CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Preprocessed data described in
Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research 2017, 6:1262 https://doi.org/10.12688/f1000research.11964.2
are available at https://legacy.openfmri.org/dataset/ds000030/ and via Amazon Web Services S3 protocol at: s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/derivatives/
The participants.tsv file contains subject IDs with demographic informations as well as an inventory of the scans that are included for each subject.
The /derivaties folder contains summary information that reflects the data and its contents:
All scan files were converted from scanner DICOM files using dcm2niix (0c9e5c8 from https://github.com/neurolabusc/dcm2niix.git). Extra DICOM metadata elements were extracted using GDCM (http://gdcm.sourceforge.net/wiki/index.php/Main_Page) and combined to form each scan's .json sidecar.
Note regarding scan and task timing: In most cases, the trigger time was provided in the task data file and has been transferred into the TaskParameter section of each scans *_bold.json file. If the trigger time is available, a correction was performed to the onset times to account for trigger delay. The uncompensated onset times are included in the onset_NoTriggerAdjust column. There will be an 8 second discrepancy between the compensated and uncompensated that accounts for pre-scans (4 TRs) performed by the scanner. In the cases where the trigger time is not available, the output of (TotalScanTime - nVols*RepetitionTime) may provide an estimate of pre-scan time.
Defacing was performed using freesurfer mri_deface (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface)
Bischoff-Grethe, Amanda et al. "A Technique for the Deidentification of Structural Brain MR Images." Human brain mapping 28.9 (2007): 892–903. PMC. Web. 27 Jan. 2016.
The larger amount of missing PAM scans is due to a task design change early in the study. It was decided that data collected before the design change would be excluded.
The Stop Signal task consisted of both a training task (no MRI) and the in-scanner fMRI task. The data from the training run is included in each subject's beh folder with the task name "stopsignaltraining".
Some of the T1-weighted images included within this dataset (around 20%) show an aliasing artifact potentially generated by a headset. The artifact renders as a ghost that may overlap the cortex through one or both temporal lobes. A list of participants showing the artifact has been added to the dataset.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
ReadMe:
This dataset contains raw data for the Maternal Brain Project, a project that uses precision imaging methods to map the human maternal brain starting pre-conception through postpartum and beyond. Subjects undergo repeated multi-modal MRI, venipuncture, and mood/health assessments over the course of pregnancy.
Data will be released iteratively. The first installment of data (V1) involves the Maternal Brain Project-Pilot, wherein a single nulliparous woman completed 26 MRI scans (T1w, high-resolution MTL, DWI) alongside state-dependent measures and serum assessments of sex hormones from pre-conception through two years postpartum. Future data releases will involve extended phenotyping in a larger cohort of participants and their partners both in the United States (V2) and Internationally (V3). More details can be found here: https://wbhi.ucsb.edu/
V1 notes: - Final two sessions (ses-26 & ses-27) took place within a 24-hr period to measure test-retest reliability of MRI measures between the two scanning sites, UCI and UCSB. - Due to technical difficulties, fmaps for ses-07 have different scan parameters (see json file) - For more details regarding scan parameters, see json files located in each session's modality folder
For questions, please reach out to lpritschet38@gmail.com & ucsbjacobslab@gmail.com
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CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Preprocessed data described in
Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research 2017, 6:1262 https://doi.org/10.12688/f1000research.11964.2
are available at https://legacy.openfmri.org/dataset/ds000030/ and via Amazon Web Services S3 protocol at: s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/derivatives/
The participants.tsv file contains subject IDs with demographic informations as well as an inventory of the scans that are included for each subject.
The /derivaties folder contains summary information that reflects the data and its contents:
All scan files were converted from scanner DICOM files using dcm2niix (0c9e5c8 from https://github.com/neurolabusc/dcm2niix.git). Extra DICOM metadata elements were extracted using GDCM (http://gdcm.sourceforge.net/wiki/index.php/Main_Page) and combined to form each scan's .json sidecar.
Note regarding scan and task timing: In most cases, the trigger time was provided in the task data file and has been transferred into the TaskParameter section of each scans *_bold.json file. If the trigger time is available, a correction was performed to the onset times to account for trigger delay. The uncompensated onset times are included in the onset_NoTriggerAdjust column. There will be an 8 second discrepancy between the compensated and uncompensated that accounts for pre-scans (4 TRs) performed by the scanner. In the cases where the trigger time is not available, the output of (TotalScanTime - nVols*RepetitionTime) may provide an estimate of pre-scan time.
Defacing was performed using freesurfer mri_deface (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface)
Bischoff-Grethe, Amanda et al. "A Technique for the Deidentification of Structural Brain MR Images." Human brain mapping 28.9 (2007): 892–903. PMC. Web. 27 Jan. 2016.
The larger amount of missing PAM scans is due to a task design change early in the study. It was decided that data collected before the design change would be excluded.
The Stop Signal task consisted of both a training task (no MRI) and the in-scanner fMRI task. The data from the training run is included in each subject's beh folder with the task name "stopsignaltraining".
Some of the T1-weighted images included within this dataset (around 20%) show an aliasing artifact potentially generated by a headset. The artifact renders as a ghost that may overlap the cortex through one or both temporal lobes. A list of participants showing the artifact has been added to the dataset.