Is Additional Quality Check Required After Running the HCP Pipeline?

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李佳艺

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Dec 18, 2025, 7:18:28 AM (3 days ago) Dec 18
to HCP-Users
Dear all,

I hope this email finds you well. Recently, I have been using the HCP pipeline for preprocessing structural and functional data. I ran the following batches: PreFreeSurferPipelineBatch.sh/FreeSurferPipelineBatch.sh/PostFreeSurferPipelineBatch.sh/GenericfMRIVolumeProcessingPipelineBatch.sh/GenericfMRISurfaceProcessingPipelineBatch.sh/IcaFixProcessingBatch.sh/PostFixBatch.sh/MSMAllPipelineBatch.sh/DeDriftAndResamplePipelineBatch.sh.

Based on the reference materials, I understand that: Structural data processing generates a structuralQC.wb_scene file; functional data processing generates a fMRIQC.wb_scene file; the ICA processing generates a ICA_Classification_singlescreen.scene file

I would like to know whether running the HCP pipeline as a standard processing procedure is sufficient, or if further quality checks are required. If a quality check is needed, are these three wb_scene files adequate to evaluate the quality of the HCP pipeline processing?

Additionally, I have attached two images showing the structuralQC.wb_scene and fMRIQC.wb_scene results for one of my subjects. Could you please advise on how to assess the quality of processing based on these results?

Thank you very much for your time and assistance. 

Best regards,
Jiayi Li

structuralQC.jpgfMRIQC.jpg

Harms, Michael

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Dec 18, 2025, 10:14:10 AM (3 days ago) Dec 18
to hcp-...@humanconnectome.org

Yes, in general you should do some sort of QC/review of your data, including the processed outputs from the HCPpipelines.

 

The structuralQC scene provides a quick way to confirm that you got reasonable surfaces.  The myelin map is particularly useful in its ability to highlight regions with a surface placement error.

 

The fMRIQC scene is useful to confirm that the registration and distortion correction is visually reasonable.  It is also useful to gather the mincost values from the BBR registration, and give a closer look at data with mincost "outliers".

 

The ICA scene can be used to confirm that the ICA classification is working reasonably in your data, and is a good check to match, at least in a handful of subjects.  As a quantitative check across a whole study, I also find it informative to examine scatterplot of the number of noise components, and number of signal components, against the total number of components.  This will frequently identify "outliers" that are worth a closer look as well.

 

Cheers,

-MH

 

-- 

Michael Harms, Ph.D.

-----------------------------------------------------------

Professor of Psychiatry

Washington University School of Medicine

Department of Psychiatry, Box 8134

660 South Euclid Ave.                        Tel: 314-747-6173

St. Louis, MO  63110                          Email: mha...@wustl.edu

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Harms, Michael

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Dec 18, 2025, 10:16:55 AM (3 days ago) Dec 18
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Forgot to mention, the fMRIQC scene also makes it easy to assess whether the participant moved out of the imaging field-of-view over the course of the run, and if so, where.

 

-- 

Michael Harms, Ph.D.

-----------------------------------------------------------

Professor of Psychiatry

Washington University School of Medicine

Department of Psychiatry, Box 8134

660 South Euclid Ave.                        Tel: 314-747-6173

St. Louis, MO  63110                          Email: mha...@wustl.edu

 

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