7/15/16, 5:21 PM
Page 1 of 3http://indexsmart.mirasmart.com/ISMRM2016/PDFfiles/0118.html
The inﬂuence of node assignment strategies and track termination
criteria on diﬀusion MRI-based structural connectomics
Chun-Hung Yeh , Robert Elton Smith , Thijs Dhollander , Fernando Calamante , and Alan Connelly
The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
This study highlights the issue of using the common strategyThis study highlights the issue of using the common strategy
for assigning individual streamlines to an atlas-based brainfor assigning individual streamlines to an atlas-based brain
parcellation. This process is non-trivial and can introduceparcellation. This process is non-trivial and can introduce
ambiguity into connectome quantiﬁcation. In many ﬁbre-ambiguity into connectome quantiﬁcation. In many ﬁbre-
tracking algorithms, track termination criteria can causetracking algorithms, track termination criteria can cause
premature termination of streamlines within WM or CSF,premature termination of streamlines within WM or CSF,
which can result in up to ~50–80% of streamlines failing inwhich can result in up to ~50–80% of streamlines failing in
identifying pairwise connections between nodes fromidentifying pairwise connections between nodes from
streamline endpoints. Our results demonstrate that suchstreamline endpoints. Our results demonstrate that such
issue can be largely ameliorated through the combination ofissue can be largely ameliorated through the combination of
biologically meaningful track terminations and anbiologically meaningful track terminations and an
appropriate node assignment mechanism. This couldappropriate node assignment mechanism. This could
therefore be advantageous to structural connectometherefore be advantageous to structural connectome
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) . 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% ), 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 tractography (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 ACT – for identifying pairwise
connectivity between nodes, revealing important implications for
MRI acquisition: T1s and DWIs (2.5-mm isotropic resolution, 60
directions, b=3000 s/mm ) of 22 healthy volunteers were acquired using
a Siemens 3T Tim Trio MRI scanner.
1 1 1 1 1
Fig. 1.Fig. 1. Compared to the FreeSurfer
parcellation (left), the AAL parcels (right)
extend far into the WM; the spatial extent of
these GM parcels may inﬂuence the frequency
of successful streamlines assignment.
Fig. 2.Fig. 2. Histograms of node count per
streamline. Tractograms, generated either with
or without ACT, were assigned to network
nodes deﬁned by either FreeSurfer or AAL
parcellation through the (a) end voxels and (b)
Fig. 3.Fig. 3. (a) The segmented anatomical
reference map overlaid with track endpoints
(yellow) and the FreeSurfer parcellation. (b-c)
With ACT, track endpoints (see purple arrows)
are located at the GM-WM interface, which
could however be inconsistent with the GM
parcellation due to factors such as
discretisation of GM labels.
7/15/16, 5:21 PM
Page 2 of 3http://indexsmart.mirasmart.com/ISMRM2016/PDFfiles/0118.html
Tractogram reconstruction: Fibre orientation distributions (FODs) were
computed using constrained spherical deconvolution . For each scan,
tractograms of 10 million streamlines were generated through seeding
from WM mask, tracking either with or without ACT using the iFOD2
GM parcellation: Two parcellation schemes were used (Fig. 1): (i) the T1-
based FreeSurfer parcellation ; (ii) the AAL atlas 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
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
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
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 ACT , 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
7/15/16, 5:21 PM
Page 3 of 3http://indexsmart.mirasmart.com/ISMRM2016/PDFfiles/0118.html
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.
No acknowledgement found.
1. Bullmore E & Sporns O. Complex brain networks: graph theoretical
analysis of structural and functional systems. Nat Rev Neurosci.
2. Hagmann P, et al. Mapping the structural core of human cerebral
cortex. PLoS Biol. 2008;6(7):e159.
3. Zalesky A, et al. Whole-brain anatomical networks: does the choice of
nodes matter? NeuroImage. 2010;50(3):970-83.
4. Smith RE, et al. Anatomically-constrained tractography: improved
diﬀusion MRI streamlines tractography through eﬀective use of
anatomical information. NeuroImage. 2012;62(3):1924-38.
5. Tournier JD, et al. Robust determination of the ﬁbre orientation
distribution in diﬀusion MRI: non-negativity constrained super-resolved
spherical deconvolution. NeuroImage. 2007;35(4):1459-72.
6. Tournier JD, et al. Improved probabilistic streamlines tractography by
2nd order integration over ﬁbre orientation distributions. Proc. ISMRM.
7. Desikan RS, et al. An automated labeling system for subdividing the
human cerebral cortex on MRI scans into gyral based regions of interest.
8. Tzourio-Mazoyer N, et al. Automated anatomical labeling of activations
in SPM using a macroscopic anatomical parcellation of the MNI MRI
single-subject brain. NeuroImage. 2002;15(1):273-289.
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 0118