This dataset contains the Midnight Scanning Club (MSC) data, a dataset focused on the precise characterization of ten individual subjects via collection of large amounts of per-individual data. Each subject underwent twelve separate two-hour scanning sessions. In the first two sessions we collected four T1 images, four T2 images, four MR angiograms, and eight MR venograms. In the last ten sessions we collected five hours of resting-state fMRI data and over five and a half hours of task fMRI data across three different tasks. Participants also underwent extensive neuropsychological testing. These raw data are all provided here.

In addition to the raw data, we also provide several derivatives processed using both a volumetric (Talaraich-space) and a surface-based (fs_LR_32k space) pipeline. Volumetric derivatives include cross-session-average T1 and T2 images linearly registered to atlas (Talaraich) space; as well as resting-state data from each scanning session that has been fully preprocessed, motion-censored, and confound-regressed. Surface pipeline derivatives include cortical surfaces which were segmented from the T1 scans using freesurfer, hand-edited, and registered to fs_LR atlas space; resting-state data from each scanning session that has been fully preprocessed, motion-censored, and confound-regressed in CIFTI format (cortical: fs_LR32k; subcortical: Talaraich); cortical parcellations estimated from the resting-state data; vertex-wise whole-brain networks estimated from the resting-state data; task timecourses in CIFTI (cortical: fs_LR32k; subcortical: Talaraich) space; a selection of task contrast images in CIFTI (cortical: fs_LR32k; subcortical: Talaraich) space; myelin maps estimated from the T1- and T2-weighted anatomical scans; and matrices describing the physical geodesic/euclidean distances between every two points in the cifti image.

Details of this dataset are more fully described in Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, Ortega M, Hoyt-Drazen C, Gratton C, Sun H, et al. (2017). Precision Functional Mapping of Individual Human Brains. Neuron 95, 791–807. This manuscript should be cited when publishing work using this data.

Comments added by Openfmri Curators

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General Comments

Defacing

Defacing was performed by the submitter.

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000144/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000144. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

• A number of the BOLD runs were unusable due to motion and thus were left out of this dataset.
• The defacing software may have removed small amounts of brain in a few anatomical images (see sub-40 and sub-41)

Participants Twenty people with borderline personality disorder were recruited from outpatient and support services from around Edinburgh, Scotland. Diagnoses were confirmed using the Structured Clinical Interview for DSM-IV (SCID-II). Current symptoms were assessed using the Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD [1]). Adverse childhood events were assessed using the Childhood Trauma Questionnaire (CTQ [2]). Fifteen BPD participants were receiving antidepressant medication and twelve were taking antipsychotic medication. Twenty age- and sex-matched controls were recruited from the community, however four were excluded due to technical issues during scanning, leaving sixteen controls. Exclusion criteria for all participants included pregnancy, MRI contraindications, diagnosis of a psychotic disorder, previous head injury or current illicit substance dependence. Controls met the additional criteria of no personal or familial history of major mental illness. Ethical approval was obtained from the Lothian National Health Service Research Ethics Committee, and all participants provided written informed consent before taking part.

Experimental task Participants performed the Cyberball social exclusion task [3] during functional magnetic resonance imaging (fMRI), adapted from a previous implementation by Kumar et al 2009 [4]. The task involves playing “catch” with two computer-controlled players, during which the participant can be systematically included or excluded from the game. We used this task as it assesses neural responses to social exclusion, is known to activate a range of social brain regions [5] and is amenable to reinforcement learning modelling [4]. The task was modified such that inclusion was varied parametrically over four levels: 0%, 33%, 66% and 100%, achieved by arranging the task into blocks of nine throws, respectively involving zero, one, two or three throws to the participant. Here, 100% inclusion means the degree to which the participant was included was equal to that of the other two players, with each receiving three throws per nine-throw block. Participants were asked to imagine that the other players were real, as exclusion by both human or simulated players has been previously reported to be similarly distressing [6-8]. When the participant received the ball, they indicated which computer player they wished to throw the ball to with a button press. There were four repetitions of each inclusion level, providing 16 experimental blocks in total, with the first block being 100% inclusion, and all subsequent blocks being randomised. Each throwing event had a mean duration of 2700ms, with each being preceded by randomised jitter that was in part adjusted to accommodate the participant’s reaction time from the previous trial, when applicable. This was achieved by comparing the total duration of the previous trial, including reaction time, with the ideal trial time of 2700ms: if this value was exceeded, a random jitter between 0 and 1000ms was subtracted from the mean jitter time of 1500ms; otherwise, the random jitter was added to 1500ms. Jitter therefore varied between 500ms and 2500ms. Mean block duration was 24s, with onsets denoted by the appearance of the cartoon figures following rest, and offsets by the conclusion of the final throw animation. Blocks were randomized, and interleaved with 13s rest blocks. Within blocks, throwing events were jittered to permit event disambiguation for reinforcement learning analysis.

Neuroimaging Scanning took place at the Clinical Research Imaging Centre in Edinburgh, using a 3T Siemens Magnetom Verio scanner. Echo Planar Blood Oxygen Level Dependent images were acquired axially with a TR 1560ms, TE 26ms, flip angle 66’, field of view 220 x 220mm, in-plane resolution 64 x 64, 26 interleaved slices, 347 volumes, resolution 3.4 x 3.4 x 5mm. A high resolution T1 MPRAGE structural image was acquired with TR 2300ms, TE 2.98ms, flip angle 90’, field of view 256 x 256mm, in-plane resolution 256 x 256, 160 interleaved slices, resolution 1 x 1 x 1mm.

References 1 Zanarini MC, Vujanovic AA, Parachini EA, Boulanger JL, Frankenburg FR, Hennen J. Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD): a continuous measure of DSM-IV borderline psychopathology. J Pers Disord 2003; 17: 233–242 2 Bernstein DP, Fink L. Childhood trauma questionnaire: A retrospective self-report: Manual. Psychological Corporation, 1998. 3 Williams KD, Cheung CK, Choi W. Cyberostracism: effects of being ignored over the Internet. J Pers Soc Psychol 2000; 79: 748–762. 4 Kumar P, Waiter G, Ahearn TS, Milders M, Reid I, Steele JD. Frontal operculum temporal difference signals and social motor response learning. Hum Brain Mapp 2009; 30: 1421–1430. 5 Eisenberger NI, Lieberman MD, Williams KD. Does rejection hurt? An FMRI study of social exclusion. Science 2003; 302: 290–292. 6 Zadro L, Williams KD, Richardson R. How low can you go? Ostracism by a computer is sufficient to lower self-reported levels of belonging, control, self-esteem, and meaningful existence. Journal of Experimental Social Psychology 2004; 40: 560–567. 7 Sebastian CL, Tan GCY, Roiser JP, Viding E, Dumontheil I, Blakemore S-J. Developmental influences on the neural bases of responses to social rejection: implications of social neuroscience for education. NeuroImage 2011; 57: 686–694. 8 Gradin VB, Waiter G, Kumar P, Stickle C, Milders M, Matthews K et al. Abnormal neural responses to social exclusion in schizophrenia. PLoS ONE 2012; 7: e42608.

Comments added by Openfmri Curators

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Defacing

Defacing was performed by the submitter.

Quality Control

Mriqc output was not run on this dataset due to issues we are having with the software. It will be included in the next revision.

Where to discuss the dataset

1) www.openfmri.org/dataset/ds******/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and dsXXXXXX accession number. 3) Send an email to submissions@openfmri.org. Please include the dsXXXXXX accession number in your email.

Bids-validator Output

1: This file is not part of the BIDS specification, make sure it isn't included in 

the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. (code: 1 - NOT_INCLUDED) /participants.json Evidence: participants.json

  Summary:         Available Tasks:    Available Modalities:
  116 Files, 2.01GB    Cyberball        T1w
  36 - Subjects                  bold
  1 - Session

stab_map_sub04_netPM-VIS_state0.nii (old ext: .nii)

Collection description

Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, i.e. they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 minutes) time windows into “states” with highly similar seed parcel. We splitted individual time series of the Midnight Scan Club sample into two independent sets of 2.5 hours (test and retest). We found that average within-state parcels, called stability maps, were highly reproducible (over .9 test-retest spatial correlation) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may thus impact how we model the rich interactions between brain networks in health and disease.

Subject species

homo sapiens

Map type

Other

This dataset was obtained from the OpenfMRI project (http://www.openfmri.org). Accession #: ds102 Description: Flanker task (event-related)

The "NYU Slow Flanker" dataset comprises data collected from 26 healthy adults (age and sex included in Slow_Flanker_age_sex.txt) while they performed a slow event-related Eriksen Flanker task.

**Please note that all data have been uploaded regardless of quality it is up to the user to check for data quality (movement etc).

On each trial (inter-trial interval (ITI) varied between 8 s and 14 s; mean ITI=12 s),participants used one of two buttons on a response pad to indicate the direction of a central arrow in an array of 5 arrows. In congruent trials the flanking arrows pointed in the same direction as the central arrow (e.g., < < < < <), while in more demanding incongruent trials the flanking arrows pointed in the opposite direction (e.g., < < > < <).

Subjects performed two 5-minute blocks, each containing 12 congruent and 12 incongruent trials, presented in a pseudorandom order.

Functional imaging data were acquired using a research dedicated Siemens Allegra 3.0 T scanner, with a standard Siemens head coil, located at the NYU Center for Brain Imaging.

We obtained 146 contiguous echo planar imaging (EPI) whole-brainfunctional volumes (TR=2000 ms; TE=30 ms; flip angle=80, 40 slices, matrix=64x64; FOV=192 mm; acquisition voxel size=3x3x4mm) during each of the two flanker task blocks. A high-resolution T1-weighted anatomical image was also acquired using a magnetization prepared gradient echo sequence (MPRAGE, TR=2500 ms; TE= 3.93 ms; TI=900 ms; flip angle=8; 176 slices, FOV=256 mm).

Please cite one of the following references if you use these data:

Kelly, A.M., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2008). Competition between functional brain networks mediates behavioral variability. Neuroimage, 39(1):527-37

Mennes, M., Kelly, C., Zuo, X.N., Di Martino, A., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage, 50(4):1690-701. doi: 10.1016/j.neuroimage.2010.01.002. Epub 2010 Jan 15. Erratum in: Neuroimage. 2011 Mar 1;55(1):434

Mennes, M., Zuo, X.N., Kelly, C., Di Martino, A., Zang, Y.F., Biswal, B., Castellanos, F.X., Milham, M.P. (2011). Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage, 54(4):2950-9. doi: 10.1016/j.neuroimage.2010.10.046

This dataset is made available under the Public Domain Dedication and License v1.0, whose full text can be found at http://www.opendatacommons.org/licenses/pddl/1.0/. We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge the OpenfMRI project and NSF Grant OCI-1131441 (R. Poldrack, PI) in any publications.

Comments added by OpenNeuro Curators

Known Issues

N/A

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

s dataset is a re-publishing in BIDS format of a dataset previously published on figshare: Maclaren, Julian; Han, Zhaoying; B. Vos, Sjoerd; Fischbein, Nancy; Bammer, Roland (2014): Test-retest data, comprising multiple T1-weighted MRI brain volumes. figshare. https://doi.org/10.6084/m9.figshare.929651_D7

The dataset comprises three participants, each of whom was scanned 40 times.

Details of this dataset are described in Maclaren et al, Scientific Data 1:140037 (2014), doi: 10.1038/sdata.2014.37, which should be cited when publishing work using this data.

The dataset was defaced and rearranged into BIDS format by Gustav Nilsonne (gustav.nilsonne@ki.se) with assistance from Daniel Samsami.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Defacing was performed by the submitter using SPM12.

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000239/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000239. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Description of the ASL sequence A sequence with pseudo-continuous labeling, background suppression and 3D RARE Stack-Of-Spirals readout with optional through-plane acceleration was implemented for this study. At the beginning of the sequence, gradients were rapidly played with alternating polarity to correct for their delay in the spiral trajectories, followed by two preparation TRs, to allow the signal to reach the steady state. A non-accelerated readout was played during the preparation TRs, in order to obtain a fully sampled k-space dataset, used for calibration of the parallel imaging reconstruction kernel, needed to reconstruct the skipped kz partitions in the accelerated images.

Description of study Single-shot and two-shot versions of the accelerated sequence were acquired during rest. For each participant, first a high-resolution anatomical T1-weighted image was acquired with a magnetization prepared rapid gradient echo (MPRAGE) sequence. Subjects were instructed to remain still and awake, while resting perfusion data were acquired using either 1-shot or 2-shot 1D-accelerated readout. 64 and 32 label-control images were acquired, respectively, during a total scan time of 5 min. Labeling and PLD times where 1.8 and 1.8 s. Two M0 images with long TR and no magnetization preparation were acquired per run for CBF quantification purposes.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000235/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000235. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output

Experiment

Three healthy adult participants wore a cast covering the entire right upper extremity for two weeks. They were scanned every day for 6-9 weeks. Scans included 42-64 daily 30-minute resting-state functional MRI scans before, during and after casting. Participants later underwent 12-24 additional scans as part of a control experiment. In all, we collected 27-43 hours of resting-state functional MRI data in each individual.

Participants also performed a block-design movement task (right hand, left hand, right foot, left foot, tongue) for 8 minutes each night prior to casting.

Details of this dataset are described in Newbold et al., Plasticity and Spontaneous Activity Pulses in Disused Human Brain Circuits, Neuron (2020), https://doi.org/10.1016/j.neuron.2020.05.007. This manuscript should be cited whenever publishing work using this dataset.

Raw data

Sessions are grouped into 5 conditions. 3 conditions (pre, cast, post) correspond to the original experiment. 2 conditions (on, off) correspond to a control experiment in which participants wore a removable cast during scanning (on sessions) but were not casted during daily life.

Two participants were also studied in a previous experiment, the Midnight Scan Club (MSC) experiment (Gordon et al, 2017, https://openneuro.org/datasets/ds000224). sub-cast1 was sub-MSC02. sub-cast2 was sub-MSC06. Carried forward many of the methods from the MSC experiment to the current study.

MSC participants were scanned using a 3T Siemens Trio MRI scanner. BOLD data were acquired at a spatial resolution of 4mm, single-band, with a TR of 2.2s. We used identical sequences for sub-cast1 during the original cast experiment (but not during the later control experiment).

After running sub-cast1, a new MRI scanner became available. sub-cast2 and sub-cast3 were scanned on a 3T Siemens Prisma using new sequences. The updated scanner and sequences were also appleid to sub-cast1 during the later control experiment. BOLD data for these scans were acquired at a spatial resolution 2.4mm, multi-band 4, with a TR of 1.1s.

Pre-processed data

In addition to the raw BOLD and structural data we collected, we have also provided fully pre-processed rs-fMRI and task-fMRI data. Processing pipelines are described in Newbold et al, 2020 and all processing scripts are available on GitLab (https://gitlab.com/DosenbachGreene/cast-induced-plasticity). Processed data are provided in volume space as well as cifti space -- combined cortical surface data and sub-cortical/cerebellar volume data.

Surface projection followed methods described in Gordon et al, 2017. Derivative structural files needed for cifti creation (e.g. pial/white surfaces, subcortical masks) are provided for sub-cast2 and sub-cast3. Because sub-cast1 was scanned using the same scanner and sequences used for the MSC study, cortical projections for sub-cast1 used the projection files generated for sub-MSC02 (https://openneuro.org/datasets/ds000224, derivatives/surface_pipeline/sub-MSC02/fs_LR_Talairach/).

Parcellations

Surface parcellations for sub-cast3 were created using methods described by Gordon et al (2017). Corresponding parcellations for sub-cast1 and sub-cast2 can be found in the MSC dataset (https://openneuro.org/datasets/ds000224, derivatives/surface_pipeline/sub-{subject}/surface_parcellation).

Audio-Visual Memory

Aims:

Needs to be specified.

Methods

Behavior task: Needs to be specified.

fMRI: Needs to be specified.

General findings and importance:

Needs to be specified.

Sample Size

Needs to be specified.

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

This dataset was obtained from the OpenfMRI project (http://www.openfmri.org). Accession #: ds000203 Description: Visual Imagery and False Memory task

Please cite the following references if you use these data:

Stephan-Otto C, Siddi S, Senior C, Muńoz-Samons D, Ochoa S, Sánchez Laforga AM, Brébion G (Visual imagery and false memory for pictures: a Functional Magnetic Resonance Imaging study in healthy participants. doi: 10.1371/journal.pone.0169551

Release history: 6/2/2016: initial release

This dataset is made available under the Public Domain Dedication and License v1.0, whose full text can be found at http://www.opendatacommons.org/licenses/pddl/1.0/. We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge the OpenfMRI project and NSF Grant OCI-1131441 (R. Poldrack, PI) in any publications.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000203/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000203. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Data acquisition methods have been described in detail in Dalenberg et al. (2017, Plos One)

———————————————————————————————— IMPORTANT NOTES ———————————————————————————————— Due to technical difficulties with the PRESTO sequence several volumes were missing or broken. To fix the timing of the data, missing volumes need to be filled and broken volumes need replacement.

Missing/borken volumes:

Participant_id run volumes_missing volumes_broken sub-02 r1 [1 2 3 4 5 6 7 8 9 10 11] [561] sub-08 r2 [1 2 3 4 5 6 7 8 9 10] [561] sub-08 r3 [1 2 3 4 5 6 7 8 9 10 11] [559] sub-10 r2 [1 2 3 4 5 6 7 8 9 10] sub-18 r2 [1 2 3 4 5 6 7 8 9 10] [559]

Further missing data due to difficulties with the gustometer: sub-03 sub-06 sub-16

Stimulus information

Flavour_Products Description fc_cap Oral Nutritional Supplement Product brand: Forticare, flavour Cappucino fc_ole Oral Nutritional Supplement Product brand: Forticare, flavour Orange Lemon fc_peg Oral Nutritional Supplement Product brand: Forticare, flavour Peach Ginger fm_apr Oral Nutritional Supplement Product brand: Fortimel, flavour Apricot fm_neu Oral Nutritional Supplement Product brand: Fortimel, flavour Neutral fm_van Oral Nutritional Supplement Product brand: Fortimel, flavour Vanilla

Product compositions

Fortimel: Milk protein concentrate, water, maltodextrin, vegetable oils, sucrose, acidity regulator (citric acid), emulsifier (soy lecithin), cocoa, flavoring (vanilla/apricot), tri-potassium citrate, choline chloride, calcium hydroxide, sodium L-ascorbate, potassium hydroxide, trisodium citrate, DL-α-tocopherol, ferrous lactate, nicotinamide, retinyl acetate, copper gluconate, manganese sulfate, zinc sulfate, sodium selenite, chromium chloride, D-calcium pantothenate, D-biotin, cholecalciferol, pyridoxine hydrochloride, pteroylmonoglutamic acid, thiamine hydrochloride, sodium fluoride, sodium molybdate, riboflavin, potassium iodide, phytomenadione.

Forticare: Demineralised water, glucose syrup, sodium molybdate, milk protein isolate, sodium fluoride, trehalose, sucrose, vegetable oils, dietary fibres (oligofructose, inulin, cellulose, resistant starch), fish oil, whey protein concentrate (from milk), tri potassium citrate, flavour, sodium chloride, tri sodium citrate, colour (E150d), flavour, magnesium hydrogen phosphate, choline chloride, carotenoids (contains soy) (b-carotene, lutein, lycopene), sodium L-ascorbate, magnesium carbonate, potassium hydroxide, taurine, DL-a-tocopheryl acetate, L-carnitine, ferrous lactate, zinc sulphate, nicotinamide, retinyl aceteate, sodium selenite, manganese sulphate, copper gluconate, pyridoxine hydrochloride, calcium D-pantothenate, pteroylmonoglutamic acid, D-biotin, chromium chloride, cholecalciferol, cyanocobalamin, thiamin hydrochloride, sodium molybdate, sodium fluoride, riboflavin, potassium iodide, phytomenadione.

General procedure per trial

Period Description warning_star Warning for upcomming flavour stimulus using a star on the screen (2s) taste_and_swallow Instruction to first taste (3.5s) and then swallow (4s) the 2ml flavour stimulus judge Passivily judge the stimulus (22.5s) while watching a fixation cross. rate Actively rate the stimulus on a 7 point liking scale (self paced) rinse Rinsing procedure to rinse the palate using 2ml of a 5% artificial saliva solution (29s)

Stimulus labels

All stimulus screens (except "warning_star" and "rinse") are coded as a combination of the general procedure and Flavour product as follows:

taste_and_swallow_fm_neu judge_fm_neu rate_fm_neu

taste_and_swallow_fc_ole judge_fc_ole rate_fc_ole

taste_and_swallow_fm_van judge_fm_van rate_fm_van

taste_and_swallow_fc_peg judge_fc_peg rate_fc_peg

taste_and_swallow_fc_cap judge_fc_cap rate_fc_cap

taste_and_swallow_fm_apr judge_fm_apr rate_fm_apr

———————————————————————————————— SCANNER & PRESTO SEQUENCE DETAILS ————————————————————————————————

3T Philips Intera MRI Scanner 32 channel head coil Variable number of TRs per run (depending on answering times)

———————————————————————————————— SCANNER PRESTO EXAM CARD ———————————————————————————————— Coil selection 1 = "SENSE-Head-32P"; element selection = "selection 1"; Coil selection 2 = "SENSE-Head-32AH"; element selection = "selection 1"; Dual coil = "yes"; CLEAR = "yes"; body tuned = "no"; FOV FH (mm) = 230; AP (mm) = 230; RL (mm) = 153; Voxel size FH (mm) = 3; AP (mm) = 3; RL (mm) = 3; Recon voxel size (mm) = 2.875; Fold-over suppression = "no"; Slice oversampling = "default"; Reconstruction matrix = 80; SENSE = "yes"; P reduction (AP) = 1.89999998; P os factor = 1; S reduction (RL) = 1.89999998; Overcontiguous slices = "no"; Stacks = 1; slices = 51; slice orientation = "sagittal"; fold-over direction = "AP"; fat shift direction = "P"; Stack Offc. AP (P=+mm) = -8.07165337; RL (L=+mm) = -2.93776155; FH (H=+mm) = 21.0553951; Ang. AP (deg) = 0.122744754; RL (deg) = 1.3835777; FH (deg) = -0.273202777; Chunks = 1; Large table movement = "no"; PlanAlign = "no"; REST slabs = 0; Interactive positioning = "no"; Patient position = "head first"; orientation = "supine"; Scan type = "Imaging"; Scan mode = "3D"; technique = "FFE"; Contrast enhancement = "T1"; Acquisition mode = "cartesian"; Fast Imaging mode = "EPI"; 3D non-selective = "no"; shot mode = "multishot"; EPI factor = 17; Echoes = 1; partial echo = "no"; shifted echo = "yes"; TE>TR shift = 1; M add factor = -4; P add factor = -4; S add factor = -4; TE = "user defined"; (ms) = 10; Flip angle (deg) = 7; TR = "user defined"; (ms) = 20; Halfscan = "no"; Water-fat shift = "minimum"; Shim = "auto"; Fat suppression = "no"; Water suppression = "no"; MTC = "no"; Research prepulse = "no"; Diffusion mode = "no"; SAR mode = "high"; B1 mode = "default"; PNS mode = "low"; Gradient mode = "default"; SofTone mode = "no"; Cardiac synchronization = "no"; Respiratory compensation = "no"; Navigator respiratory comp = "no"; Flow compensation = "no"; fMRI echo stabilisation = "no"; NSA = 1; Angio / Contrast enh. = "no"; Quantitative flow = "no"; Manual start = "yes"; Dynamic study = "individual"; dyn scans = 700; dyn scan times = "shortest"; FOV time mode = "default"; dummy scans = 2; immediate subtraction = "no"; fast next scan = "no"; synch. ext. device = "yes"; start at dyn. = 1; interval (dyn) = 1; dyn stabilization = "yes"; prospect. motion corr. = "no"; Keyhole = "no"; Arterial Spin labeling = "no"; Preparation phases = "full"; Interactive F0 = "no"; B0 field map = "no"; B1 field map = "no"; MIP/MPR = "no"; Images = " M", (3) " no"; Autoview image = " M"; Calculated images = (4) " no"; Reference tissue = "Grey matter"; Preset window contrast = "soft"; Reconstruction mode = "real time"; reuse memory = "no"; Save raw data = "no"; Hardcopy protocol = "no"; Ringing filtering = "default"; Geometry correction = "default"; Elliptical k-space shutter = "default"; IF_info_seperator = 1634755923; Total scan duration = "17:56.5"; Rel. signal level (%) = 100; Act. TR/TE (shifted) (ms) = "20 / 30"; Dyn. scan time = "00:01.5"; Time to k0 = "0.750"; ACQ matrix M x P = "76 x 58"; ACQ voxel MPS (mm) = "3.03 / 3.92 / 3.00"; REC voxel MPS (mm) = "2.88 / 2.88 / 3.00"; Scan percentage (%) = 77.272728; Act. WFS (pix) / BW (Hz) = "4.999 / 86.9"; BW in EPI freq. dir. (Hz) = "2654.6"; Min. WFS (pix) / Max. BW (Hz) = "4.976 / 87.3"; Min. TR/TE (ms) = "19 / 7.4"; ES-FFE: added M area = -15.5215797; ES-FFE: added P area = -15.5215797; ES-FFE: added S area = -15.5215797; SAR / head = "< 2 %"; Whole body / level = "0.0 W/kg / normal"; B1 rms = "0.33 uT"; PNS / level = "46 % / normal"; Sound Pressure Level (dB) = 14.8522921;

———————————————————————————————— PUBLICATIONS ———————————————————————————————— Dalenberg, J.R., Weitkamp, L., Renken, R.J., Nanetti, L., ter Horst G.J., (2017). Flavor pleasantness processing in the ventral emotion network. Plos One _, doi: _

Comments added by Openfmri Curators

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General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000218/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000218 accession number. 3) Send an email to submissions@openfmri.org. Please include the ds000218 accession number in your email.

Known Issues

N/A

Bids-validator Output

u should define 'SliceTiming' for this file. If you don't provide this information slice time correction will not be possible. It can be included one of the following locations: /task-ONSflavourtask_bold.json,

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

All anatomical images were defaced by the submitter using mri_deface (freesurfer), with the exception of sub-CTL12_T1w_pydeface.nii.gz, sub-ODN01_T1w_pydeface.nii.gz, sub-ODP10_T1w_pydeface.nii.gz and sub-ODP12_T1w_pydeface.nii.gz. We defaced these four images using pydeface. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/stable/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000245/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000245. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A