Thank you both again for your reply.

Right now, I am calculating ALFF and comparing it to functional connectivity.

Global intensity normalization is probably a good processing step for ALFF, but 
I had hoped to also look at ALFF without any sort of intensity normalization.

>From what you are saying though, it sounds like this might be more effort than 
>its worth…

Best,
Erik



> On Apr 11, 2018, at 6:45 PM, Harms, Michael <[email protected]> wrote:
> 
>  
> We consider those “intermediate” files and thus they aren’t part of our 
> released packages or “CinaBox” disks.  So, this could be rather challenging.  
> You would need to use “REST” calls into the ConnectomDB database directly to 
> pull down the files for each run.
>  
> Perhaps you could describe why you need this scaling factor, and we can see 
> if we have any further insight.
>  
> Cheers,
> -MH
>  
>  
> -- 
> Michael Harms, Ph.D.
> -----------------------------------------------------------
> Associate 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: [email protected] 
> <mailto:[email protected]>
>  
> From: "Glasser, Matthew" <[email protected]>
> Date: Wednesday, April 11, 2018 at 5:35 PM
> To: erik lee <[email protected]>, "Harms, Michael" <[email protected]>
> Cc: "[email protected]" <[email protected]>
> Subject: Re: [HCP-Users] Grand Mean Intensity Normalization
>  
> Jacobian wasn’t used for the 3T fMRI data.  Also the bias field correction 
> was non-optimal.  These are the files that you would want:
>  
> 1) ${StudyFolder}/${Subject}/${fMRIName}/BiasField.2.nii.gz
> 2) ${StudyFolder}/${Subject}/${fMRIName}/rfMRI_REST1_LR_nonlin.nii.gz
> 3) ${StudyFolder}/${Subject}/${fMRIName}/rfMRI_REST1_LR_nonlin_norm.nii.gz
>  
> I expect you will find the normalization factor will be correlated with head 
> size and be related to the amount of coil loading each subject has.  
>  
> Keep in mind the same normalization factor was applied to all voxels.  
>  
> Matt.
>  
> From: erik lee <[email protected] <mailto:[email protected]>>
> Date: Wednesday, April 11, 2018 at 5:05 PM
> To: "Harms, Michael" <[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] Grand Mean Intensity Normalization
>  
> Hi Matt and Michael, 
>  
> Thank you both for your helpful replies.
>  
> If I wanted to back-calculate the original mean of the rfMRI image after bias 
> field and Jacobian correction, do you know what the appropriate files would 
> be to do this?
>  
> Looking at the HCP data/intensity normalization script, I wasn’t exactly sure 
> what the file names would be for the following:
>  
> 1. Input rfMRI (pre-Jacobian/Bias Field correction)
> 2. Bias Image
> 3. Jacobian Image
> 4. Output rfMRI (post-Jacobian/Bias Field correction and global intensity 
> normalization, but before any temporal processing)
>  
> Presumably once I have these, I could do some simple algebra at any given 
> voxel to find the global mean used in the intensity normalization.
>  
> Thanks,
> Erik
>  
>  
>  
>  
>> On Apr 11, 2018, at 3:43 PM, Harms, Michael <[email protected] 
>> <mailto:[email protected]>> wrote:
>>  
>>  
>> Just to expand on this, since I think I might know why you are asking.
>>  
>> The grand mean is computed on the brain masked volume timeseries, after bias 
>> field correction and jacobian modulation is first applied – see the end of 
>> IntensityNormalization.sh, which is called as the final step in 
>> GenericfMRIVolumeProcessingPipeline.sh
>>  
>> There is NOT another grand mean normalization applied specifically to the 
>> CIFTI data, so don’t expect the CIFTI data to have a grand mean of 10000.  
>> IIRC, the grand mean of the CIFTI timeseries tends to end up in the 
>> 8000-9000 range.
>>  
>> Cheers,
>> -MH
>>  
>> -- 
>> Michael Harms, Ph.D.
>>  
>> -----------------------------------------------------------
>>  
>> Associate 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: [email protected] 
>> <mailto:[email protected]>
>>  
>> On 4/11/18, 2:31 PM, "[email protected] 
>> <mailto:[email protected]> on behalf of Glasser, 
>> Matthew" <[email protected] 
>> <mailto:[email protected]> on behalf of 
>> [email protected] <mailto:[email protected]>> wrote:
>>  
>> 1) The overall mean of each scan is 10000, this is not done voxelwise
>> (e.g. like a bias correction would be).
>>  
>> 2) Unfortunately this information is not saved.  I don¹t think fslmaths
>> outputs it, perhaps it could be back computed from some intermediate
>> files.   
>>  
>> Peace,
>>  
>> Matt.
>>  
>> On 4/11/18, 2:21 PM, "[email protected] 
>> <mailto:[email protected]> on behalf of
>> erik lee" <[email protected] 
>> <mailto:[email protected]> on behalf of
>> [email protected] <mailto:[email protected]>> wrote:
>>  
>> >Dear HCP Experts,
>> > 
>> >I am currently using the temporally preprocessed rfMRI data in the S900
>> >release (aka rfMRI_REST?_??_Atlas_hp2000_clean.dtseries.nii).
>> > 
>> >According to the Smith 2013 NeuroImage paper, it sounds like the images I
>> >am using have all received global intensity normalization prior to the
>> >temporal preprocessing.
>> > 
>> >I have two sets of questions relating to this:
>> > 
>> >(1) Does this mean that the global mean of all voxels (averaged across
>> >time points) is used to normalize each voxel? If this is the case, is
>> >this the mean of every voxel in the image, or exclusively those in the
>> >brain?
>> > 
>> >(2) Looking through ConnectomeDB, I couldn¹t find a file with the scaling
>> >factor used for normalizing. Is this something that is saved anywhere?
>> > 
>> >Thanks for the help!
>> > 
>> >Best,
>> >Erik Lee
>> >_______________________________________________
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>> >[email protected] <mailto:[email protected]>
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>> ><http://lists.humanconnectome.org/mailman/listinfo/hcp-users>
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>>  
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