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566 results for “fmri”
The Contributionsof Eye Gaze Fixations and Target-Lure Similarity to Behavioral and fMRI Indices of Pattern Separation and Pattern Completion
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Data Set: fMRI Meta-Analyses
<p>This data set is the result of a systematic search in <strong>PubMed </strong>and <strong>APA PsycINFO</strong> for <strong>fMRI meta-analyses.</strong></p> <p>These records would be suitable for:</p> <ul> <li>meta-meta analysis on fMRI meta-analyses</li> <li>research questions regarding neuroimaging meta-analysis methodology (especially for those interested in coordination-based meta-analyses (CBMA): activation likelihood estimation (ALE) using GingerALE software, multi-level kernel density analysis (MKDA), seed-based d mapping (SDM) or image-based meta-analyses.</li> </ul> <p><strong>NOTE: These data are the raw search results from PubMed and PsycINFO and have been deduplicated but have NOT been screened for any inclusion criteria. This means you may find records in these results that are not, in fact, meta-analyses but still have the search terms (below) present in the title or abstract of the paper. </strong></p> <p>The data set is available in three formats: .csv, .ris, and a <a href="https://www.zotero.org/groups/4150721/fmri_meta-analyses">Zotero shared library</a></p> <p><strong>Search documentation</strong>: </p> <p>Search Date: May 21, 2021</p> <p>Conducted by: Meghan Testerman, Behavioral Sciences Librarian, Princeton University, mtesterman@princeton.edu</p> <p> </p> <p>PubMed: 433 records identified</p> <p>PubMed Search query (exact): (meta-analysis[Title]) AND (fMRI[Title/Abstract])</p> <p> </p> <p>PsycINFO: 289 records identified</p> <p>PsycINFO Search Query (exact): (TI meta-analysis) AND (TI fMRI OR AB fMRI)</p> <p><br> <strong>Total Results</strong></p> <p>Pubmed (433) + PsycINFO (289) = 722</p> <p>Deduplicates removed: 234</p> <p>Unique records: 488</p>
Haxby et al. (2001): Faces and Objects in Ventral Temporal Cortex (fMRI)
<pre><a href="http://data.pymvpa.org/datasets/haxby2001/">http://data.pymvpa.org/datasets/haxby2001/</a> This is a block-design fMRI dataset from a study on face and object representation in human ventral temporal cortex. It consists of 6 subjects with 12 runs per subject. In each run, the subjects passively viewed greyscale images of eight object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval. Full-brain fMRI data were recorded with a volume repetition time of 2.5s, thus, a stimulus block was covered by roughly 9 volumes. This dataset has been repeatedly reanalyzed. For a complete description of the experimental design, fMRI acquisition parameters, and previously obtained results see the references_ below. Terms Of Use ============ The original authors of :ref:`Haxby et al. (2001) <HGF+01>` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license. .. _Creative Commons Attribution-Share Alike 3.0: http://creativecommons.org/licenses/by-sa/3.0/</pre> <pre>References ========== :ref:`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, P. (2001) <HGF+01>`. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430. :ref:`Hanson, S., Matsuka, T., and Haxby, J. (2004) <HMH04>`. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area? NeuroImage 23, 156–166. :ref:`O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005) <OJA+05>`. Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590. :ref:`Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S. and Pollmann, S (2009) <HHS+09b>`. PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.</pre> <p> </p>
fMRI study of a VR-based motor imagery and observation task, a conventional motor imagery task, and a motor execution task
<p>We used functional magnetic resonance imaging (fMRI) to map brain activation during: i) a VR-based motor imagery and observation task called NeuRow; ii) a conventional non-VR, motor imagery task based on the Graz paradigm; and iii) a motor execution task. Data were collected from two groups of healthy right-handed participants: 11 young adults (mean 58 age 27 ± 4 years) and 10 older adults (mean age 51 ± 6 years). The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM), in accordance with the 1964 Declaration of Helsinki, and approved by the scientific and ethic committees of the Central Hospital of Funchal with approval reference number: 21/2019. A written informed consent was obtained from each participant upon recruitment.</p> <p>Imaging was carried out on a 3T GE Signa HDxt MRI scanner (General Electrics Healthcare, Little Chalfont, United Kingdom) using a 12-channel head coil. fMRI data were acquired using a multi-slice 2D gradient-echo EPI sequence (TR/TE = 2500/30 ms, voxel size = 3.75x3.75x3.00 mm3, flip angle = 90, and FoV = 240x240 mm2). For co-registration purposes, whole-brain structural images were also acquired using a T1-weighted 3D Fast Spoiled Gradient-Echo (FSPGR) sequence (TR/TE = 7.8/3.0 ms, voxel size = 1.00x1.00x0.60 mm3).</p> <p>The following three tasks were performed for left and right arm movement separately, yielding a total of six fMRI runs (pseudo-randomized order): (a) NeuRow task: motor imagery and observation task through VR scenario (NeuRow); (b) Graz task: motor imagery only with abstract instructions (Graz), (c) and motor execution (ME): finger-tapping. Each fMRI run consisted of 8 trials, each with 20 s of baseline followed by 20 s of task (total run duration 5.33 min).</p>
Temporal SNR optimization through RF coil combination in fMRI: The more, the better? - DATASET
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FSS7B - Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex: Supplementary fMRI data
<p>Functional magnetic resonance imaging (fMRI) data supplementing a publication on inhibiting somatosensory fear memory in humans with transcranial magnetic stimulation (TMS). Contains 1) individual regions-of-interest (ROIs) masks in the bilateral primary somatosensory cortex (S1) to target with TMS, 2) S1 masks for left and right hemisphere used in restricting the ROIs to a priori expected area, 3) sum and probability maps of the ROIs over participants, and 4) summary group level fMRI NIFTI images including beta images and T-maps. Individual SPMs/beta images can be requested from the authors for academic research purposes (k.ojala@uke.de). Details on the methods are found in the Supplement of the publication (see linked DOI). </p>
PCF05: fMRI data in a Pavlovian delay threat conditioning task with four visual CS with different rates of electrical US
<p>This data set includes selected functional magnetic resonance imaging (fMRI) data supplementing an article. The data include: </p> <ol> <li>Untresholded Statistical Parametric Maps (SPMs) and beta images (BOLD signal estimates) for relevant contrasts for the GLMs reported in the article</li> <li>Region-of-interest (ROI) masks: anatomical ROIs with combined hemispheres, and masks created from significant BOLD signal clusters from the whole-brain analyses</li> <li>Summary data files for mean beta (BOLD signal estimate) values and their within-subject errors for each ROI</li> <li>A compilation Excel sheet of condition-wise BOLD parameter estimates and standard errors from previous axiomatic aversive prediction error studies, and associated effect sizes as well as sample sizes from a power analysis. </li> </ol> <p>Details of the experimental paradigm as well as of the fMRI data acquisition and analysis can be found in the associated article. </p>
Visual stimuli used in fMRI study on top-down feedback across cortical depths
<p>These videos contain samples of visual stimuli used in an fMRI study on top-down feedback across cortical depths in human early visual cortex [in preparation]. There is one video sample for each of the three experimental conditions in the main experiment: ‘Pac-Man dynamic’, ‘Pac-Man static’, and ‘control dynamic’. In addition, there are videos of stimuli used in a control experiment in which the shape of the stimulus (‘Pac-Man’ or square) and the background (texture or uniform) was varied. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the rest blocks surrounding the stimulus presentations were much longer. The stimulus design of the main experiment is adapted from Akin et al. (2014).</p> <p>Akin, B., Ozdem, C., Eroglu, S., Keskin, D. T., Fang, F., Doerschner, K., Kersten, D., Boyaci, H. (2014). Attention modulates neuronal correlates of interhemispheric integration and global motion perception. Journal of Vision, 14(12). https://doi.org/10.1167/14.12.30</p>
From Maps to Models: A Survey on the Reliability of Small Studies of Task-Based fMRI
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T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data, Part 1
<p>Raw and processed MRI data of the study entiled: T1234-Part 1</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>
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
fMRI study of experimental endotoxemia in humans
<p>This dataset was acquired at Hannover Medical School, Hanover, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee (#7427). All subjects gave written informed consent, and consent to publish their data anonimously.</p> <p>Using high-resolution functional magnetic resonance imaging (fMRI) we recorded brain activity in healthy male subjects undergoing experimental inflammation from intravenous endotoxin. Four fMRI runs covered key phases of the developing inflammation: pre-inflammatory baseline, onset of endotoxemia, onset of proinflammatory cytokinemia, and peak of proinflammatory cytokinemia. We informed the participants that they would either receive an endotoxin or saline injection at some point during the fMRI experiment. However, all subjects received the endotoxin with the start of the second fMRI acquisition.</p> <p>General exclusion criteria included a body mass index of <18 and >30 kg/m², any concurrent medical condition, history of allergies, current use of prescription and non-prescription medications, smoking, and regular high alcohol use. To exclude any inflammatory diseases that may aggravate through the HEM, each subject was interviewed and examined by a physician before being admitted to the study.</p> <p> </p> <p><br> <strong>Data acquisition</strong><br> <em>Experimental design [min]</em></p> <ul> <li>-30 -> -10: Baseline fMRI</li> <li>-5: Baseline blood levels</li> <li>0: Endotoxin administration</li> <li>0-20: Second fMRI</li> <li>20, 30: Blood sampling</li> <li>30-50: Third fMRI</li> <li>50, 60, 70, 80: Blood sampling</li> <li>80-100: Fourth fMRI</li> <li>100, 110, 120, 180, 240, 300, 360: Blood sampling</li> </ul> <p><br> <em>Human endotoxemia model</em><br> All subjects received an intravenous bolus injection of endotoxin over one minute through an intravenous catheter in an antecubital forearm vein. GMP-grade lipopolysaccharide from Escherichia coli O:113:H10:K-strain (Lot 94332B1) provided by National Institute of Health Clinical Center, Bethesda, MD, USA was prepared for human use by reconstitution with sterile water for injection, shaking for 15 minutes on a vortex shaker and final dilution. We used a dose of 1ng/kg (0,02 ml/kg) body weight. All endotoxin solutions were administered immediately after their preparation. Subjects were injected between nine and ten o’clock in the morning and discharged six to eight hours later, when their symptoms had receded, all altered physiological parameters had demonstrated consistent reduction toward baseline values, and the physical exam was normal.</p> <p><br> <em>MRI data</em><br> All MR images were acquired on a Siemens 3T MAGNETOM Skyra using a 64-channel head/neck coil. The scanning protocol consisted of the following sequences (see Sequences.ods):</p> <ul> <li>func_i: Functional whole brain gradient-echo echo-planar images (EPI) (TR=1180 ms; TE=32 ms; 2 mm isotropic resolution; simultaneous multi-slice factor=6; partial Fourier=7/8)</li> <li>func_ref: Reference scan for motion correction and template formation; equivalent to func but without multi-band acceleration (TR=6770 ms)</li> <li>SE_pe1_pe2: Reference scans for unwarping: Two spin-echo images matched to func in distortion without multi-band acceleration; one with the same, the other one with inverted phase encoding direction.</li> <li>t1: T1-weighted magnetisation-prepared rapid acquisition gradient-echo image (MPRAGE) (TR=2400 ms; TE=2.13 ms; TI=1000 ms; 1 mm isotropic resolution; in-plane acceleration factor=2)</li> <li>t2: T2-weighted image (TR=3200 ms; TE=564 ms; 1 mm isotropic resolution; in-plane acceleration factor=2)</li> </ul> <p><br> <em>fMRI data preprocessing</em><br> Our preprocessing pipleine (JPreprocessing) is optimised for the brainstem and hypothalamus by avoiding superfluous resampling steps and unnecessary smoothing. On that account, motion correction (MCFLIRT [Jenkinson et al., 2002]) and unwarping (topup [Andersson et al.,2003]) are applied in a single transformation. Afterwards, brain extraction (BET [Smith, 2002]), grand mean scaling and high pass filtering (0.005 Hz) are applied. The data are not smoothed.</p> <p>Two study templates were generated using Advanced Normalization Tools (ANTs [Avants et al., 2008] using antsMultivariateTemplateConstruction2.sh). The first one using the unwarped EPI reference images (func_ref); the second one using the T1-images.</p> <p><br> <em>Physiological data</em><br> The following physiological measures were acquired with an MR-compatible BIOPAC MP150 system</p> <ul> <li>Blood pressure (systolic and dyastolic): Non-invasive continuous blood pressure of the digital artery (pulse decomposition analysis using CareTaker)</li> <li>Electrodermal activity</li> <li>Photoplethysmography</li> <li>Respiration (belt)</li> <li>Electrocardiography</li> </ul> <p><br> <em>Subject information</em><br> 01 m 25 a 175 cm 70 kg<br> 02 m 44 a 192 cm 84 kg<br> 03 m 27 a 188 cm 85 kg<br> 04 m 19 a 183 cm 99 kg<br> 05 m 20 a 183 cm 90 kg<br> 06 m 26 a 180 cm 78 kg<br> 07 m 21 a 189 cm 80 kg</p> <p><br> <em>Missing data</em></p> <ul> <li>sub01: functional data during run3 and run4 is shorter</li> </ul> <p> </p> <p><strong>Data structure</strong><br> <em>data.csv</em></p> <ul> <li>Contains blood parameters, symptom ratings, as well as blood pressure measurements for all subjects.</li> </ul> <p><br> <em>sequences.ods</em></p> <ul> <li>MRI sequence parameters</li> </ul> <p><br> <em>subXX</em></p> <ul> <li>acqparams.txt: Acquisition parameters needed for topup</li> <li>func_i: raw functional data</li> <li>func_ref: Reference image for motion correction and template generation</li> <li>physio.acq: Physiological measurements <ul> <li>Trigger</li> <li>Blood pressure (systolic and dyastolic)</li> <li>Electrodermal activity</li> <li>Photoplethysmogram</li> <li>Respiration</li> <li>Electrocardiogram</li> </ul> </li> <li>physio_cuts.txt: time points in the physio data that correspond to fMRI blocks (start-time end-time TR #volumes)</li> <li>SE_pe1_pe2: auxiliary image for unwarping</li> <li>slicetiming_i.txt: Slice timing information in seconds</li> <li>t1: Defaced T1-weighted image (undefaced images were used for template generation)</li> <li>t2: Defaced T1-weighted image</li> </ul> <p><br> <em>templates</em></p> <ul> <li>EPI-template: Generated from unwarped func_ref images <ul> <li>Transformations for all subjects (can be applied using antsApplyTransforms)</li> <li>wb_mask</li> </ul> </li> <li>EPI-2-T1: Transformation from EPI to T1-template</li> <li>MNI-template: T1-template warped into MNI_152 <ul> <li>wb_mask</li> </ul> </li> <li>T1-template: Generated from t1 images <ul> <li>hyp_mask</li> <li>wb_mask</li> </ul> </li> <li>T1-2-MNI: Transformation from T1 to MNI_152-template</li> </ul> <p><br> <em>JPreprocessing</em><br> Preprocessing pipeline</p>
Preprocessed auditory task-fMRI files of common marmoset
<p>Preprocessed auditory task-fMRI files of common marmoset.</p> <p>Original data was acquired by https://doi.org/10.1016/j.jneumeth.2022.109737</p> <p>Related github repository is https://github.com/takuto-okuno-riken/oku2023dmn</p>
Supporting data (MEG and fMRI) for brain model
<p>Supporting data (MEG and fMRI) for the brain model published here: https://doi.org/10.5281/zenodo.7988965.</p>
Embodiment of action-related language in the native and a late foreign language – An fMRI-study
<p>Theories of embodied cognition postulate that language processing activates similar sensory-motor structures as<br> when interacting with the environment. Only little is known about the neural substrate of embodiment in a<br> foreign language (L2) as compared to the mother tongue (L1). In this fMRI study, we investigated embodiment of<br> motor and non-motor action verbs in L1 and L2 including 31 late bilinguals. Half had German as L1 and French as<br> L2, and the other half vice-versa. We collapsed across languages to avoid the confound between language and<br> order of language acquisition. Region of interest analyses showed stronger activation in motor regions during L2<br> than during L1 processing, independently of the motor-relatedness of the verbs. Moreover, a stronger involvement<br> of motor regions for motor-related as compared to non-motor-related verbs, similarly for L1 and L2, was<br> found. Overall, the similarity between L1 and L2 embodiment seems to depend on individual and contextual<br> factors.</p>
Resting-state fMRI data for locating causal hubs of memory consolidation in spontaneous brain network
<p>The mouse fMRI data for the paper "<strong>Locating causal hubs of memory consolidation in spontaneous brain network in male mice</strong>"<strong> </strong>published in <strong>Nature Communications </strong>(DOI: 10.1038/s41467-023-41024-z)<strong>. </strong>This includes longitudinal resting-state fMRI data in mice after behavioural training for 1-Day or 5-Day Active Place Avoidance (APA) task, acquired at post-training day 1 and day 8. Due to the large datasets, each group has been packed into several 2GB zip files. They need to be downloaded into the same folder and unpacked together (e.g. 1-Day APA Post training day 1 has five zip files starting with "1DAPA_PostDay1"). The structural and EPI templates and the ROI labels in the AMBMC atlas space are provided in the AMBMC_label.zip. </p>
Affinity matrices from fMRI data
<p>This is a set of 27x27 matrices constructed from the fMRI data for 2 participants from the ABIDE data set [Craddock et al., 2013] and 10 from the HCP Young Adult data set [Van Essen et al., 2013, Glasser et al., 2013]. A subset of 118 matrices (saved in a folder titled 'Random') was produced by sampling uniformly from the interval [0,1].</p> <p>Each matrix represents the affinity in the immediate neighbourhood of a randomly-selected vertex on the midthickness surface of the brain, and was generated with the VB toolbox as outlined in [Bajada et al., 2020]. Note, however, that the referenced work takes a surface-based approach, but we used a version of the toolbox that is newer and employs a hybrid method whereby the randomly-chosen vertex is first mapped to the corresponding voxel, a 27-voxel neighbourhood (which includes the original voxel) is identified, and the affinity in said neighbourhood calculated. The element <em>a</em><sub>ij</sub> of the matrix constructed for the neighbourhood indicates the degree of correlation between voxels<em> i </em>and<em> j</em> on the basis of their fMRI data. The particular version of the VB toolbox utilised for this work is available at https://github.com/VBIndex/py_vb_toolbox/tree/Local-gradients-paper (but please note that it would have to be modified to print out the affinity matrices for a set of randomly-selected vertices).</p> <p>The matrices are made available as numpy array files and are separated into folders according to the origin of the fMRI data used to construct them (the prefix ABIDE_ added to folder names indicates that the matrices contained inside were generated with the fMRI data of subjects from the ABIDE data set, while HCP is the Human Connectome Project counterpart. Each folder pertains to a different data subject, but note that the numbering scheme employed (DS1, DS2, etc.) has no correspondence to the original ABIDE or HCP participant numbers.</p>
Mechanisms of individualized fMRI neuromodulation for visual perception and visual imagery
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fMRI data of morphological processing in Hebrew dyslexic and typical readers
<p>see the attached documentation file</p>
Re-imagining fMRI for awake behaving infants
<p>Thousands of functional magnetic resonance imaging (fMRI) studies have provided important insight into the human brain. However, only a handful of these studies tested infants while they were awake, because of the significant and unique methodological challenges involved. We report our efforts to address these challenges, with the goal of creating methods for awake infant fMRI that can reveal the inner workings of the developing, preverbal mind. We use these methods to collect and analyze two fMRI datasets obtained from infants during cognitive tasks, released publicly with this paper. In these datasets, we explore and evaluate data quantity and quality, task-evoked activity, and preprocessing decisions. We disseminate these methods by sharing two software packages that integrate infant-friendly cognitive tasks and eye-gaze monitoring with fMRI acquisition and analysis. These resources make fMRI a feasible and accessible technique for cognitive neuroscience in awake and behaving human infants.</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.