How to perform inter-subject connection density normalisation?

2

Some updates:

  1. After reviewing recent literature on structural connectome (SC) mapping using MRtrix3, I have observed a general lack of consensus regarding whether and how to perform inter-subject normalization.

  2. Based on Smith et al. (2022) and previous discussions in this forum (e.g., this post), the recommended approach for ensuring comparable SC across subjects appears to be the following:

  • Use a group-average response function for FOD estimation
  • Apply global intensity normalization with the mtnormalise command
  • Estimate streamline weights using SIFT2 and extract the proportionality coefficient (mu)
  • Generate the SC matrix via tck2connectome , where each element represents the sum of streamline weights
  • Multiply the SC matrix by the proportionality coefficient to obtain the final normalized SC matrix
  • The normalized matrix can then be used for edge-wise inter-subject analyses, with each element representing fibre bundle capacity (FBC) between two regions
  1. If I have followed the Tahedl protocol, my understanding is that the necessary modifications would be:
  • Using a group-average response function instead of a subject-specific one
  • Multiplying the resulting SC matrix by the SIFT2 proportionality coefficient to obtain the final normalized matrix
  1. If a common response function is not used, how substantial would the impact be? Using a common response function can be somewhat inflexible in practice. For example, if we initially estimate the response function using all subjects but later exclude some during analysis, the function must be re-estimated. If we use only a subset of subjects from the start, the choice of this subset introduces uncertainty, which may affect the reproducibility of the results. If the potential bias introduced by using subject-specific response functions is minimal and does not substantially affect the validity of FBC interpretation, I would prefer to use subject-specific function for greater practical flexibility.

As I do not have a strong technical background and find some aspects of Smith et al. (2022) challenging to fully digest, I would greatly appreciate confirmation from the MRtrix3 experts on whether my understanding is correct.