Right.

Peace,

Matt.

From: 
<[email protected]<mailto:[email protected]>>
 on behalf of Sang-Yun Oh <[email protected]<mailto:[email protected]>>
Date: Tuesday, May 16, 2017 at 11:25 AM
To: Timothy Coalson <[email protected]<mailto:[email protected]>>
Cc: "[email protected]<mailto:[email protected]>" 
<[email protected]<mailto:[email protected]>>
Subject: Re: [HCP-Users] Infinite values in Group average data

This makes more sense! Sorry I missed your mention of fisher-z transform

So I would apply tanh to each element to revert back to regular correlation 
coefficients

Thank you for your help!

Best,
Sang

On Mon, May 15, 2017 at 5:57 PM Timothy Coalson 
<[email protected]<mailto:[email protected]>> wrote:
After the fisher-z transform, you can have values greater than 1, see the graph 
on the right:

https://en.wikipedia.org/wiki/Fisher_transformation

This is why the "correct" answer for the diagonal is infinity for the "zcorr" 
file.

Tim


On Mon, May 15, 2017 at 7:51 PM, Sang-Yun Oh 
<[email protected]<mailto:[email protected]>> wrote:
I am also finding that some off-diagonal elements in this matrix are also 
greater than 1 indicating this matrix is not a correlation matrix.

In [5]: img
Out[5]:
memmap([[  8.66434002e+00,   1.96847185e-01,   1.66294336e-01, ...,
          1.01449557e-01,   7.45474100e-02,   1.15624115e-01],
       [  1.96847185e-01,              inf,   3.36383432e-01, ...,
         -5.70017472e-03,  -5.49946353e-02,   3.72834280e-02],
       [  1.66294336e-01,   3.36383432e-01,              inf, ...,
         -4.45242636e-02,  -6.07097335e-02,  -1.51601573e-02],
       ...,
       [  1.01449557e-01,  -5.70017472e-03,  -4.45242636e-02, ...,
                     inf,   1.91883039e+00,   9.20160294e-01],
       [  7.45474100e-02,  -5.49946353e-02,  -6.07097335e-02, ...,
          1.91883111e+00,   8.31776619e+00,   8.82132888e-01],
       [  1.15624115e-01,   3.72833721e-02,  -1.51601573e-02, ...,
          9.20160294e-01,   8.82132888e-01,   8.66434002e+00]], dtype=float32)

Any insight would be appreciated

Thanks,
Sang

On Mon, May 15, 2017 at 1:13 PM Sang-Yun Oh 
<[email protected]<mailto:[email protected]>> wrote:
Thank you for the response.

I am, too, confused by some being non-zero finite values, and others being 
infinities.

Before computing a correlation matrix, if standardized by subtracting the mean 
and scaling by variance, all diagonal elements should be exactly 1.

What I am concerned about is how the whole matrix was computed, since a 
fundamental characteristic of correlation matrix is not satisfied

Best,
Sang

On Mon, May 15, 2017 at 11:33 AM Timothy Coalson 
<[email protected]<mailto:[email protected]>> wrote:
Per the name "zcorr", the correlation values have been z-transformed (fisher's 
small z transform).  I am somewhat confused as to why some elements in the 
diagonal are not infinite.  The "true" value of applying this transform would 
be infinite on the entire diagonal, as arctanh(1) is infinite.  I am guessing 
this result was generated in matlab, as wb_command actually prevents infinities 
when using the z transform, putting a cap on the correlation (when not using 
z-transform, it shows correlations of 1 as expected).

Whatever analysis you do with correlation matrices like this should ignore the 
diagonal anyway, since it is correlation to itself.

Tim


On Mon, May 15, 2017 at 3:57 AM, Sang-Yun Oh 
<[email protected]<mailto:[email protected]>> wrote:
I downloaded group average functional correlation file: 
HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii

Some diagonal elements of the square matrix (91282x91282) are infinite (Please 
see below).

I want to use this matrix in ananalysis; however, I am not sure how to 
understand or deal with infinite diagonal values.

I appreciate any insight

Thanks,
Sang

======================

In [1]: import nibabel

In [2]: asdf = 
nibabel.load('HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii')

In [3]: img = asdf.get_data()

In [4]: img.shape
Out[4]: (1, 1, 1, 1, 91282, 91282)

In [5]: S = img[0,0,0,0,:,:]

In [6]: S
Out[6]:
memmap([[  8.66434002e+00,   1.96847185e-01,   1.66294336e-01, ...,
          1.01449557e-01,   7.45474100e-02,   1.15624115e-01],
       [  1.96847185e-01,              inf,   3.36383432e-01, ...,
         -5.70017472e-03,  -5.49946353e-02,   3.72834280e-02],
       [  1.66294336e-01,   3.36383432e-01,              inf, ...,
         -4.45242636e-02,  -6.07097335e-02,  -1.51601573e-02],
       ...,
       [  1.01449557e-01,  -5.70017472e-03,  -4.45242636e-02, ...,
                     inf,   1.91883039e+00,   9.20160294e-01],
       [  7.45474100e-02,  -5.49946353e-02,  -6.07097335e-02, ...,
          1.91883111e+00,   8.31776619e+00,   8.82132888e-01],
       [  1.15624115e-01,   3.72833721e-02,  -1.51601573e-02, ...,
          9.20160294e-01,   8.82132888e-01,   8.66434002e+00]], dtype=float32)

In [7]: S.diagonal()
Out[7]:
memmap([ 8.66434002,         inf,         inf, ...,         inf,
        8.31776619,  8.66434002], dtype=float32)



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