocol at Dartmouth Hitchcock Medical Center.</p>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<p><span>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although </span>the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a <span>small number of</span> lab-controlled video stimuli, how the human brain processes <span>natural</span> facial expressions still needs to be investigated. <span>T</span>o our knowledge<span>,</span> this type of data <span>specifically</span> <span>on </span><span>large number of </span><span>natural</span> <span>facial expression videos</span> is currently missing. <span>W</span>e describe <span>here </span>the <span>natural </span>Facial Expressions Dataset (NFED), a fMRI dataset <span>including </span>responses to 1,320 short (<span>3-second</span>) <span>natural</span> facial expression video clips. <span>These video clips is </span><span>annotated</span> <span>with three types of labels: emotion, gender, and ethnicity</span><span>, along with accompanying metadata</span>. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of <span>natural </span> <span>facial expression videos.</span></p>
A fMRI dataset in response to large-scale short natural dynamic facial expression videos
<p>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large<span>-scale</span> natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.</p>
Spatial–temporal dynamics of gesture–speech integration: a simultaneous EEG-fMRI study
<p>The semantic integration between gesture and speech (GSI) is mediated by the left posterior temporal sulcus/middle temporal gyrus (pSTS/MTG) and the left inferior frontal gyrus (IFG). Evidence from electroencephalography (EEG) suggests that oscillations in the alpha and beta bands may support processes at different stages of GSI. In the present study, we investigated the relationship between electrophysiological oscillations and blood-oxygen-level-dependent (BOLD) activity during GSI. In a simultaneous EEG-fMRI study, German participants (n = 19) were presented with videos of an actor either performing meaningful gestures in the context of a comprehensible German (GG) or incomprehensible Russian sentence (GR), or just speaking a German sentence (SG). EEG results revealed reduced alpha and beta power for the GG vs. SG conditions, while fMRI analyses showed BOLD increase in the left pSTS/MTG for GG > GR ∩ GG > SG. In time-window-based EEG-informed fMRI analyses, we further found a positive correlation between single-trial alpha power and BOLD signal in the left pSTS/MTG, the left IFG, and several sub-cortical regions. Moreover, the alpha-pSTS/MTG correlation was observed in an earlier time window in comparison to the alpha-IFG correlation, thus supporting a two-stage processing model of GSI. Our study shows that EEG-informed fMRI implies multiple roles of alpha oscillations during GSI, and that the method is a best candidate for multidimensional investigations on complex cognitive functions such as GSI.</p>
Reversal learning in FTD (fMRI)
<p class="MsoNoSpacing"><b>Objective</b>: Frontotemporal Dementia (FTD) is a neurodegenerative disorder that results in disinhibition and difficulty with flexible responding when provided feedback. Inflexible responding is observed early in the course of the illness and contributes to the financial and social morbidities of FTD. Reversal learning is an established cognitive paradigm that indexes flexible responding in the face of feedback signaling a change in reinforcement contingencies, with components of reversal learning associated with specific neurotransmitter systems. The objective of the study was to evaluate the neural mechanisms underlying impaired flexible behavioural responding in FTD using a reversal learning paradigm combined with fMRI.</p> <p class="MsoNoSpacing"><b>Methods</b>: Twenty-two patients meeting the diagnostic criteria for FTD and twenty-one healthy controls completed the study. Participants completed an fMRI-adapted reversal learning task that indexes behavioural flexibility when provided positive and negative feedback.</p> <p class="MsoNoSpacing"><b>Results</b>: Patients with FTD demonstrated poorer behavioural flexibility relative to controls and abnormal BOLD responses within the left ventrolateral prefrontal cortex to incorrect responses made during the learning phase, and during correct responses when reward contingencies were reversed. As well, patients showed decreased activity within the left dorsal lateral prefrontal cortex to incorrect responses compared to controls.</p> <p class="MsoNoSpacing"><b>Conclusions</b>: These findings suggest that reversal learning impairments in patients with FTD, in particular those with frontal predominant atrophy, may be related to impaired flexible motor responding when selecting among several choices and deficient attention to relevant stimuli during instances of conflict (i.e. receiving negative feedback). These results and the associated neurotransmitter systems mediating these regions may provide targets for future pharmacological or behavioural interventions mediating these cognitive deficits.</p>
fMRI free-viewing data, resting-state and structural in macaques associated with publication 'Social prediction modulates activity of macaque superior temporal cortex'
<p>Using a free-viewing and functional magnetic resonance imaging, we show that activity in a region of the macaque middle superior temporal (midSTS) cortex was specifically modulated by the predictability of social interactions. This region could be distinguished from other temporal regions involved in face processing. Using resting-state fMRI in anesthetized macaques, we showed that the connectivity between the face-responsive areas and the social prediction area was more integrated in macaques than in humans. We reproduce the social prediction results in a replication study and provide a control to rule out oculomotor implication through the FEF in the social prediction activity of the midSTS, using Transcranial Ultrasound Stimulation. Using standard geometric shape movement stimuli, we also show that macaques do not attribute mental state to shapes.</p>
High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors
<p>Data to reproduce figures in submitted manuscript "High-dimensional multivariate autoregressive model estimation of<br> human electrophysiological data using fMRI priors"</p> <p>https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1</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.