We didn’t do global signal regression or mean stabilization if that is what you 
are wondering about.  The formula is essentially (data – 4DMean) + 10000.

Peace,

Matt.

From: mali s <[email protected]<mailto:[email protected]>>
Date: Monday, November 21, 2016 at 9:49 PM
To: Timothy Coalson <[email protected]<mailto:[email protected]>>
Cc: Matt Glasser <[email protected]<mailto:[email protected]>>, 
"[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Subject: Re: [HCP-Users] 4D Intensity normalization in the fMRIVolume pipeline

We are doing connectivity analysis on this data, and it is important for us to 
understand the exact formula used to convert to the intensity normalized image. 
It would really help if you can share this or guide me to a link where I can 
find the details.

To put it precisely, let's say I have r(x,y,z,t) after all the preprocessing 
steps, and just before the intensity normalization. What is the formula used to 
convert this to the intensity normalized image n(x,y,z,t) ?

Regards,
Mali


On Tue, Nov 22, 2016 at 8:44 AM, Timothy Coalson 
<[email protected]<mailto:[email protected]>> wrote:
Yes, because the 3d volume means were already close to each other even before 
any normalization.

Tim


On Mon, Nov 21, 2016 at 8:56 PM, mali s 
<[email protected]<mailto:[email protected]>> wrote:

Thank you the replies!

Tim, I think I understand your explanation. In mathematical terms, it means 4d 
mean is close to each of the 3d volume means, right?

I'm guessing "PFA" means "please find attached".

BOLD scans have a lot of structural signal in them, which we haven't subtracted 
out - the activation-related signal is a modest local fluctuation compared to 
this base signal level.  Additionally, since you are averaging over the entire 
brain mask, the activation signals get considerably diluted by white matter, 
and by gray matter that isn't similarly active at that timepoint.

Tim

I'm guessing "PFA" means "please find attached".

BOLD scans have a lot of structural signal in them, which we haven't subtracted 
out - the activation-related signal is a modest local fluctuation compared to 
this base signal level.  Additionally, since you are averaging over the entire 
brain mask, the activation signals get considerably diluted by white matter, 
and by gray matter that isn't similarly active at that timepoint.

Tim

I'm guessing "PFA" means "please find attached".

BOLD scans have a lot of structural signal in them, which we haven't subtracted 
out - the activation-related signal is a modest local fluctuation compared to 
this base signal level.  Additionally, since you are averaging over the entire 
brain mask, the activation signals get considerably diluted by white matter, 
and by gray matter that isn't similarly active at that timepoint.

Tim


On Mon, Nov 21, 2016 at 8:14 PM, Glasser, Matthew 
<[email protected]<mailto:[email protected]>> wrote:
I don’t know what PFA is and am otherwise having some trouble following your 
question.  There is grand mean intensity normalization to 10000, meaning that 
the entire 4D image inside the brain mask has a mean of 10000.

Peace,

Matt.

From: 
<[email protected]<mailto:[email protected]>>
 on behalf of mali s <[email protected]<mailto:[email protected]>>
Date: Monday, November 21, 2016 at 8:08 PM
To: "[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Subject: [HCP-Users] 4D Intensity normalization in the fMRIVolume pipeline

Dear HCP team,

We are using minimally preprocessed volume files from HCP database 
(100206/MNINonLinear/Results/tfMRI_MOTOR_LR/tfMRI_MOTOR_LR.nii). The release 
notes for this subject say these files were processed using v3+ of the 
pipeline. I believe 4d intensity normalization to mean of 10^4 is being done 
here.

However, PFA the mean brain activation (after applying mask) during the course 
of the scan . They are all close to 10^4,  more like a global signal 
normalization for every 3d volume.

I am having trouble reconciling both. Thanks in advance for the help.

Regards,
Mali



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--
Mali Sundaresan

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