Hi @Uri_Shinitsky,and welcome to Neurostars!
As the articles and threads point out, there really isn’t a “best” method, especially for task based GLMs which have not been extensively benchmarked like resting state connectivity has.
A denoising scheme from fmriprep confounds may look like:
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to control for motion: some amount head motion parameters (6 is most basic, but you could also choose to include the squared and derivative expansions for up to 24 terms). Alternatively, using the edge regressors (e.g., This paper) introduced in recent versions are promising, but still yet to be rigorously evaluated.
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for physiological noise: some amount of acompcor regressors (either a set number or enough components to account for 50% variance) or mean tissue class signal. Note that if you use acompcor components you should also use the cosine columns in the confounds to high pass filter your data.
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volume censoring: removing motion outliers and/or non-steady-state volumes
The choice of what you go with should be motivated by the quality and amount of data you have and what you want to analyze.
If you have lots of within-subject data (short TR, long acquisition) then maybe you will be comfortable with higher temporal DOF loss from more aggressive (I.e more terms) pipelines.
If you think a task will cause motion, maybe don’t scrub out motion outliers, which may introduce bias against your task.
Global signal regression is always hotly debated. Do or do not do at your own risk 
White matter signal could actually be informative during tasks (Functional MRI and resting state connectivity in white matter - a mini-review - PubMed), so if that’s something important for you, don’t regress it out.
The best thing to do is to find some independent but similar data, and try out different denoising pipelines there, to figure out what to apply to the data of interest. This will avoid the urge to try different denoising strategies on your own data until one comes out that happens to confirm your hypothesis…
For mathematical interpretations of biases that different regressors may introduce, the articles will spell it out better than we could here.
Best,
Steven