NITRC Resting State Hemodynamic Response Function Retrieval and Deconvolution (RS-HRF) Forum: help

RE: Statistical analysis

daniele marinazzo — Wed, 26 Jun 2024 8:21:25 GMT

Hi
 
sorry for the late reply. You can extract some shape parameters (width, height, time to peak) and use them as (individual or multiple) voxel-wise dependent variables, or use the full shape of the HRF, for example using AFNI's 3dMVM or 3dMSS.

RE: Batch editing

daniele marinazzo — Wed, 26 Jun 2024 8:18:52 GMT

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/10.1098/rsta.2015.0190

RE: Batch editing

lxop — Tue, 25 Jun 2024 10:33:21 GMT

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.: 
<div style="padding-left: 40px;">y(i) is defined as peak: y(i)>y(i+x),  x=[-K: K];     </div>
<div style="padding-left: 40px;"> </div>
<div>Thank you for the explanation. However, I still have some doubt about the meaning of k. Could be the default value (k=2) a good choice?</div>
<div class="yj6qo ajU"> </div>
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:
<blockquote>
Dear Beatrice
thanks for your questions, please see some replies inline below
<blockquote>
 
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.: 
<div style="padding-left: 40px;">y(i) is defined as peak: y(i)>y(i+x),  x=[-K: K];     </div>
<div class="yj6qo ajU"> </div>
</blockquote>
I hope this helps
 
</blockquote>

RE: Batch editing

daniele marinazzo — Tue, 25 Jun 2024 7:32:59 GMT

Dear Beatrice
thanks for your questions, please see some replies inline below
<blockquote>
 
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.: 
<div style="padding-left: 40px;">y(i) is defined as peak: y(i)>y(i+x),  x=[-K: K];     </div>
<div class="yj6qo ajU"> </div>
</blockquote>
I hope this helps

Batch editing

lxop — Thu, 20 Jun 2024 16:00:47 GMT

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

Statistical analysis

aestevez29 — Mon, 20 May 2024 14:59:49 GMT

I am new analysing resting state fMRI's and using the rsHRF toolbox to find differences in HRF parameters between control and very mild cognitive impairment group.
I have done Voxel-wise and ROI-wise deconvolution but now I am not sure on how to use the output of the toolbox to perform statistical analysis. Any help will be much appreciated
Andrea

RE: Issue with loading 4D nifti data

guorong wu — Wed, 14 Dec 2022 16:04:57 GMT

double click 'Scans' (or 'Specify' bottom), you will see 'frames' at the bottom left.
you can also only select the first volume of 4D nifti data.

Issue with loading 4D nifti data

Marialena Dounavi — Wed, 14 Dec 2022 14:42:51 GMT

Hello,

I am trying to run the voxel-wise HRF analysis using preprocessed 4D nifti data but I am getting the following message:

[b]'Please check your input data: 4 volumes ? set frames "inf" for 4D NIfTI data'[/b]

However, in the batch editor I cannot see any entry termed 'frames'.

I would be grateful for any advice on how to resolve this.

Best wishes,
Marialena

seed-to-vertex with a seed from volumetric space

kav — Tue, 28 Sep 2021 8:13:53 GMT

Dear rsHRF team,

Thank you very much for your work on this toolbox. I am interested in implementing this but I had a question about the workflow for my analysis.

I am using HCP data and am constructing seed-to-vertex FC for a seed that is extracted from volumetric space (as it may include white matter). I was thinking I would first have to construct a vector of the mean time-series for the seed (number of timepoints x 1), save this as a .mat file, and then perform the deconvolution steps through the "Signals" option. Next, I would deconvolve the func.gii files using the "Vertices" option. Is this correct so far?
I was then unsure how to perform FC analysis with your toolbox, as I am only interested in edges from each vertex to my seed. Would this be possible with your toolbox?

Thank you in advance,
Kav

RE: Before rsHRF

daniele marinazzo — Sat, 23 Mar 2019 9:14:11 GMT

Dear Ariadna

thanks a lot for your message.

High pass filter is ok, the important thing is to do low pass too, and no problem if you do it twice.

About spatial smoothing, you can include it (it's better to extract nuisance variables white matter/CSF from unsmoothed data) or not include it if you want better spatial resolution.

If you download the rsHRF plugin for SPM, you will find the suitable preprocessing options for example in this demo job

./spm/rsHRF/demo_jobs/vox_deconv_job1.mat

Hope this helps.

Daniele