MPI-Leipzig_Mind-Brain-Body

uploaded by Franklin Feingold on 2018-03-23 - over 3 years ago
last modified on 2020-07-22 - over 1 year ago
authored by Anahit Babayan, Blazeij Baczkowski, Roberto Cozatl, Maria Dreyer, Haakon Engen, Miray Erbey, Marcel Falkiewicz, Nicolas Farrugia, Michael Gaebler, Johannes Golchert, Laura Golz, Krzysztof Gorgolewski, Philipp Haueis, Julia Huntenburg, Rebecca Jost, Yelyzaveta Kramarenko, Sarah Krause, Deniz Kumral, Mark Lauckner, Daniel S. Margulies, Natacha Mendes, Katharina Ohrnberger, Sabine Oligschläger, Anastasia Osoianu, Jared Pool, Janis Reichelt, Andrea Reiter, Josefin Röbbig, Lina Schaare, Jonathan Smallwood, Arno Villringer
74610297
Your dataset is no longer valid. You must fix the 1 Error to use all of the site features.

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ses-02/func/sub-010029_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

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ses-02/func/sub-010029_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz

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ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz') does not point to an existing file('ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010083/').

ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010133/ses-02/fmap/sub-010133_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz

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ses-02/func/sub-010133_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010133/ses-02/fmap/sub-010133_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz

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ses-02/func/sub-010133_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010180/ses-02/fmap/sub-010180_ses-02_acq-GEfmap_run-01_magnitude1.nii.gz

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ses-02/func/sub-010180_ses-02_task-rest_acq-PA_run-01_bold.nii.gz

/sub-010180/ses-02/fmap/sub-010180_ses-02_acq-GEfmap_run-01_magnitude2.nii.gz

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ses-02/func/sub-010180_ses-02_task-rest_acq-PA_run-01_bold.nii.gz

/sub-010045/ses-01/dwi/sub-010045_ses-01_dwi.nii.gz

The most common set of dimensions is: 128,128,88,67 (voxels), This file has the dimensions: 128,128,88,44 (voxels).

/sub-010087/ses-02/func/sub-010087_ses-02_task-rest_acq-PA_run-01_bold.nii.gz

The most common set of dimensions is: 88,88,64,657 (voxels), This file has the dimensions: 88,88,64,356 (voxels).

/sub-010189/ses-02/anat/sub-010189_ses-02_acq-lowres_FLAIR.nii.gz

The most common set of dimensions is: 384,512,28 (voxels), This file has the dimensions: 384,512,35 (voxels).

/sub-010201/ses-02/func/sub-010201_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

The most common set of dimensions is: 88,88,64,657 (voxels), This file has the dimensions: 88,88,64,643 (voxels).

/participants.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: gender, age (5-year bins) not defined, please define in: /participants.json


OpenNeuro Accession Number: ds000221
Files: 12040, Size: 369.78GB, Subjects: 318, Sessions: 2
Available Tasks: Rest
Available Modalities: MRI

README

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

Authors

  • Anahit Babayan
  • Blazeij Baczkowski
  • Roberto Cozatl
  • Maria Dreyer
  • Haakon Engen
  • Miray Erbey
  • Marcel Falkiewicz
  • Nicolas Farrugia
  • Michael Gaebler
  • Johannes Golchert
  • Laura Golz
  • Krzysztof Gorgolewski
  • Philipp Haueis
  • Julia Huntenburg
  • Rebecca Jost
  • Yelyzaveta Kramarenko
  • Sarah Krause
  • Deniz Kumral
  • Mark Lauckner
  • Daniel S. Margulies
  • Natacha Mendes
  • Katharina Ohrnberger
  • Sabine Oligschläger
  • Anastasia Osoianu
  • Jared Pool
  • Janis Reichelt
  • Andrea Reiter
  • Josefin Röbbig
  • Lina Schaare
  • Jonathan Smallwood
  • Arno Villringer

Dataset DOI

10.18112/openneuro.ds000221.v1.0.0

License

CC0

Acknowledgements

How to Acknowledge

This data was obtained from the OpenfMRI database. Its accession number is ds000221.

Funding

  • This work was partially supported by the Volkswagen Foundation (AZ.: 89 440)

References and Links

Ethics Approvals

How To Cite

Copy
Anahit Babayan and Blazeij Baczkowski and Roberto Cozatl and Maria Dreyer and Haakon Engen and Miray Erbey and Marcel Falkiewicz and Nicolas Farrugia and Michael Gaebler and Johannes Golchert and Laura Golz and Krzysztof Gorgolewski and Philipp Haueis and Julia Huntenburg and Rebecca Jost and Yelyzaveta Kramarenko and Sarah Krause and Deniz Kumral and Mark Lauckner and Daniel S. Margulies and Natacha Mendes and Katharina Ohrnberger and Sabine Oligschläger and Anastasia Osoianu and Jared Pool and Janis Reichelt and Andrea Reiter and Josefin Röbbig and Lina Schaare and Jonathan Smallwood and Arno Villringer (2020). MPI-Leipzig_Mind-Brain-Body. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds000221.v1.0.0
More citation info

MPI-Leipzig_Mind-Brain-Body

uploaded by Franklin Feingold on 2018-03-23 - over 3 years ago
last modified on 2020-07-22 - over 1 year ago
authored by Anahit Babayan, Blazeij Baczkowski, Roberto Cozatl, Maria Dreyer, Haakon Engen, Miray Erbey, Marcel Falkiewicz, Nicolas Farrugia, Michael Gaebler, Johannes Golchert, Laura Golz, Krzysztof Gorgolewski, Philipp Haueis, Julia Huntenburg, Rebecca Jost, Yelyzaveta Kramarenko, Sarah Krause, Deniz Kumral, Mark Lauckner, Daniel S. Margulies, Natacha Mendes, Katharina Ohrnberger, Sabine Oligschläger, Anastasia Osoianu, Jared Pool, Janis Reichelt, Andrea Reiter, Josefin Röbbig, Lina Schaare, Jonathan Smallwood, Arno Villringer
74610297

OpenNeuro Accession Number: ds000221
Files: 12040, Size: 369.78GB, Subjects: 318, Sessions: 2
Available Tasks: Rest
Available Modalities: MRI

README

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

Authors

  • Anahit Babayan
  • Blazeij Baczkowski
  • Roberto Cozatl
  • Maria Dreyer
  • Haakon Engen
  • Miray Erbey
  • Marcel Falkiewicz
  • Nicolas Farrugia
  • Michael Gaebler
  • Johannes Golchert
  • Laura Golz
  • Krzysztof Gorgolewski
  • Philipp Haueis
  • Julia Huntenburg
  • Rebecca Jost
  • Yelyzaveta Kramarenko
  • Sarah Krause
  • Deniz Kumral
  • Mark Lauckner
  • Daniel S. Margulies
  • Natacha Mendes
  • Katharina Ohrnberger
  • Sabine Oligschläger
  • Anastasia Osoianu
  • Jared Pool
  • Janis Reichelt
  • Andrea Reiter
  • Josefin Röbbig
  • Lina Schaare
  • Jonathan Smallwood
  • Arno Villringer

Dataset DOI

10.18112/openneuro.ds000221.v1.0.0

License

CC0

Acknowledgements

How to Acknowledge

This data was obtained from the OpenfMRI database. Its accession number is ds000221.

Funding

  • This work was partially supported by the Volkswagen Foundation (AZ.: 89 440)

References and Links

Ethics Approvals

How To Cite

Copy
Anahit Babayan and Blazeij Baczkowski and Roberto Cozatl and Maria Dreyer and Haakon Engen and Miray Erbey and Marcel Falkiewicz and Nicolas Farrugia and Michael Gaebler and Johannes Golchert and Laura Golz and Krzysztof Gorgolewski and Philipp Haueis and Julia Huntenburg and Rebecca Jost and Yelyzaveta Kramarenko and Sarah Krause and Deniz Kumral and Mark Lauckner and Daniel S. Margulies and Natacha Mendes and Katharina Ohrnberger and Sabine Oligschläger and Anastasia Osoianu and Jared Pool and Janis Reichelt and Andrea Reiter and Josefin Röbbig and Lina Schaare and Jonathan Smallwood and Arno Villringer (2020). MPI-Leipzig_Mind-Brain-Body. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds000221.v1.0.0
More citation info

Dataset File Tree

Git Hash: 7b2df1b 

BIDS Validation

Your dataset is no longer valid. You must fix the 1 Error to use all of the site features.

/sub-010029/ses-02/fmap/sub-010029_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010029/ses-02/fmap/sub-010029_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz') does not point to an existing file('ses-02/func/sub-010029_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010029/').

ses-02/func/sub-010029_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010029/ses-02/fmap/sub-010029_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010029/ses-02/fmap/sub-010029_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz') does not point to an existing file('ses-02/func/sub-010029_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010029/').

ses-02/func/sub-010029_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz') does not point to an existing file('ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010083/').

ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010083/ses-02/fmap/sub-010083_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz') does not point to an existing file('ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010083/').

ses-02/func/sub-010083_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010133/ses-02/fmap/sub-010133_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010133/ses-02/fmap/sub-010133_ses-02_acq-GEfmap_run-02_magnitude1.nii.gz') does not point to an existing file('ses-02/func/sub-010133_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010133/').

ses-02/func/sub-010133_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010133/ses-02/fmap/sub-010133_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010133/ses-02/fmap/sub-010133_ses-02_acq-GEfmap_run-02_magnitude2.nii.gz') does not point to an existing file('ses-02/func/sub-010133_ses-02_task-rest_acq-PA_run-02_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010133/').

ses-02/func/sub-010133_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

/sub-010180/ses-02/fmap/sub-010180_ses-02_acq-GEfmap_run-01_magnitude1.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010180/ses-02/fmap/sub-010180_ses-02_acq-GEfmap_run-01_magnitude1.nii.gz') does not point to an existing file('ses-02/func/sub-010180_ses-02_task-rest_acq-PA_run-01_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010180/').

ses-02/func/sub-010180_ses-02_task-rest_acq-PA_run-01_bold.nii.gz

/sub-010180/ses-02/fmap/sub-010180_ses-02_acq-GEfmap_run-01_magnitude2.nii.gz

'IntendedFor' property of this fieldmap ('/sub-010180/ses-02/fmap/sub-010180_ses-02_acq-GEfmap_run-01_magnitude2.nii.gz') does not point to an existing file('ses-02/func/sub-010180_ses-02_task-rest_acq-PA_run-01_bold.nii.gz'). Please mind that this value should not include subject level directory ('/sub-010180/').

ses-02/func/sub-010180_ses-02_task-rest_acq-PA_run-01_bold.nii.gz

/sub-010045/ses-01/dwi/sub-010045_ses-01_dwi.nii.gz

The most common set of dimensions is: 128,128,88,67 (voxels), This file has the dimensions: 128,128,88,44 (voxels).

/sub-010087/ses-02/func/sub-010087_ses-02_task-rest_acq-PA_run-01_bold.nii.gz

The most common set of dimensions is: 88,88,64,657 (voxels), This file has the dimensions: 88,88,64,356 (voxels).

/sub-010189/ses-02/anat/sub-010189_ses-02_acq-lowres_FLAIR.nii.gz

The most common set of dimensions is: 384,512,28 (voxels), This file has the dimensions: 384,512,35 (voxels).

/sub-010201/ses-02/func/sub-010201_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

The most common set of dimensions is: 88,88,64,657 (voxels), This file has the dimensions: 88,88,64,643 (voxels).

/participants.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: gender, age (5-year bins) not defined, please define in: /participants.json

Dataset File Tree

Git Hash: 7b2df1b 

Comments

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By sara.sorella@unitn.it - almost 3 years ago
Hi, thank you very much for this extremely interesting dataset! May I ask you, please, when the LEMON questionnaires will be available? REALLY THANK YOU,
Sara
By cristina.scarpazza@gmail.com - over 3 years ago
Dear Uploader, many thanks for sharing this dataset. Do you please know whether there is an automatic way to download more than one image a time? For instance, if i would like to download only the T1 images of every subjects, is there an automatic way to do it, or I need to download them subject by subject manually? Many thanks. Cristina
By franklin.feingold@gmail.com - over 3 years ago
Hello,
Thank you for your comment and raising this issue. The size of this dataset uncompressed is: 390GB.
By cristina.scarpazza@gmail.com - over 3 years ago
Hi, many thanks for your reply. Your dataset is really interesting. I have two additional questions: 1. are all the participants acquired with the same scanner? Is the scanner of ses-01 the same one of ses-02? 2. is there a way to download the anatomical images only? Many many thanks.
By franklin.feingold@gmail.com - over 3 years ago
Hi, Thank you for your questions. 1. That is a good question. I found this bio archive that appears to explain the acquisition procedure: https://www.biorxiv.org/content/biorxiv/early/2017/07/18/164764.full.pdf . But I am not certain if they were all scanned with the same scanner. 2. At this time there is not a way to only download the anatomical images. You would have to download the dataset and then select out the anatomical images.
By cristina.scarpazza@gmail.com - over 3 years ago
Many thanks for your reply. Very helpful. Thanks again
By naturescarl@gmail.com - about 3 years ago
Hello,i just have a question about the download, when i finished the 20 percent,the download thread is stoping,I can't continue downloading.is that the question of my network ?
By madisonmaylong@gmail.com - about 3 years ago
Hi there. Is there any additional demographic or socioeconomic information available for the subjects in this data set?
By dtd.sms2016@gmail.com - almost 3 years ago
I cannot find the questionnaire data within the BIDS structure. Is this a separate download?
By cristina.scarpazza@gmail.com - over 3 years ago
May I please know how many GB is this dataset? It is written 3.7 GB but I am downloading it and I am at 45 GB and still not finish... Many thanks
By mbriden@ucsc.edu - about 1 year ago
I know this was asked some time ago, but for those who may have the same question: Files: 12040, Size: 369.78GB.
By lewis.dunne00@gmail.com - over 1 year ago
Hello, thanks for making this dataset open it looks really interesting. I am just having a look at this and I see that it only seems to contain resting state data, but the main description does not say that the task data are excluded. Could somebody clarify this please?
By lewis.dunne00@gmail.com - over 1 year ago
Hello, thanks for making this dataset open it looks really interesting. I am just having a look at this and I see that it only seems to contain resting state data, but the main description does not say that the task data are excluded. Could somebody clarify this please?
By lewis.dunne00@gmail.com - over 1 year ago
Hello, thanks for making this dataset open it looks really interesting. I am just having a look at this and I see that it only seems to contain resting state data, but the main description does not say that the task data are excluded. Could somebody clarify this please?
By sponticorvo@unisa.it - about 1 year ago
Hello, Thanks for sharing this data set. I have a question regarding dwi data and in particular slice acquisition time/acquisition scheme. Is there a possibility to have the complete .jason file with this information? It is now not included in the current .jason file and I need it for preprocessing. Thanks a lot
By julie.victor.pro@gmail.com - 12 months ago
Hi, is it possible to have the precise ages of the subjects during the MR sessions ? (or at least a 2-3 years bins ?) Thank you for your great MRI data ! Have a nice day.
By julie.victor.pro@gmail.com - 12 months ago
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By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By julie.victor.pro@gmail.com - 12 months ago

By xiaolinfang92@gmail.com - 5 months ago
May I ask for the resolution of the GRE SWI dataset?