Comparing HCP to CONN pipeline outputs
katarina jerotic
Hello HCP community,
I have run preprocessing (volume based) using CONN and am trying to compare the outcomes to those of the HCP minimal preprocessing pipeline. I have a couple of questions I would greatly appreciate your help with.
- Would you expect the subcortical regions’ timeseries in the volume based and the surface based data for an HCP subject to be identical to one another following preprocessing? If not could you please give a couple of examples of why this may not be the case?
- Is there a file / document that clearly outline the naming of files for the HCP - specifically describing the data within the file and where in the preprocessing steps this occurs?
- What is the name of the final preprocessed surface-based file for a subject?
Thank you very much in advance for any help with this!
Elam, Jennifer
Jennifer Elam, Ph.D.
Scientific Outreach, Human Connectome Project
Washington University School of Medicine
Department of Neuroscience, Box 8108
660 South Euclid Avenue
St. Louis, MO 63110
314-362-9387
el...@wustl.edu
www.humanconnectome.org
Sent: Wednesday, July 10, 2024 9:53 AM
To: HCP-Users <hcp-...@humanconnectome.org>
Cc: elvira...@upf.edu <elvira...@upf.edu>
Subject: [hcp-users] Comparing HCP to CONN pipeline outputs
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Glasser, Matthew
We don’t use the outputs of the minimal preprocessing pipelines to compute connectivity. After minimal preprocessing, we do extensive denoising with spatial and temporal ICA (not yet generally released for HCP data, but available in the HCP Pipelines). We also use multi-modal alignment to get brain areas aligned across subjects. We also don’t spatially smooth HCP data to any significant degree. For all these reasons, I would not expect CONN results to look very much like results processed with the HCP Pipelines.
Fortunately, there is already a PNAS paper comparing the effects of volume-based versus surface based (and CIFTI-based) analyses so you don’t need to repeat the analysis: https://www.pnas.org/doi/abs/10.1073/pnas.1801582115
The bottom line is that the HCP Pipelines when used as intended offer 3-fold better spatial localization of neuroimaging results than typical volume-based methods. These effects are agnostic to data type and other analysis choices—they are simple consequences of the brain’s geometry and how that interacts with spatial smoothing and cross-subject alignment of brain areas.
Matt.
From:
katarina jerotic <katarina...@gmail.com>
Reply-To: "hcp-...@humanconnectome.org" <hcp-...@humanconnectome.org>
Date: Wednesday, July 10, 2024 at 9:53 AM
To: HCP-Users <hcp-...@humanconnectome.org>
Cc: "elvira...@upf.edu" <elvira...@upf.edu>
Subject: [hcp-users] Comparing HCP to CONN pipeline outputs
Hello HCP community,
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