Print

Print


Hi Akira,

The first contrast (C1) is fine for testing laterality: if there is no effect, the average is zero. However, the same contrast, if applied to the original FA maps (i.e., not L-R differences), isn't informative and can be problematic. This contrast tests then if FA is larger than zero, but we know that this is true without any test. Trying to test it causes the statistic to reach extremely high values, which give the warnings when computing TFCE

So, either use C1 to test laterality, or drop it if the idea is to test FA itself.

All the best,

Anderson



--
Anderson M. Winkler
FMRIB / Analysis Group
Blog | Twitter ]


On 4 August 2015 at 17:24, Akira Yogi <[log in to unmask]> wrote:
Hi Anderson,

I put the original values without rounding off. Since I could not paste the copied data, I entered them manually.
Anyway, I restarted randomization on a same computer but it does not work after showing the warning: "tfce has detected a large number of integral steps. This operation may require a great deal of time to complete".
Even though 14 hours later, the calculation have not progressed at all.
Is it because I put the complicated values (with the order of 10^-16)?

Here I attached the matrices just in case.


Best regards,
Akira Yogi




On Tue, 4 Aug 2015 07:44:37 +0100, Anderson M. Winkler <[log in to unmask]> wrote:

>Hi Akira,
>
>Excel can give values very close to zero (something to the order of 10^-16
>I think). Check the formatting options as these other decimal places may be
>hidden. Also, use copy/paste to put the files in the Glm_gui window, so
>that these are preserved.
>
>All the best,
>
>Anderson
>
>
>
>On 4 August 2015 at 00:58, Akira Yogi <[log in to unmask]> wrote:
>
>> Hi Anderson,
>>
>> Thank you for your reply.
>> As you told, the covariate values were rounded off to two decimal places
>> by Excel's function, resulting in incomplete zero mean.
>> I will try to put the value particularly as possible as Excel can show,
>> but it seems to be impossible to have these values being completely
>> zero-mean.
>>
>>
>> Akira Yogi
>>
>