AFD in the human connectome dataset?

[Dave]

Hi Dorian,

@DorianP

It looks like AFD is the measure that I will need (FA is too outdated, right?)

Yes, for performing analysis of connectivity using streamlines we would recommend using AFD to perform SIFT on your tractogram.

@DorianP

If that is the case, shall I just use average_response to average the 5tt responses (GM.txt, CSF.txt, WM.txt) from all subjects?

Yes, however you may need to update since average_response can only recently average over multiple b-values.

@DorianP

The quantitative analysis tutorial suggests using dwidenoise, dwibiascorrect, and dwiintensitynorm. Do you still advise to run these steps on the HCP data (I am using the preprocessed data, exactly as pointed out in the tutorial) ?

You can skip step 2 (dwipreproc), if you are using the pre-processed HCP data. Strictly speaking you should perform dwidenoise (step 1) before any other pre-processing as effects such as interpolation will change the properties of the noise. HCP data is good enough to skip this step anyway.

@DorianP

Finally, I am a bit confused what should I use to extract AFD: afdconnectivity or fod2fixel?

If you are performing analysis of streamlines, then tcksift will automatically estimate the AFD per fixel from the input FOD image. @rsmith, feel free to chime in here about the stopping criteria.

@DorianP

1. Could these data be already on a similar scale, such that no intensity normalization is needed.

No, it’s unlikely that these will already be intensity normalised. Since most DWI models perform a voxel-wise normalisation by dividing the DW signal by the b=0 within each voxel. [quote=“DorianP, post:2, topic:607”]

2. Can I apply intensity normalization as an additional step on their preprocessed data (i.e., a 5th step).
   [/quote]
   Yes

@DorianP

One last question that I couldn’t find the answer:Is the ratio between number of seeded streamlines and number of selected streamlines indicative of the connectivity of the tract? I.e., if the left hemisphere tract selects 300 tracts out of 478,000 and the right hemisphere tracts selects 170 out of 1 million, is this indicative that the left tract is more robust, or strong, or prominent?

The short answer is no. It should have nothing to do with seeded/selected. The number of steamlines in the tract after tracking are what you want to compare. However, this is only if you have performed SIFT, in addition to appropriate pre-processing. @rsmith, feel free to elaborate. Or link to any previous useful discussions on this.