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Monash rsPET-MR

OpenNeuro Accession Number: ds002898Files: 572Size: 47.51GB

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Monash rsPET-MR
Monash rsPET-MR
  •   .bidsignore
  •   .gitignore
  •   CHANGES
  •   dataset_description.json
  •   participants.json
  •   participants.tsv
  •   README
  • derivatives
  • sub-01
  • sub-02
  • sub-03
  • sub-04
  • sub-05
  • sub-06
  • sub-07
  • sub-08
  • sub-09
  • sub-10
  • sub-11
  • sub-12
  • sub-13
  • sub-14
  • sub-15
  • sub-16
  • sub-17
  • sub-18
  • sub-19
  • sub-20
  • sub-21
  • sub-22
  • sub-23
  • sub-24
  • sub-25
  • sub-26
  • sub-27

README

Monash REST PET

This dataset contains concurrent rsfMRI and FDG PET acquisitions on Monash Biomedical Imaging (MBI)'s Siemens Biograph mMR 3T MR-PET scanner from 27 healthy controls.

Relevant papers

  • Jamadar, S.D., Ward, P.G.D., Carey, A., McIntyre, R., Parkes, L., Sasan, D., Fallon, J., Orchard, E., Li, S., Chen, Z., Egan, G.F., 2019a. Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET. JoVE 60259. https://doi.org/10.3791/60259
  • Jamadar, S.D., Ward, P.G.D., Li, S., Sforazzini, F., Baran, J., Chen, Z., Egan, G.F., 2019b. Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex. NeuroImage 189, 258–266. https://doi.org/10.1016/j.neuroimage.2019.01.003",
  • Jamadar, S.D., Ward, P.G.D., Liang, E.X., Orchard, W.R., Chen, Z., Egan, G., 2020. Metabolic and haemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study (preprint). Neuroscience. https://doi.org/10.1101/2020.05.01.071662"

Comments

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By eswar.damaraju10@gmail.com - about 2 years ago
How do I download the data? Using datalad sync option or direct download, I only get json files under BIDS structured subject folders. No nifti's or dicom files can be seen?

Thanks
By thomas.close@sydney.edu.au - about 2 years ago
Sorry, apparently it is because I put *.nii.gz in the gitignore. I will try to work out how to edit it
By rustyruss1985@gmail.com - about 2 years ago
the dataset only contains json files...
By thomas.close@sydney.edu.au - about 2 years ago
They are actually uploaded but are not showing up. I just heard back from OpenNeuro support saying that it is because I had put *.nii.gz in the .gitignore (as I was using git locally during JSON file editing). I will try to fix it up now
By tommorin95@gmail.com - 9 months ago
Amazing dataset! I have two quick questions.
1. Is the PET data (sub-**/pet/sub-**_task-rest_pet.nii.gz) already motion corrected?
2. For sub-03, the pet data is only 337 volumes, (for all other subjects it is 356 volumes), but they have 6 complete fMRI runs. Was the PET acquisition ended early?
By sharna.jamadar@monash.edu - 7 months ago
1. No, the PET data has not been motion corrected.
2. Please see below - we have identified that subject 03 had an incomplete upload for the PET data. We are awaiting a bug fix from the OpenNeuro team to rectify this.
By sharna.jamadar@monash.edu - 7 months ago
A problem with the PET data for subject 03 has been identified (incomplete upload). Currently there is a bug in the OpenNeuro web interface that means we cannot upload the full dataset - we have been told this will be rectified in 1 week. Apologies for the inconvenience
By sharna.jamadar@monash.edu - 7 months ago
Code for the spatiotemporal filter reported in Jamadar et al. (2020) Scientific Data is now available on the github repository for this dataset.

https://github.com/MonashBI/Monash_rsPET-MR_prep
code: PrepData_rsfPET_code.m
By eric.ceballos-dominguez@tum.de - 4 months ago
How long is the pause between fMRI runs? Is it possible to assume that they are all in immediate succession to concatenate them into a single sequence?
By sharna.jamadar@monash.edu - 4 months ago
The fMRI runs were automated to run consecutively, however automatic scanner processes between sequences, like shimming, etc., means that there will be a short and variable delay between runs. It is best to check the acquisition time of the last image in a run, and compare to the first image in the next run, to know what this delay was. Then it can be decided if it is acceptable (to the researcher) to concatenate the runs or not.
By eric.ceballos-dominguez@tum.de - 4 months ago
Great, thanks for your answer!
By lijunle.1995@gmail.com - about 2 months ago
Thanks for sharing!
I have a question: I underwent the motion correction and PET-T1 coregistration with SPM12 to the resting-state PET data, but found that all the connectivity are around 0.99. I can't find that what's wrong about my preprocessing. Could you give some advices? Thanks a lot!
By lijunle.1995@gmail.com - about 2 months ago
Oh, I find that it may be due to the accumulating effect of the radiotracer, which is mentioned in your artical.
By lijunle.1995@gmail.com - about 2 months ago
Oh, I find that it may be due to the accumulating effect of the radiotracer, which is mentioned in your artical.
By sharna.jamadar@monash.edu - about 2 months ago
Yes that's right :)
By colm.mcginnity@kcl.ac.uk - 8 days ago
Hi, it doesn't look like this includes the umaps used for attenuation correction? Is it possible for you to share these please?
Thanks!