Dear experts,

I installed your toolbox and I've encountered some issues compiling the batch. 

First, is there a way to choose the best HRF basis function based on my data?

Could be a correct approach to estimate both canonical and gamma functions and then compare them? If so, how should I do it operatively?

In the case of gamma function, how can I estimate the number of basis functions?

In Threshold (SD) for event detection should I change the value (i.e 1)? (considering both the options: canonical and gamma function)

Also, what is K (local peak)? How is it calculated?

Many thanks in advance

Beatrice

Dear Beatrice

thanks for your questions, please see some replies inline below

 

First, is there a way to choose the best HRF basis function based on my data?

There's no universal rule. The gamma mixtures will best reproduce a wide variety of shapes. FIR is more agnostic and data driven, and possibly more suitable if your target property is the whole hrf shape, for example with afni 3dMSS. It's also more suitable for unusual shapes, like animal or pathology.

Could be a correct approach to estimate both canonical and gamma functions and then compare them? If so, how should I do it operatively?

Unless there's a "ground truth" to compare to, then it's difficult. Indicators such as spatial or temporal variance can be useful though.

In the case of gamma function, how can I estimate the number of basis functions?

It's a choice you make, rather than an estimation. To avoid overfitting, the number should not be too high.

In Threshold (SD) for event detection should I change the value (i.e 1)? (considering both the options: canonical and gamma function)

This is not necessary, but it's indeed a parameter that you can choose to tune.

Also, what is K (local peak)? How is it calculated?

See eq. [3] of the tutorial paper, where K is defined as the range of x values in which we look for a peak.: 

y(i) is defined as peak: y(i)>y(i+x),  x=[-K: K];     

 

I hope this helps

Dear Daniele, 

Thank you for your kind reply.

Based on your feedback we'll adopt the FIR function. Following the idea of choosing the most agnostic function, would you suggest to use the smoothed FIR instead of the standard FIR?

Based on the function selected, could the default parameter (32) for HRF's length be ok as a standard value or does it need to be optimised?

See eq. [3] of the tutorial paper, where K is defined as the range of x values in which we look for a peak.: 

Then, before the estimation of the HRF, we preprocessed rs-fMRI data using CONN toolbox. Which nuisance covariates in your opinion should be insered in the batch?

Thank you so much for your help

Sincerely

Beatrice

Originally posted by daniele marinazzo:

Dear Beatrice

thanks for your questions, please see some replies inline below

 

First, is there a way to choose the best HRF basis function based on my data?

There's no universal rule. The gamma mixtures will best reproduce a wide variety of shapes. FIR is more agnostic and data driven, and possibly more suitable if your target property is the whole hrf shape, for example with afni 3dMSS. It's also more suitable for unusual shapes, like animal or pathology.

Could be a correct approach to estimate both canonical and gamma functions and then compare them? If so, how should I do it operatively?

Unless there's a "ground truth" to compare to, then it's difficult. Indicators such as spatial or temporal variance can be useful though.

In the case of gamma function, how can I estimate the number of basis functions?

It's a choice you make, rather than an estimation. To avoid overfitting, the number should not be too high.

In Threshold (SD) for event detection should I change the value (i.e 1)? (considering both the options: canonical and gamma function)

This is not necessary, but it's indeed a parameter that you can choose to tune.

Also, what is K (local peak)? How is it calculated?

See eq. [3] of the tutorial paper, where K is defined as the range of x values in which we look for a peak.: 

y(i) is defined as peak: y(i)>y(i+x),  x=[-K: K];     

 

I hope this helps

 

Following the idea of choosing the most agnostic function, would you suggest to use the smoothed FIR instead of the standard FIR?

Both options are possible, sFIR allows for more spatial smoothing.

Based on the function selected, could the default parameter (32) for HRF's length be ok as a standard value or does it need to be optimised?

This is measured in seconds, so it's automatically adjusted to the TR. If for some reason you expert your HRF to be longer than 32 seconds (or much shorter), you can change this.

I still have some doubt about the meaning of k. Could be the default value (k=2) a good choice?

It's the range over which we search for the optimal lag (in TRs) between a pseudo-event and the HRF onset. The default is ok, unless you have a very short TR (0.2s or so).

we preprocessed rs-fMRI data using CONN toolbox. Which nuisance covariates in your opinion should be insered in the batch?

 If CONN is used for denoising, simply skip the denoising step in rsHRF (again, the default opinion is to do nothing). The order of processing might have an influence https://royalsocietypublishing.org/doi/1...