The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old

uploaded by Franklin Feingold on 2018-03-14 - over 3 years ago
last modified on 2018-07-17 - about 3 years ago
authored by Gustav Nilsonne, Sandra Tamm, Paolo d’Onofrio, Hanna Å Thuné, Johanna Schwarz, Catharina Lavebratt, Jia Jia Liu, Kristoffer NT Månsson, Tina Sundelin, John Axelsson, Peter Fransson, Göran Kecklund, Håkan Fischer, Mats Lekander, Torbjörn Åkerstedt
3779955
We found 2 Warnings in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-9001/ses-1/beh/sub-9001_ses-1_task-workingmemorytest_events.tsv

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.bval

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.bvec

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.json

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.nii.gz

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude1.json

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude1.nii.gz

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude2.json

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude2.nii.gz

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_phase1.json

This file is missing for subject sub-9001, but is present for at least one other subject.

and 2176 more files

/sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude2.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/fmap/sub-9001_ses-1_phase1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/fmap/sub-9001_ses-1_phase2.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/func/sub-9001_ses-1_task-hands_bold.nii.gz

The most common set of dimensions is: 128,128,46,183 (voxels), This file has the dimensions: 128,128,45,202 (voxels). The most common resolution is: 1.72mm x 1.72mm x 2.40mm x 3.00s, This file has the resolution: 1.88mm x 1.88mm x 2.40mm x 3.00s.

/sub-9001/ses-1/func/sub-9001_ses-1_task-rest_bold.nii.gz

The most common set of dimensions is: 128,128,49,192 (voxels), This file has the dimensions: 128,128,49,193 (voxels).

/sub-9001/ses-2/dwi/sub-9001_ses-2_dwi.nii.gz

The most common set of dimensions is: 96,96,63,50 (voxels), This file has the dimensions: 256,256,62,50 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.30mm x 7.00s, This file has the resolution: 0.86mm x 0.86mm x 2.30mm x 7.00s.

/sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude2.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-2/fmap/sub-9001_ses-2_phase1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

and 453 more files

OpenNeuro Accession Number: ds000201
Files: 41384, Size: 4.5GB, Subjects: 86, Sessions: 2
Available Tasks: PVT, Hands, Resting State, Sleepiness, Working Memory Test
Available Modalities: T1w, T2w, bold, dwi, fieldmap

README

The Stockholm SleepyBrain Project

Background and Aim ##

Sleepiness is a brain state with pervasive effects on cognitive and affective functioning. However, little is known about the functional mechanisms and correlates of sleepiness in the awake brain. This project aimed to investigate overall effects of sleepiness on brain function with particular regard to emotional processing.

Method and Design ##

We investigated the effects of sleep deprivation using a randomized cross-over design. Resting state functional connectivity was investigated using functional magnetic resonance imaging (fMRI). Emotional contagion was studied using concurrent fMRI and electromyography (EMG) of facial muscles in response to emotional expressions and empathy for pain was investigated using pictures of others receiving pain stimuli. To study emotional reappraisal, participants were instructed to actively up-regulate or down-regulate their emotional responses to picture stimuli. The participants were characterized using several rating scales, biometric information, and blood sampling.

Specific Notes

participants.tsv File

Subject ID list and subject-level variables. Please refer to the participants.json for guidance on how to interpret specific columns in the participants.tsv file.

BIDS dataset

Data were converted from DICOM source files using dcm2niix. The parameters were further extracted from the DICOM files using pydicom and converted to .json format. SeriesDates were anonymized and shifted to pre-1900's years and a subject-based offset added to the month/year that preserves time difference between initial and follow-up visit.

T1- and T2-anatomical scans (anat/*_T{1,2}w.nii.gz) were defaced using the pydeface.py software: https://github.com/poldracklab/pydeface (c1ceeb2)

derivatives Folder

This folder contains the processed output from the MRIQC protocol. MRIQC is an automated processing pipeline designed to compute many image quality metrics for T1 weighted anatomical and T2* weighted functional scans. For more information please see:

https://github.com/poldracklab/mriqc (a5f68f5)

Additional derivatives include:

  • Plots of the fMRI event logs
  • thumbnail mosaics of the high-resolution T1w and T2w scans used to confirm defacing process.

sourcedata Folder

This folder contains the as-provided source files used to create the BIDS dataset files. The only changes made to these source files were to remove any information that could potentially be used to identify the study participants. Specifically:

  • EyeTrackingLogFiles: Files renamed, "TimeValues" and "TimeStamp" entries changed to "REMOVED" within each file.
  • PresentationLogFiles: Files renamed, scrubbed of Dates, Subject IDs. These files were used to create the sub-9XXX_ses-{1,2}_task-_events.tsv files.
  • PulseGatingFiles: Files renamed to remove original IDs.
  • WorkingMemoryTestResults: Files renamed to remove original IDs., subject IDs altered to 9XXX series randomized IDs. Dates removed. Times-of-day left intact.

Other data that could not be included in raw form due to its binary nature:

  • Physiological recordings (EMG): Converted from raw Acknowledge format to compressed .tsv files using the "convert_physio_files.py" script located in the code/ directory within the dataset. The output data are located within the dataset as *_physio.tsv.gz and *_physio.json pairs.

Diffusion Imaging - use these data with caution

Diffusion imaging from the following subjects should be used with caution due to suspicious bval/bvecs tables extracted from the source DICOM files:

sub-9019  sub-9070  sub-9057  sub-9091  sub-9090  sub-9013  sub-9044
sub-9050  sub-9067  sub-9035  sub-9035  sub-9073  sub-9083  sub-9037
sub-9007  sub-9053  sub-9066  sub-9012  sub-9082  sub-9077  sub-9076
sub-9099  sub-9001

Raw Polysomnography Data

Raw polysomnography data is available upon request. Please contact Gustav Nilsonne at gustav.nilsonne@ki.se to request this data.

Known Issues

-sub-9066/ses-1/func/sub-9066_ses-1_task-hands_events.tsv does not have all of the columns present in the other events files. It only has 'onset', 'duration' and 'condition'.

Authors

  • Gustav Nilsonne
  • Sandra Tamm
  • Paolo d’Onofrio
  • Hanna Å Thuné
  • Johanna Schwarz
  • Catharina Lavebratt
  • Jia Jia Liu
  • Kristoffer NT Månsson
  • Tina Sundelin
  • John Axelsson
  • Peter Fransson
  • Göran Kecklund
  • Håkan Fischer
  • Mats Lekander
  • Torbjörn Åkerstedt

Dataset DOI

10.18112/openneuro.ds000201.v1.0.3

License

CC0

Acknowledgements

We are grateful to Diana Cortes and Roberta Nagai for assistance with polysomnography recordings, to Birgitta Mannerstedt Fogelfors for assistance with screening, instructions to participants, and blood sampling, to Rouslan Sitnikov and Jonathan Berrebi for assistance with MRI sequences and auxiliary equipment, to Hannes Ingre for entering sleep diary data into a spreadsheet, and to William Triplett, data curator at openfmri.org, for assistance with data archiving.

How to Acknowledge

We encourage researchers to use the published dataset freely and ask that they cite the respective data sources as well as this paper.

Funding

Ethics Approvals

How To Cite

Copy
Gustav Nilsonne and Sandra Tamm and Paolo d’Onofrio and Hanna Å Thuné and Johanna Schwarz and Catharina Lavebratt and Jia Jia Liu and Kristoffer NT Månsson and Tina Sundelin and John Axelsson and Peter Fransson and Göran Kecklund and Håkan Fischer and Mats Lekander and Torbjörn Åkerstedt (2018). The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds000201.v1.0.3
More citation info

The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old

uploaded by Franklin Feingold on 2018-03-14 - over 3 years ago
last modified on 2018-07-17 - about 3 years ago
authored by Gustav Nilsonne, Sandra Tamm, Paolo d’Onofrio, Hanna Å Thuné, Johanna Schwarz, Catharina Lavebratt, Jia Jia Liu, Kristoffer NT Månsson, Tina Sundelin, John Axelsson, Peter Fransson, Göran Kecklund, Håkan Fischer, Mats Lekander, Torbjörn Åkerstedt
3779955

OpenNeuro Accession Number: ds000201
Files: 41384, Size: 4.5GB, Subjects: 86, Sessions: 2
Available Tasks: PVT, Hands, Resting State, Sleepiness, Working Memory Test
Available Modalities: T1w, T2w, bold, dwi, fieldmap

README

The Stockholm SleepyBrain Project

Background and Aim ##

Sleepiness is a brain state with pervasive effects on cognitive and affective functioning. However, little is known about the functional mechanisms and correlates of sleepiness in the awake brain. This project aimed to investigate overall effects of sleepiness on brain function with particular regard to emotional processing.

Method and Design ##

We investigated the effects of sleep deprivation using a randomized cross-over design. Resting state functional connectivity was investigated using functional magnetic resonance imaging (fMRI). Emotional contagion was studied using concurrent fMRI and electromyography (EMG) of facial muscles in response to emotional expressions and empathy for pain was investigated using pictures of others receiving pain stimuli. To study emotional reappraisal, participants were instructed to actively up-regulate or down-regulate their emotional responses to picture stimuli. The participants were characterized using several rating scales, biometric information, and blood sampling.

Specific Notes

participants.tsv File

Subject ID list and subject-level variables. Please refer to the participants.json for guidance on how to interpret specific columns in the participants.tsv file.

BIDS dataset

Data were converted from DICOM source files using dcm2niix. The parameters were further extracted from the DICOM files using pydicom and converted to .json format. SeriesDates were anonymized and shifted to pre-1900's years and a subject-based offset added to the month/year that preserves time difference between initial and follow-up visit.

T1- and T2-anatomical scans (anat/*_T{1,2}w.nii.gz) were defaced using the pydeface.py software: https://github.com/poldracklab/pydeface (c1ceeb2)

derivatives Folder

This folder contains the processed output from the MRIQC protocol. MRIQC is an automated processing pipeline designed to compute many image quality metrics for T1 weighted anatomical and T2* weighted functional scans. For more information please see:

https://github.com/poldracklab/mriqc (a5f68f5)

Additional derivatives include:

  • Plots of the fMRI event logs
  • thumbnail mosaics of the high-resolution T1w and T2w scans used to confirm defacing process.

sourcedata Folder

This folder contains the as-provided source files used to create the BIDS dataset files. The only changes made to these source files were to remove any information that could potentially be used to identify the study participants. Specifically:

  • EyeTrackingLogFiles: Files renamed, "TimeValues" and "TimeStamp" entries changed to "REMOVED" within each file.
  • PresentationLogFiles: Files renamed, scrubbed of Dates, Subject IDs. These files were used to create the sub-9XXX_ses-{1,2}_task-_events.tsv files.
  • PulseGatingFiles: Files renamed to remove original IDs.
  • WorkingMemoryTestResults: Files renamed to remove original IDs., subject IDs altered to 9XXX series randomized IDs. Dates removed. Times-of-day left intact.

Other data that could not be included in raw form due to its binary nature:

  • Physiological recordings (EMG): Converted from raw Acknowledge format to compressed .tsv files using the "convert_physio_files.py" script located in the code/ directory within the dataset. The output data are located within the dataset as *_physio.tsv.gz and *_physio.json pairs.

Diffusion Imaging - use these data with caution

Diffusion imaging from the following subjects should be used with caution due to suspicious bval/bvecs tables extracted from the source DICOM files:

sub-9019  sub-9070  sub-9057  sub-9091  sub-9090  sub-9013  sub-9044
sub-9050  sub-9067  sub-9035  sub-9035  sub-9073  sub-9083  sub-9037
sub-9007  sub-9053  sub-9066  sub-9012  sub-9082  sub-9077  sub-9076
sub-9099  sub-9001

Raw Polysomnography Data

Raw polysomnography data is available upon request. Please contact Gustav Nilsonne at gustav.nilsonne@ki.se to request this data.

Known Issues

-sub-9066/ses-1/func/sub-9066_ses-1_task-hands_events.tsv does not have all of the columns present in the other events files. It only has 'onset', 'duration' and 'condition'.

Authors

  • Gustav Nilsonne
  • Sandra Tamm
  • Paolo d’Onofrio
  • Hanna Å Thuné
  • Johanna Schwarz
  • Catharina Lavebratt
  • Jia Jia Liu
  • Kristoffer NT Månsson
  • Tina Sundelin
  • John Axelsson
  • Peter Fransson
  • Göran Kecklund
  • Håkan Fischer
  • Mats Lekander
  • Torbjörn Åkerstedt

Dataset DOI

10.18112/openneuro.ds000201.v1.0.3

License

CC0

Acknowledgements

We are grateful to Diana Cortes and Roberta Nagai for assistance with polysomnography recordings, to Birgitta Mannerstedt Fogelfors for assistance with screening, instructions to participants, and blood sampling, to Rouslan Sitnikov and Jonathan Berrebi for assistance with MRI sequences and auxiliary equipment, to Hannes Ingre for entering sleep diary data into a spreadsheet, and to William Triplett, data curator at openfmri.org, for assistance with data archiving.

How to Acknowledge

We encourage researchers to use the published dataset freely and ask that they cite the respective data sources as well as this paper.

Funding

Ethics Approvals

How To Cite

Copy
Gustav Nilsonne and Sandra Tamm and Paolo d’Onofrio and Hanna Å Thuné and Johanna Schwarz and Catharina Lavebratt and Jia Jia Liu and Kristoffer NT Månsson and Tina Sundelin and John Axelsson and Peter Fransson and Göran Kecklund and Håkan Fischer and Mats Lekander and Torbjörn Åkerstedt (2018). The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds000201.v1.0.3
More citation info

Dataset File Tree

Git Hash: 498701b 

BIDS Validation

We found 2 Warnings in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-9001/ses-1/beh/sub-9001_ses-1_task-workingmemorytest_events.tsv

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.bval

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.bvec

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.json

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.nii.gz

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude1.json

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude1.nii.gz

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude2.json

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude2.nii.gz

This file is missing for subject sub-9001, but is present for at least one other subject.

/sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_phase1.json

This file is missing for subject sub-9001, but is present for at least one other subject.

and 2176 more files

/sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude2.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/fmap/sub-9001_ses-1_phase1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/fmap/sub-9001_ses-1_phase2.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-1/func/sub-9001_ses-1_task-hands_bold.nii.gz

The most common set of dimensions is: 128,128,46,183 (voxels), This file has the dimensions: 128,128,45,202 (voxels). The most common resolution is: 1.72mm x 1.72mm x 2.40mm x 3.00s, This file has the resolution: 1.88mm x 1.88mm x 2.40mm x 3.00s.

/sub-9001/ses-1/func/sub-9001_ses-1_task-rest_bold.nii.gz

The most common set of dimensions is: 128,128,49,192 (voxels), This file has the dimensions: 128,128,49,193 (voxels).

/sub-9001/ses-2/dwi/sub-9001_ses-2_dwi.nii.gz

The most common set of dimensions is: 96,96,63,50 (voxels), This file has the dimensions: 256,256,62,50 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.30mm x 7.00s, This file has the resolution: 0.86mm x 0.86mm x 2.30mm x 7.00s.

/sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude2.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

/sub-9001/ses-2/fmap/sub-9001_ses-2_phase1.nii.gz

The most common set of dimensions is: 96,96,46 (voxels), This file has the dimensions: 64,64,45 (voxels). The most common resolution is: 2.29mm x 2.29mm x 2.40mm, This file has the resolution: 3.75mm x 3.75mm x 2.40mm.

and 453 more files

Dataset File Tree

Git Hash: 498701b 

Comments

Please sign in to contribute to the discussion.
By kronokairos@gmail.com - almost 3 years ago
Hello, I'm having a problem accessing the files in the zip archive. I've got an error message when unzipping. I downloaded it twice, and tried to extract it on linux and windows and got a 'Corrupted file error', both when trying to extract all the files, or only one subject files
By krzysztof.gorgolewski@gmail.com - almost 3 years ago
Perhaps the transfer was interrupted which can happen sometimes. You should try downloading the dataset using Amazon Web Services S3 protocol. The dataset is available at s3://openneuro.org/ds000201. You can use the AWS CLI to perform the download (you will have to use `--no-sign-request` flag to avoid having to provide any credentials): https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-welcome.html
By bklugah@gmail.com - about 2 years ago
Hello, this is very informative, however, how can I get the sleep stages(wake-through-NREM-REM) information from this experiment?
Thanks
By kirali233767@gmail.com - over 2 years ago
Hello, I have a quick question about the dataset. Have you done all the preprocessing steps for the fMRI data like realignment and time slicing? If not, any suggestion about how to do it? I am really in trouble with these steps. Thanks.
By dssiddharth2099@gmail.com - almost 2 years ago
hello, can you please me by clarifying what is the slice order acquisition for this time series(ascending, descending, interleaved(bottom-up/top-down/middle-top/siemens)?
By dssiddharth2099@gmail.com - almost 2 years ago

By chucongying@gmail.com - about 1 year ago
Thanks a lot for sharing the data.
Age would be an important confounders anyway. So, I am wondering whether the exact ages of the subjuects have bee available now? Where should I find it? Actually, I tried to follow my downloaded dataset not to find the exact ages instead of age groups.
By gustav.nilsonne@gmail.com - 9 months ago
Hello and thanks for your question. Sorry for the long delay in responding, I did not see the comment until now. The exact ages are not present in the published dataset in order to reduce the risk of reidentification. Please e-mail me directly: https://medarbetare.ki.se/people/gusnil
By soichih@gmail.com - 9 months ago
Hello. The T2W images from session 1 looks like FLAIR - not t2w. I am having a problem analyzing the data through fmriprep.
201106-18:57:28,292 nipype.workflow INFO: [Node] Running "11_add_gm" ("nipype.interfaces.ants.utils.ImageMath"), a CommandLine Interface with command: ImageMath 3 08_mult_gm_maths.nii.gz addtozero /export/prod/5fa31ed129699c4b62edefa9/5fa56cfc29699ca973ee4591/fmripworkdir/fmriprep_wf/single_subject_9002_wf/anat_preproc_wf/brain_extraction_wf/atropos_wf/08_mult_gm/08_mult_gm.nii.gz /export/prod/5fa31ed129699c4b62edefa9/5fa56cfc29699ca973ee4591/fmripworkdir/fmriprep_wf/single_subject_9002_wf/anat_preproc_wf/brain_extraction_wf/atropos_wf/10_me_csf/sub-9002_ses-1_T1w_ras_valid_corrected_labeled_maths_class-01_maths.nii.gz 201106-18:57:33,24 nipype.workflow WARNING: [Node] Error on "fmriprep_wf.single_subject_9002_wf.anat_preproc_wf.brain_extraction_wf.atropos_wf.11_add_gm" (/export/prod/5fa31ed129699c4b62edefa9/5fa56cfc29699ca973ee4591/fmripworkdir/fmriprep_wf/single_subject_9002_wf/anat_preproc_wf/brain_extraction_wf/atropos_wf/11_add_gm) 201106-18:57:33,586 nipype.workflow ERROR: Node 11_add_gm failed to run on host slurm7-compute8.
I am wondering if above error is caused by the data issue.. Could anyone comment on if the T2w on this dataset is indeed t2w (not t2w-flair?)

By gustav.nilsonne@gmail.com - 9 months ago
Hello, thanks for your question. It is correct that these images are T2 FLAIR. Please feel free to contact me by e-mail for more details: https://medarbetare.ki.se/people/gusnil
By soichih@gmail.com - 9 months ago
There is something wrong with this comment system. When I click "submit", the comment gets added but it doesn't get cleared and there is no visual indication that the comment was added (at the bottom of the page). So I ended up clicking "submit comment" button several times.
By shreymathur49@gmail.com - 2 months ago
What is the repetition time of this dataset in each sessions?