Print

Print


Thanks so much!
-Pete

> Hi Peter,
>
>> I've collected resting-state fmri data from a patient group and a
>> control
>> group. After processing the data using the pre-stats tab in FEAT and
>> subsequently running MELODIC, I merged the subjects' melodic outputs
>> together and reran MELODIC. From the resulting output, I've
>> indentified a
>> number of components of interest that I'd like to compare between
>> the two
>> groups.
>
>> My understanding of what I should do is the following:
>>
>> 1) Create a mask representing the combined thresh_zstat maps for
>> both groups.
>
> nothing wrong with this, but you won't need it later
>
>> 2) Concatenate all the thresh_zstat maps of a particular component
>> for all
>> subjects, with all the subjects in one group first, followed by all
>> the
>> subjects in the second.
>>
>
> No, you wouldn't want to run any model based higher-level analysis on
> thresholded lower-level stats images - simply use the associated
> unthresholded maps instead.
>
>
>> 3) Create design.mat and design.con files using the GLM Setup utility,
>> where my design looks like the following:
>>     group    EV1     EV2
>>             groupA  groupB              Title      EV1    EV2
>> s1   1         1      0       | C1   groupA mean    1      0
>> s2   1         1      0       | C2   groupB mean    0      1
>> s3   1         1      0       | C3      A > B       1     -1
>> s4   1         1      0       | C4      B > A      -1      1
>> s5   2         0      1       |
>> s6   2         0      1       |
>> s7   2         0      1       |
>> s8   2         0      1       |
>>
>
> That sounds OK
>
>> 4) run a Two-Sample Unpaired T-test using RANDOMISE with the following
>> command:
>>
>> randomise -i <4D_input> -o <output> -d design.mat -t design.con -c
>> 0.99
>>
>
> nope, the argument to the -c (or -C) option is in units of the raw t-
> stats (not z) that is being formed within randomise, i.e. if you want
> a cluster forming threshold of t>2.3 use that number here. The output
> file contains the p-values, i.e. you can threshold that one at 0.99,
> say after having obtained it from randomise.
> hope this helps
> christian
>
>
>
>> Here are my questions:
>> Does it appear I am setting up the design correctly?
>> Is using the option "-c 0.99" the equivalent of looking for
>> significance
>> at p<0.01?
>> Do you have any other suggestions or recommendations for carrying
>> this out?
>>
>> You help is greatly appreciated. Thanks in advance.
>>
>> Cheers,
>>
>> Pete
>>
>> Center for Magnetic Resonance Research
>> University of Minnesota
>
> ____
> Christian F. Beckmann
> University Research Lecturer
> Oxford University Centre for Functional MRI of the Brain (FMRIB)
> John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
> [log in to unmask]	http://www.fmrib.ox.ac.uk/~beckmann
> tel: +44 1865 222551			fax: +44 1865 222717
>