Hi Khoi,

Please, see below:

On 12 June 2016 at 05:43, Khôi Huỳnh Minh <[log in to unmask]> wrote:

Dear Anderson,

Sorry for pull out this topic

again but I have a question that related to what I mentioned before.

Is
the time series extracted from ICA have that characteristic of the haemodynamic response ? I means we all know that BOLD signal can be model by HRF and a set of stimulus and deconvolute BOLD signal with suitable stimulus can give us HRF. How about for time series from ICA result.

We would hope so, yes, after unmixing, the time courses still represent BOLD responses, that could, potentially, be subjected to deconvolution.

I have recorded the time seed when subjects in my experiment tap their finger. I try to convolute this with HRF and find out that the result is somewhat correlate with one IC time series (correlation about 0.3-0.4). The spatial map of that IC show activation at motor cortex and frontal lobes. Can we conclude that the spatial map is the activation map for the "finger tapping" activities ? I doubt that by decompose fMRI signal into ICs, the IC time series do not have haemodynamic response characteristic.

Yes, that sounds the right interpretation. However, given it is a task-based experiment, and given that you know the stimulus onsets, perhaps a more powerful and successful approach would be to use the GLM, as opposed to ICA.

All the best,

Anderson

It would be appreciated if you can help me point out the problem as my conclusion make me feel somewhat fallacious.

Best regards,

Khoi

On Fri, May 13, 2016 at 3:27 PM, Anderson M. Winkler <[log in to unmask]> wrote:

Hi Khoi,

I understand that the null hypothesis is that there is no pattern across subjects, thus the expected average across subjects would be a map of all zeroes. This doesn't seem a very good hypothesis from the outset, as we may expect that commonalities among timecourses would be enough to render the maps similar among them. Still, such a test can be done with unthresholded maps, concatenated (in standard space) and tested in randomise with the option -1.

To make this a bit more objective, consider taking, for each component, the one that has the strongest correlation of timecourse with the stimulus function, even if such correlation is poor for some subjects.

That said, perhaps better is to simply use the GLM: since you have already a sequence of stimulus, these can be used as the regressors in a 1st level, and the common pattern can be found in a higher level. There will be no risk for circularity whatsoever, and no issues related to the scaling of components (z-stat, etc).

All the best,

Anderson

On 13 May 2016 at 04:34, SUBSCRIBE FSL Khoi Huynh <[log in to unmask]> wrote:

Dear FSL experts,

After running single ICA for all subjects, I find that each subject has 1 IC which its time series is highly correlated with my interest stimulus. The stimulus design is not the same for all subject hence I cannot use tensor ICA. I want to maintain as much as information possible so I dont want to use concat ICA (since concat ICA will run in MNI space instead of subject space).

Here is what I got after my single ICA run:
-Time series of IC 1 of subject 1 is correlated with the event subject 1 lost the game -> zstats_threshold map of IC1 - subject 1
-Time series of IC 7 of subject 2 is correlated with the event subject 2 lost the game -> zstats_threshold map of IC7 - subject 2
.... so on.

I registered all the zstats_threhold map to MNI space but then stuck at finding a way to find common pattern of them.
Hence, is there any way in FSL that I can find common activation from all of the zstats_threshold maps ? I am thinking of average all the map and threshold the result at a specific threshold but I feel like it is not the correct way.

It would be very appreciated if anyone can give me any advice.

Best regards,
Khoi