Weren’t z-stats also provided? Z-stats are good for statistical thresholding, but not much else. Cohen’s D and other effect size measures are better for making biological interpretations (e.g. is X measure different between area A and area B). If you want to find the boundaries between areas involved in faces and those that are not, I would compute the gradient of the effect size map and draw the boundary along the gradient ridge. This is the same approach taken, for example, in the Zilles and Amunts cytoarchitectural parcellations. Thresholding is a biological map is a tough thing to do well—there will be imperfections the exact value of the biological measure, even very high quality average maps will have this, and this will cause the boundary to deviate randomly.
Matt.
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You can pick which gradient to focus on. Most of the strong ones are around obvious stuff, not weak positive and negative. If anything, z-stat thresholds (white lines) are more focused around weak stuff and are less reproducible as illustrated in the attached images from 2 independent 210 subject groups.
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Those are Bonferroni corrected stats. I don’t think you can be more conservative than that on any rational basis.
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I’d argue that you don’t need the thresholding step as the clusters are pretty obvious by eye (we didn’t need this to make the HCP’s multi-modal parcellation). You can use wb_command -cifti-gradient to create a gradient map (if using group average surfaces, you will need to use average midthickness vertex areas as “corrected areas”). If you want semi-automated help drawing gradient borders, there is a tool in Connectome Workbench GUI to do this (i.e. the tool used to make the HCP’s multi-modal parcellation), which means that the final borders are machine drawn, just informed by neuroanatomist choices of signal, artifacts, and noise. We could probably automate this a bit more to do some kind of flood fill bounded by gradients for peaks and valleys of a biological map for this specific use case.
It's worth keeping in mind that algorithms also all have biases and often make silly mistakes (like the one that called a meningioma a brain hemorrhage on a head CT I read recently). I still think the best work will be generated by a combination of scientist (or physician) and automated tools.
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Basically it takes an existing border and gradient map and ensures that the border optimally follows the gradient (and can take multiple gradient maps for multi-modal analysis).
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Border Optimization in the border drawing section.
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This was designed originally to optimize borders between two areas (meaning that an individual area was done in multiple parts). It optimizes for shortest path and following the gradient together within a region around a border. So you want to break up curves into smaller sections.
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I think Mike Harms made them. It is correct that they are not currently a pipeline output.
Matt.
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I did some digging, and it looks like the version that I used to create the “cohensd” maps for the HCP_S1200 group release never made it into GitHub.
That code was just:
# Cohens d
if [ "${ComputeCohensD}" = "YES" ] ; then
# 4th dim ("Time") is actually across subjects in the $merged files
fslmaths $mergedcope -Tmean ${mergedcope}_mean
fslmaths $mergedcope -Tstd -div ${mergedcope}_mean -recip cohensd${i}
#Compute a 2nd version as the mean across the Cohen's d's for each indiv subject
fslmaths $mergedvarcope -sqrt -div $mergedcope -recip -Tmean avgcohensd${i}
rm -f ${mergedcope}_mean.nii.gz
fi
The first version above (“cohensd”) is what we ended up releasing (not the “avgcohensd” version).
Cheers,
-MH
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Michael Harms, Ph.D.
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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
From: Reza Rajimehr <raji...@gmail.com>
Reply-To: "hcp-...@humanconnectome.org" <hcp-...@humanconnectome.org>
Date: Wednesday, January 17, 2024 at 12:52 AM
To: "hcp-...@humanconnectome.org" <hcp-...@humanconnectome.org>
Subject: Re: [hcp-users] Re: Thresholding Cohen’s d maps
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