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**BASC: the size of block length in CBB**Showing 1-3 of 3 posts

Oct 14, 2015 11:10 PM | Kangjoo Lee

BASC: the size of block length in CBB

Hi Pierre,

I was recently asked a question about the length of temporal block (h) that has to be determined to replicate data using CBB.

What is suggested from BASC is h=the square root of the total number of time points (T) in each run, in order to preserve the temporal structure in the resting-state fMRI time-series.

So if T=200, h can be randomly selected between 10 and 30, for example.

Assuming another case, when T=500, for example, h is now becoming larger.

The question was, for the "resting state" data, shouldn't a desired level of the temporal structure be the same for any T?

So why not using a temporal block of h=square root of 200 for data with T=500?

What is the best answer for this question?

Best,

Kangjoo

I was recently asked a question about the length of temporal block (h) that has to be determined to replicate data using CBB.

What is suggested from BASC is h=the square root of the total number of time points (T) in each run, in order to preserve the temporal structure in the resting-state fMRI time-series.

So if T=200, h can be randomly selected between 10 and 30, for example.

Assuming another case, when T=500, for example, h is now becoming larger.

The question was, for the "resting state" data, shouldn't a desired level of the temporal structure be the same for any T?

So why not using a temporal block of h=square root of 200 for data with T=500?

What is the best answer for this question?

Best,

Kangjoo

Oct 15, 2015 12:10 AM | Kangjoo Lee

RE: BASC: the size of block length in CBB

one more question, can we apply the same criterion for multi-band
resting-state fMRI data?

Oct 15, 2015 07:10 AM | Pierre Bellec

RE: BASC: the size of block length in CBB

Dear Kangjoo,

The experiment that originally motivated me was to plot the standard deviation of correlation coefficients across bootstrap samples, as a function of block length. The rationale was to have as pessimistic a view as possible, and to maximize variability. The results of this experiment are presented in (Bellec et al, 2008) http://www3.stat.sinica.edu.tw/sstest/ol... Figure 3.1 (see text beginning of section 4.3 for the results, and end of section 2.2. for the procedure). The experiment is actually about differences in correlation coefficients, but I got similar results on the coefficients themselves (not shown). The dependency between h (block length) and the std of correlation values is smooth with a clear maximum. Parameter h does not seem to have a big impact within a reasonable range. So for BASC I decided to randomize it. The sqrt(T) centered interval is more or less arbitrary but was inspired by the curves from the paper.

I guess you could easily reproduce the experiment for multiband data, and see if the same kind of behaviour is observed.

I hope this helps,

Pierre

The experiment that originally motivated me was to plot the standard deviation of correlation coefficients across bootstrap samples, as a function of block length. The rationale was to have as pessimistic a view as possible, and to maximize variability. The results of this experiment are presented in (Bellec et al, 2008) http://www3.stat.sinica.edu.tw/sstest/ol... Figure 3.1 (see text beginning of section 4.3 for the results, and end of section 2.2. for the procedure). The experiment is actually about differences in correlation coefficients, but I got similar results on the coefficients themselves (not shown). The dependency between h (block length) and the std of correlation values is smooth with a clear maximum. Parameter h does not seem to have a big impact within a reasonable range. So for BASC I decided to randomize it. The sqrt(T) centered interval is more or less arbitrary but was inspired by the curves from the paper.

I guess you could easily reproduce the experiment for multiband data, and see if the same kind of behaviour is observed.

I hope this helps,

Pierre