Diffusion MRI streamlines tractography has become the main technique for inferring structural brain connectivity. This is typically achieved by constructing a connectome by identifying streamlines that link regions-of-interest defined by an atlas-based parcellation scheme to provide a summary of white matter (WM) connections between pairs of grey matter (GM) regions (i.e. network nodes)^1-2. The mechanism used to assign streamlines to nodes is potentially crucial to providing meaningful characterisation of the connectome. Ideally, each streamline should connect exactly 2 nodes. However, track termination criteria in many fibre-tracking algorithms result in premature termination of streamlines within WM or CSF. This causes streamlines apparently being associated with either zero or only one GM regions (up to ~50–80%^2-3), or even non-GM regions within nodes. Such apparent ‘connectivity’ is not biologically meaningful, and could result therefore in a misleading connectome in non-trivial ways. The biological plausibility of streamline terminations in principle can be improved by using methods such as anatomically-constrained tractography⁴ (ACT), which accepts a streamline only if it terminates within either cortical, sub-cortical GM, or brainstem. This study investigates the influence of track assignment strategies and their interactions with some popular parcellation schemes –with or without the application of ACT⁴– for identifying pairwise connectivity between nodes, revealing important implications for connectome quantification.

MRI acquisition: T1s and DWIs (2.5-mm isotropic resolution, 60 directions, b=3000 s/mm²) of 22 healthy volunteers were acquired using a Siemens 3T Tim Trio MRI scanner.

Tractogram reconstruction: Fibre orientation distributions (FODs) were computed using constrained spherical deconvolution⁵. For each scan, tractograms of 10 million streamlines were generated through seeding from WM mask, tracking either with or without ACT using the iFOD2 algorithm⁶.

GM parcellation: Two parcellation schemes were used (Fig. 1): (i) the T1-based FreeSurfer parcellation⁷; (ii) the AAL atlas⁸ transformed onto each individual’s T1 space.

Connectome construction: Streamlines were assigned to nodes by: (i) end voxels = voxels at streamline endpoints; (ii) local search = a search from each streamline endpoint to locate the nearest node within a 2-mm radius.

Fig. 2 shows the histogram of node count per streamline. The results are summarised as follows:

Effect of assignment mechanism: Compared to using end voxels (Fig. 2(a)), using the local search (Fig. 2(b)) increased the proportion of identifying 2 nodes, particularly when FreeSurfer parcellations were used.

Effect of ACT: Compared to non-ACT, the use of ACT improved the overall ability of identifying 2 nodes. Combining ACT with the local search (Fig. 2(b)), the number of tracks identifying 2 nodes reached ~90% for both parcellation schemes.

Effect of parcellation scheme: Due to the presence of some WM voxels in the AAL parcels, the probability of streamlines reaching nodes was higher than using the FreeSurfer parcellation. When using end voxels (Fig. 2(a)), the parcellation scheme had a dominant effect; e.g. when using the FreeSurfer parcellation, <25% of streamlines were assigned to 2 nodes.

Our data demonstrate that the connectome construction can be strongly influenced by many factors during the process of assigning streamlines to nodes; this process is non-trivial, and a number of confounds can introduce ambiguity into connectome quantification. For non-ACT, using a parcellation scheme with parcels not constrained to the GM ribbon, such as the AAL atlas, does not provide a meaningful compensation for the defect in both tracking terminations and assignment mechanism: the increase in pairwise connections using AAL probably largely results from those tracks terminating within WM but which nevertheless reach the AAL parcels at both endpoint voxels. By contrast, using anatomical information to constrain track terminations to the interface of GM and WM, such as ACT⁴, is more beneficial for identification of connectivity between GM parcels; however, the efficacy of connectome construction also relies on the consistency between tissue segmentation and GM parcellation (Fig. 3). This explains why a short local search (~2 voxels) is helpful in overcoming small inconsistencies, although the biological plausibility of such a ‘searching’ process requires further investigation.The commonly-used mechanism by which individual streamlines contribute to the connectome may be ill-defined in many instances. In practice, it is difficult to develop such a mechanism that can effectively deal with every potential scenario, and connectomes constructed based on such methods may actually be heavily influenced by these mechanisms rather than realistic biology. In this context, both robust terminations of streamlines respecting anatomical information at the fibre-tracking stage and an appropriate mechanism at the node assignment stage should be beneficial to identifying pairwise GM connections from meaningful endpoints for connectome construction.