Hello Donald and List,

Thank you for coming to my assistance.

Yes, I do want to find regions in the structural gray matter (volume) that are inversely correlated with scores. Specifically, those regions of gray matter that are less involved as scores increase.

Therefore, is this what you mean?

Instead of inputting 'scans', I should input each person's   c1   image

(from which I had derived the gray matter volume information).

Then, as covariates:   score, age, tiv.

I presume that none of the covariates are set to interact with any factor...

Then, for contrasts, I notice that SPM automatically refers to column one as 'mean'; is that a reference to the scans / c1  images, or to the intercept?

If 'mean' is actually the column of c1 / scan image information, then perhaps the contrast is:

mean     score     age     tiv
-1            1            0      0

Is that correct? This confuses me because I think that   -1  1   implies a t-contrast (subtraction rather than correlation....), but if I use 'zero' for scores, then the scores will be set aside from the analysis.   Or, if my contrast is   0   -1   0  0

I suspect that I am actually getting regions of gray matter that INCREASE as scores decrease (which is, unfortunately, not what I am querying).

Or perhaps what I should be doing is an F contrast?

mean     score     age     tiv
-1           0           0         0
0            1           0         0

Thank you.

Julie

On Mon, Jun 8, 2015 at 3:16 PM, MCLAREN, Donald <[log in to unmask]> wrote:

Julie,

If you want to look at neural activation, then you to do an fMRI study, not at VBM study. Putting that aside, it seems that you hypothesis is that want to find areas where grey matter volume is negatively associated with your behavioral score, controlling for age and TIV.

Multiple linear regression model:

(1) Grey matter maps as the imaging dependent variable.

(2) Independent variables are: score, age, and TIV.

If you have a different hypothesis, please explain it in more detail

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Sun, Jun 7, 2015 at 11:34 PM, Julie Morgan <[log in to unmask]> wrote:

Dear SPM experts,

I conducted a behavioral analysis that showed a significant inverse correlation between scores and gray matter (volume) and a significant positive correlation between scores and age. I had covaried out total intracranial volume (tiv).

There is multicollinearity between age and gray matter. However, I can predict scores (regression) separately from age and from gray matter.

 

Now I would like to run an analysis in VBM that would identify neural activation associated with decreased gray matter volume as scores increase.

 

I tried Multiple Regression, entering all the scans in one “go”, then scores, gray matter, and tiv (one column each, no interactions or centering for any of these.)  I set the absolute threshold to 0.2, said ‘yes’ for an implicit mask, and omitted global calculation/normalisation.

 

The design matrix has four columns: 

 mean (automatic name given by SPM)     scores    gray      tiv

Have I set this up correctly?

 

How do I test to show neural activation associated with decreased gray matter volume as scores increase?

 

I searched the helplist and found John Ashburner’s email of June 27, 2003, and well as several other emails about multiple regression using VBM. In that email it is suggested that we should first create groups as a condition using ones and zeros.

However, when I created an F-contrast with

for each participant in agegroup1       1  0  0  (several rows)

for each participant in agegroup2       0  1  0  (several rows)

for each participant in agegroup3       0  0  1  (several rows)

 

the multiple regression batch interface would not accept that ‘vector’. (A vector is one row or column, not an F contrast.....).

 

I can enter 3 groups (the scans themselves) using a factorial design specification but then I am not doing multiple regression.

 

In multiple regression, I could ‘create three groups’  if I entered group (or age, same thing) and specified ‘interaction with factor 1’ but what would factor 1 be?

 

My goal is, still, to run an analysis in VBM that would identify neural activation associated with decreased gray matter volume as scores increase.

 

Could someone please advise me?

 

Thank you.

Julie