Finding brain regions corresponding to HCP labels

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Shirin Vafaee

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Sep 9, 2021, 7:41:37 AM9/9/21
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Hello :)

I am considering to use HCP labels to extract brain activity from each brain region. 

How should I find which labels correspond to LOC, FFA, PPA, LVC and HVC brain regions?

Thank you,
Shirin

Glasser, Matt

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Sep 9, 2021, 10:42:34 AM9/9/21
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Please have a look at the supplementary neuroanatomical results of this publication and specifically table 1 for a detailed list of the cortical areal abbreviations and their full names:

 

https://www.nature.com/articles/nature18933

 

Also if you want a map with all labels on it, that is available here:

 

https://balsa.wustl.edu/78X3

 

Matt.

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Reza Rajimehr

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Sep 9, 2021, 8:18:47 PM9/9/21
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Hi Shirin,

For functionally defined areas (e.g. FFA and PPA), you can also use category localizer data from HCP working memory task and define regions-of-interest based on group-average maps.

Reza


Shirin Vafaee

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Sep 10, 2021, 1:29:46 AM9/10/21
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Dear Matt,

I have understood that, thank you for your explanation.

Dear Reza,

Thank you very much for your response. But I am still a bit confused about the solution you provided. If possible, would you please provide me with any reference or tutorial explaining this method in more detail?

Thanks,
Shirin


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Reza Rajimehr

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Sep 10, 2021, 2:10:53 AM9/10/21
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Hi Shirin,

In the group-average package of HCP, for the working memory task, there are four contrasts that could be relevant for you: Face vs. Avg, Body vs. Avg, Tool vs. Avg, and Place vs. Avg. Each of these contrasts shows activation for one category versus the average activation for other categories, collapsed across memory loads. You can visualize these group-average activation maps in Workbench. To define regions of interest, you can take two approaches:

1) Define the borders of areas using gradient maps - this is what Matt would recommend.

2) Threshold the maps. Unfortunately thresholding can be arbitrary. In one study (
https://www.nature.com/articles/s41467-020-14610-8), we selected top 1% of vertices which had the highest activation values in a given map. This approach gave us a reasonable layout of category-selective areas. I can explain more about it off-list.

Best,
Reza


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