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

Comments added by Openfmri Curators

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

General Comments

Defacing

Pydeface was used on all anatomical images to ensure de-identification 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/stable/

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. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

BIDS-Validator output:

1: Not all subjects/sessions/runs have the same scanning parameters. (code: 39 - INCONSISTENT_PARAMETERS) ./sub-01/func/sub-01_task-localizer_bold.nii.gz The most common set of dimensions is: 96,96,39,316 (voxels), This file has the dimensions: 96,96,39,260 (voxels). ./sub-02/func/sub-02_task-localizer_bold.nii.gz The most common set of dimensions is: 96,96,39,316 (voxels), This file has the dimensions: 96,96,39,388 (voxels).

  Summary:         Available Tasks:     Available Modalities:
  327 Files, 10.44GB    Experimental Run 1    T1w
  20 - Subjects       Experimental Run 2    bold
  1 - Session        Experimental Run 3
               Experimental Run 4
               Localizer

Known Issues

Sequential Inference VBM dataset.

These data comprise behaviour from 79 subjects on a probabilistic reversal task together with T1-weighted structural images. (The task is described in more detail in: FitzGerald et al. Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions. PLoS Computational Biology (2017))

The principal findings of the original study were that the majority of subjects employed a strategy of inferring about the joint probability of sequences of states stretching into the past, and that betwene-subject differences in strategy correlated with gery-matter density changes in various parts of the brain.

Data were collected from 43 younger adults and 36 older adults. Additionally, most of the subjects performed the Raven's matrices task, and an n-back working memory task, and results from these are also included, together with height, weight, age and sex.

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/ds000222/ 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 ds000222 accession number. 3) Send an email to submissions@openfmri.org. Please include the ds000222 accession number in your email.

Known Issues

N/A

Bids-validator Output

This dataset appears to be BIDS compatible. Summary: Available Tasks: Available Modalities: 233 Files, 1.86GB T1w 79 - Subjects 1 - Session

The MPI-Leipzig Mind-Brain-Body dataset contains MRI and behavioral data from 318 participants. Datasets for all participants include at least a structural quantitative T1-weighted image and a single 15-minute eyes-open resting-state fMRI session.

The participants took part in one or two extended protocols: Leipzig Mind-Body-Brain Interactions (LEMON) and Neuroanatomy & Connectivity Protocol (N&C). The data from LEMON protocol is included in the ‘ses-01’ subfolder; the data from N&C protocol in ‘ses-02’ subfolder.

LEMON focuses on structural imaging. 228 participants were scanned. In addition to the quantitative T1-weighted image, the participants also have a structural T2-weighted image (226 participants), a diffusion-weighted image with 64 directions (228) and a 15-minute eyes-open resting-state session (228). New imaging sequences were introduced into the LEMON protocol after data acquisition for approximately 110 participants. Before the change, a low-resolution 2D FLAIR images were acquired for clinical purposes (110). After the change, 2D FLAIR was replaced with high-resolution 3D FLAIR (117). The second addition was the acquisition of gradient-echo images (112) that can be used for Susceptibility-Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM).

The N&C protocol focuses on resting-state fMRI data. 199 participants were scanned with this protocol; 109 participants also took part in the LEMON protocol. Structural data was not acquired for the overlapping LEMON participants. For the unique N&C participants, only a T1-weighted and a low-resolution FLAIR image were acquired. Four 15-minute runs of eyes-open resting-state are the main component of N&C; they are complete for 194 participants, three participants have 3 runs, one participant has 2 runs and one participant has a single run. Due to a bug in multiband sequence used in this protocol, the echo time for N&C resting-state is longer than in LEMON — 39.4 ms vs 30 ms.

Forty-five participants have complete imaging data: quantitative T1-weighted, T2-weighted, high-resolution 3D FLAIR, DWI, GRE and 75 minutes of resting-state. Both gradient-echo and spin-echo field maps are available in both datasets for all EPI-based sequences (rsfMRI and DWI).

Extensive behavioral data was acquired in both protocols. They include trait and state questionnaires, as well as behavioral tasks. Here we only list the tasks; more extenstive descriptions are available in the manuscripts.

LEMON QUESTIONNAIRES/TASKS [not yet released]

California Verbal Learning Test (CVLT) Testbatterie zur Aufmerksamkeitsprüfung (TAP Alertness, Incompatibility, Working Memory) Trail Marking Test (TMT) Wortschatztest (WST) Leistungsprüfungssystem 2 (LPS-2) Regensburger Wortflüssigkeitstest (RWT)

NEO Five-Factor Inventory (NEO-FFI) Impulsive Behavior Scale (UPPS) Behavioral Inhibition and Approach System (BISBAS) Cognitive Emotion Regulation Questionnaire (CERQ) Measure of Affective Style (MARS) Fragebogen zur Sozialen Unterstützung (F-SozU K) The Multidimensional Scale of Perceived Social Support (MSPSS) Coping Orientations to Problems Experienced (COPE) Life Orientation Test-Revised (LOT-R) Perceived Stress Questionnaire (PSQ) the Trier Inventory of Chronic Stress (TICS) The three-factor eating questionnaire (TFEQ) Yale Food Addiction Scale (YFAS) The Trait Emotional Intelligence Questionnaire (TEIQue-SF) Trait Scale of the State-Trait Anxiety Inventory (STAI) State-Trait Anger expression Inventory (STAXI) Toronto-Alexithymia Scale (TAS) Multidimensional Mood Questionnaire (MDMQ) New York Cognition Questionnaire (NYC-Q)

N&C QUESTIONNAIRES

Adult Self Report (ASR) Goldsmiths Musical Sophistication Index (Gold-MSI) Internet Addiction Test (IAT) Involuntary Musical Imagery Scale (IMIS) Multi-Gender Identity Questionnaire (MGIQ) Brief Self-Control Scale (SCS) Short Dark Triad (SD3) Social Desirability Scale-17 (SDS) Self-Esteem Scale (SE) Tuckman Procrastination Scale (TPS) Varieties of Inner Speech (VISQ) UPPS-P Impulsive Behavior Scale (UPPS-P) Attention Control Scale (ACS) Beck's Depression Inventory-II (BDI) Boredom Proneness Scale (BP) Esworth Sleepiness Scale (ESS) Hospital Anxiety and Depression Scale (HADS) Multimedia Multitasking Index (MMI) Mobile Phone Usage (MPU) Personality Style and Disorder Inventory (PSSI) Spontaneous and Deliberate Mind-Wandering (S-D-MW) Short New York Cognition Scale (Short-NYC-Q) New York Cognition Scale (NYC-Q) Abbreviated Math Anxiety Scale (AMAS) Behavioral Inhibition and Approach System (BIS/BAS) NEO Personality Inventory Revised (NEO-PI-R) Body Consciousness Questionnaire (BCQ) Creative achievement questionnaire (CAQ) Five facets of mindfulness (FFMQ) Metacognition (MCQ-30)

N&C TASKS

Conjunctive continuous performance task (CCPT) Emotional task switching (ETS) Adaptive visual and auditory oddball target detection task (Oddball) Alternative uses task (AUT) Remote associates test (RAT) Synesthesia color picker test (SYN) Test of creative imagery abilities (TCIA)

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

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000221/ 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 ds000221. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output

A verbose bids-validator output is under '/derivatives/bidsvalidatorOutput_long'. Short version of BIDS output is as follows:

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)
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-1_mp2rage.json
    Evidence: sub-010001_ses-02_inv-1_mp2rage.json
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-1_mp2rage.nii.gz
    Evidence: sub-010001_ses-02_inv-1_mp2rage.nii.gz
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-2_mp2rage.json
    Evidence: sub-010001_ses-02_inv-2_mp2rage.json
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-2_mp2rage.nii.gz
    Evidence: sub-010001_ses-02_inv-2_mp2rage.nii.gz
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-1_mp2rage.json
    Evidence: sub-010002_ses-01_inv-1_mp2rage.json
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-1_mp2rage.nii.gz
    Evidence: sub-010002_ses-01_inv-1_mp2rage.nii.gz
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-2_mp2rage.json
    Evidence: sub-010002_ses-01_inv-2_mp2rage.json
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-2_mp2rage.nii.gz
    Evidence: sub-010002_ses-01_inv-2_mp2rage.nii.gz
  /sub-010003/ses-01/anat/sub-010003_ses-01_inv-1_mp2rage.json
    Evidence: sub-010003_ses-01_inv-1_mp2rage.json
  /sub-010003/ses-01/anat/sub-010003_ses-01_inv-1_mp2rage.nii.gz
    Evidence: sub-010003_ses-01_inv-1_mp2rage.nii.gz
  ... and 1710 more files having this issue (Use --verbose to see them all).

2: Not all subjects contain the same files. Each subject should contain the same number of files with the same naming unless some files are known to be missing. (code: 38 - INCONSISTENT_SUBJECTS)
  /sub-010001/ses-01/anat/sub-010001_ses-01_T2w.json
  /sub-010001/ses-01/anat/sub-010001_ses-01_T2w.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-highres_FLAIR.json
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-highres_FLAIR.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-lowres_FLAIR.json
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-lowres_FLAIR.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-mp2rage_T1map.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-mp2rage_T1w.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-mp2rage_defacemask.nii.gz
  /sub-010001/ses-01/dwi/sub-010001_ses-01_dwi.bval
  ... and 8624 more files having this issue (Use --verbose to see them all).

3: Not all subjects/sessions/runs have the same scanning parameters. (code: 39 - INCONSISTENT_PARAMETERS)
  /sub-010007/ses-02/anat/sub-010007_ses-02_acq-mp2rage_T1map.nii.gz
  /sub-010007/ses-02/anat/sub-010007_ses-02_acq-mp2rage_T1w.nii.gz
  /sub-010007/ses-02/anat/sub-010007_ses-02_acq-mp2rage_defacemask.nii.gz
  /sub-010045/ses-01/dwi/sub-010045_ses-01_dwi.nii.gz
  /sub-010087/ses-02/func/sub-010087_ses-02_task-rest_acq-PA_run-01_bold.nii.gz
  /sub-010189/ses-02/anat/sub-010189_ses-02_acq-lowres_FLAIR.nii.gz
  /sub-010201/ses-02/func/sub-010201_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

  Summary:           Available Tasks:    Available Modalities:
  14714 Files, 390.74GB    Rest          FLAIR
  318 - Subjects                    T1map
  2 - Sessions                     T1w
                             defacemask
                             bold
                             T2w
                             dwi
                             fieldmap
                             fieldmap

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

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-06 r1 [1 2 3 4 5 6 7 8 9 10] [] sub-08 r4 [1 2 3 4 5 6 7 8 9 10] [] sub-18 r1 [1 2 3 4 5 6 7 8 9 10] [] sub-20 r1 [1 2 3 4 5 6 7 8 9 10] []

Further missing data due to difficulties with the gustometer or brain abnormalities: sub-02 r1 sub-03 r2 sub-04 r1 sub-10 r3 sub-16 r1 r2 r3 r4 sub-19 r2 sub-22 r4

Stimulus information

Flavour_Products Description anaman Product brand DubbelFrisss, flavour Pineapple-Mango appbos Product brand DubbelFrisss, flavour Apple-Blueberry appper Product brand DubbelFrisss, flavour Apple-Peach sinman Product brand DubbelFrisss, flavour Orange-Tangerine fra Product brand Optimel, flavour Raspberry kok Product brand Optimel, flavour Coconut lim Product brand Optimel, flavour Lemon ska Product brand Optimel, flavour Orange-Cinnamon

Product compositions

DubbelFrisss Pineapple-Mango: water, fruit juices from concentrated fruit juices, (apple, pineapple 1%, mango 1%), citric acid, aroma, sugar, carbonic acid (< 0,1%). Total sugars/sodium (per 100gr): 8.6gr/0.001gr.

DubbelFrisss Apple-Blueberry: water, fruit juices from concentrated fruit juices, (apple 11%, blackcurrant 3%, aronia berry), citric acid, aroma, sugar, carbonic acid (< 0,1%). Total sugars/sodium (per 100 gr): 8.5gr/0.001gr.

DubbelFrisss Apple-Peach: water, fruit juices from concentrated fruit juices, (apple 14%, peach 1.1%), citric acid, aroma, sugar, carbonic acid (< 0,1%). Total sugars/sodium (per 100gr): 8.6gr/0.001gr.

DubbelFrisss Orange-Tangerine: water, fruit juices from concentrated fruit juices, (orange 7%, apple, tangerine 2.4%), citric acid, aroma, sugar, carbonic acid (< 0,1%). Total sugars/sodium (per 100 gr): 8.6gr/0.001gr

Optimel general: Yogurt from skimmed milk, 5% fruit juices (see below), corn starch, calcium, sucralose. Proteins/Sugars/Sodium/Calcium (per 100gr): 3.1g/3.6g/0.04gr/120mg. Vitamins from the B complex (B2: 0,21mg; B6: 0,21mg; B12: 0,38 microgr).

Optimel Raspberry: apple, 1% raspberry, aronia berry, lemon Optimel Coconut: apple, lemon, 1% coconut Optimel Lime: apple, 0.5% lemon, 0.5% lime Optimel Orange-Cinnamon: apple, 1% orange, cinnamon

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_appbos judge_appbos rate_appbos

taste_and_swallow_appper judge_appper rate_appper

taste_and_swallow_sinman judge_sinman rate_sinman

taste_and_swallow_anaman judge_anaman rate_anaman

taste_and_swallow_fra judge_fra rate_fra

taste_and_swallow_kok judge_kok rate_kok

taste_and_swallow_lim judge_lim rate_lim

taste_and_swallow_ska judge_ska rate_ska

———————————————————————————————— 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

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

General Comments

This is second set of dataset(first set of dataset for Flavor Plesantness processing is ds000218 ) for Flavor Plesantness processing study.

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/ds000219/ 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 ds000219 accession number. 3) Send an email to submissions@openfmri.org. Please include the ds000219 accession number in your email.

Known Issues

N/A

Bids-validator Output

1: You should define 'SliceTiming' for this file. If you don't provide this information slice time correction will not be possible. (code: 13 - SLICE_TIMING_NOT_DEFINED)
    /sub-01/func/sub-01_task-RPflavourtask_run-01_bold.nii.gz
      You should define 'SliceTiming' for this file. If you don't provide this