...  ...  @@ 120,8 +120,7 @@ No, the basic functionality of PhysIO, i.e. creating nuisance regressors for you 


Currently, PhysIO natively supports the following physiological logfile types:






 Brain Imaging Data Structure (BIDS)



 [Standard for peripheral recordings]



(https://bidsspecification.readthedocs.io/en/stable/04modalityspecificfiles/06physiologicalandothercontinousrecordings.html)



 [Standard for peripheral recordings](https://bidsspecification.readthedocs.io/en/stable/04modalityspecificfiles/06physiologicalandothercontinuousrecordings.html)



 both raw physiological traces and precomputed pulse events are



supported



 BioPac formats

...  ...  @@ 304,7 +303,36 @@ The most likely explanation, however, is that the setup of PhysIO on your machin 


 If yes, try `rmpath(genpath('path/to/spm12'));addpath('path/to/spm12')` and run the example again.



 More details on this issue can be found in our [GitHub Forum](https://github.com/translationalneuromodeling/tapas/issues/166).






## 17. I cannot find the answer to my question in the FAQ. Whom do I ask for help?






## 17. Which models do I have to include in my physiological regressor matrix? And which number of regressors (model order / delays) per model?






The question about how many and which regressors you need for successful noise removal depends a lot on your experimental design and your research questions. Here are a couple of rules of thumb:






1. To have sufficient degrees of freedom to estimate the parameters of the general linear model (GLM), it is often recommended to have a least 10 times as many datapoints as regressors in your model. That means, for 26 regressors in the GLM, it is advisable to have 260 volumes or more.



2. 26 regressors is the standard output if you include



1. RETROICOR (3rd order cardiac = 6 regressors (1 cosine and one sine per order), 4th order respiratory = 8 regressors, and 1st order multiplicative terms (4: cos*cos, sin*sin, cos*sin, sin*cos),



2. HRV (1 regressor),



3. RVT (1) and



4. motion realignment parameters (6)



5. This list also gives an indication of the order of the regressors in the matrix, but see [question 10](https://tnurepository.ethz.ch/physio/physiopublic//wikis/FAQ/edit#10whatistheorderoftheregressorcolumnsinthemultipleregressorsfile) for details.



3. You can now reduce the number of regressors by the following observations:



1. The RETROICOR 3/4/1 order is taken from a paper that optimized physiological noise removal in the brainstem [1]. You might not need the full model, if you are interested in other brain areas



2. For example, in our PhysIO paper, figure 9 [2], where we evaluated this model for 35 subjects, you can see that the multiplicative terms explain noise mostly in the midbrain, acquaeduct and more inferior parts of the brainstem.



3. Furthermore, also in that figure, you can see that the cardiac RETROICOR terms explain most of the variance (temporal SNR (tSNR) gains of up to 70%, compared to only motion correction), whereas the effect of both respiratory and multiplicative terms is one order of magnitude smaller (5% and 3% tSNR gains, respectively).



4. So, you could probably leave out the multiplicative terms and reduce the number of regressors by 4, or reduced the respiratory terms to 2nd or 1st order, reducing them by 4 or 6, respectively. I have also seen 2nd cardiac order models, reducing by a further 2 regressors.



5. In total, you would end up with 14 regressors for a RETROICOR 3/2/1, 10 regressors for RETROICOR 3/2/0 and 8 regressors for 2/2/0. In each case, you would still add the 8 regressors from HRV, RVT and motion.



4. To get a first idea which sets of regressors contribute to noise removal for your data, you can run FTests over the columns for the respective regressors only (see also [question 11](https://tnurepository.ethz.ch/physio/physiopublic//wikis/FAQ/edit#11howdoiknowwhetherthephysiologicalnoisecorrectionworked) and see where in the brain they explain significant variance (you can also use tapas_physio_compute_tsnr_gains to compute tSNR as in our figure).



5. To formally compare whether it’s warranted to include a set of regressors into your GLM, you would have to do model comparison (as in [1]). There is a toolbox for SPM that can do this automatically for the kind of regressor matrices that are output by PhysIO [3].









[1] Harvey, A.K., Pattinson, K.T.S., Brooks, J.C.W., Mayhew, S.D., Jenkinson, M., Wise, R.G., 2008. Brainstem functional magnetic resonance imaging: Disentangling signal from physiological noise. Journal of Magnetic Resonance Imaging 28, 1337–1344. https://doi.org/10.1002/jmri.21623






[2] https://www.sciencedirect.com/science/article/pii/S016502701630259X#fig0045 from: Kasper, L., Bollmann, S., Diaconescu, A.O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T.U., Sebold, M., Manjaly, Z.M., Pruessmann, K.P., Stephan, K.E., 2017. The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data. Journal of Neuroscience Methods 276, 56–72. https://doi.org/10.1016/j.jneumeth.2016.10.019






[3] Soch, J., Allefeld, C., 2018. MACS – a new SPM toolbox for model assessment, comparison and selection. Journal of Neuroscience Methods 306, 19–31. https://doi.org/10.1016/j.jneumeth.2018.05.017









## 18. I cannot find the answer to my question in the FAQ. Whom do I ask for help?






We are very happy to provide support on how to use the PhysIO Toolbox. However,



as every researcher, we only have a limited amount of time. So please excuse, if

...  ...  