Tanzil Mahmud Arefin1, Choong Heon Lee1, Zifei Liang1, and Jiangyang Zhang1
1Radiology, NYU School of Medicine, New York, NY, United States
Synopsis
In this work, we used our previously reported
high-resolution dMRI-based mouse brain atlas8 to trace node-to-node
thalamo-cortical structural connectivity in the mouse brain. Taking advantage of
the rich viral tracer data from the Allen Mouse Brain Connectivity Atlas
(AMBCA)4, the tractography results were examined using
the tracer data as ground truth. Our findings pinpoint
the potentiality of mapping reliable structural connectivity in gray matter
structures using tractography and at the same time, highlight the necessity of further investigation on determining
the imaging and tractography parameters for accurate estimation of such
connectivity.
Introduction:
Mapping
complex neural connections across a range of spatial scales and in a number of
species has yielded intriguing insights into brain circuits in health and
diseases, and their relationship to behavior1,2. Despite the use of
chemical and viral tracers has allowed direct visualization of neural connections
with high sensitivity and specificity3,4, the technique remained limited
due to its invasiveness and inability to examine multiple circuitries within a
single brain. Diffusion MRI (dMRI) tractography, in contrast, permits
non-invasive mapping of multiple pathways in the entire brain, but often
contains false positives or negatives5-7. Although major white
matter pathways can be reliably reconstructed using dMRI tractography, whether
we can extend it to seek neural connections within largely gray matter
structures remaining to be investigated.
In this work, we used our previously
reported high-resolution dMRI-based mouse brain atlas8 to trace node-to-node
thalamo-cortical structural connectivity in the mouse brain. Taking advantage of
the rich viral tracer data from the Allen Mouse Brain Connectivity Atlas
(AMBCA)4, the tractography results were examined using
the tracer data as ground truth. Our main aim is to find thalamo-cortical
connections that can be reliably traced and lay the ground work for systematic
optimization of dMRI tractography.Methods:
MRI data: High-resolution dMRI of ex-vivo adult mouse brains (n=7) were acquired
using a 7T MR scanner equipped with a receive-only four-channel cryogenic coil
and a modified 3D diffusion-weighted GRASE sequence9,10 with the
following parameters: TE/TR=33/500 ms; NA=2; 0.1x0.1x0.1 mm3; 60 diffusion-directions;
and b=5,000 s/mm2 and one with b=2,000/5,000/8,000 s/mm2.
Probabilistic
fiber tractography mapping: Mouse whole
brain fiber tractogram (WBFT) was generated from the fiber orientation
distribution maps (FOD11, lmax = 6) by placing seeds
randomly within the individual brain mask using second-order integration over
FOD algorithm12. 5 million streamlines were generated for each
subject using the following tractography parameters: FOD amplitude
threshold=0.05, minimum fiber length=3 mm, step size=0.025 mm, angle between
successive steps=45°. Using the spherical deconvolution (SD) informed filtering
of tractograms algorithm13, we corrected the number of streamlines. Then we extracted 14 cortical and 11 thalamic
ROIs using our mouse brain atlas8 to estimate thalamo-cortical
tractograms from the filtered WBFT
of each subject and
further generated tract density images (TDI)14 for individual
trajectory.
TDI and
tracer data co-registration in the atlas space: Estimated TDIs and the tracer data from the AMBCA4 were co-registered into the atlas space (Fig.1A-C) following image
registration pipeline described earlier8. Thus we were able to map TDIs and
the tracer data (ground truth) into a common atlas space (Fig.1D-F) and examine
the similarity index by computing DICE15 score into 3 categories:
similarity level–good (>0.8), moderate (0.6–0.8) and poor (<0.6).Results and discussion:
Patterns
of thalamo-cortical fiber projections: Most
thalamo-cortical connections, except
the connection to the retrosplenial/visual area, passed through the fiber
tracts in the striatum, narrowing through the globus pallidus region of the
pallidum before spreading throughout the thalamus (TH) via thalamic reticular
nucleus (RT) (Fig.2A-E).
Quantitative analysis of the detected
tracts: The consistency between the estimated thalamo-cortical
connectome (Fig.3A) and the ground truth (Fig.3B) was in general high, albeit,
differences were observed in several cortical nodes to thalamic network
projection pathways. False positive (FP) connections were detected in five out
of fourteen cortical nodes, namely temporal association (TEa), somatosensory
barrel-field (SSP-bfd), primary motor (MOp), dorsal auditory (AUDd), and dorsal
retrosplenial area (RSPd) to the TH (Fig.3C). Prefrontal (PFC: ACAd/PL/ILA/ORBl/AId),
gustatory/visceral (GU/VISC) and the ectorhinal area (ECT) on the other hand did
not show any FP projections, yet, several false negative (FN) connections were
identified predominantly toward the ventral group of the dorsal TH (Fig.3C).
One of the reasons behind these discrepancies might be related to partial volume
effect in the GM–WM interface, which often appears at the edge of a nucleus due
to the concoction of high diffusion anisotropy from WM with low anisotropy from
the GM in the nucleus and thus resulting uncertainty in the detection of
terminating fiber tracts.
Based on DICE scores, we found that
the tractography results from VISp, SSp-bfd, and MOp showed strong spatial
agreement (DICE>0.8) with the AMBCA results (Fig.4A), whereas tractography
results from the AUDd, RSPd and TEa showed moderate agreements
(0.6<DICE<0.8) (Fig.4B) and results from other cortical nodes only showed
poor agreements (DICE<0.6) (Fig.4C). However, the DICE scores varied from
good-poor when we further dissected the results for different thalamic nuclei
(Fig.4A-C).
High b-value dMRI improved tract reconstruction: Fig.5A-C
illustrates reconstruction results and DICE scores for 3 representative tracts based
on dMRI data acquired with b=2,000 and 5,000 s/mm2 respectively. The results from dMRI
data with b=5,000 s/mm2
showed better agreement with ground truth than the results with b=2,000 s/mm2. Fig.5D visualizes
the tracts that showed improved DICE scores at b=5,000 s/mm2.Conclusion:
In conclusion, our DW-MRI based adult C57BL/6J
mouse brain atlas can be used to locate anatomical structures and
investigate macroscopic structural connectivity in the mouse brain with high
throughput. The framework reported in this study will serve as a ground for
cross-examination of potential disrupted connections in genetically modified
mouse strains.Acknowledgements
NIH R01 NS 102904References
1. Bassett
DS, Bullmore ET. Human brain networks in health and disease. Curr Opin
Neurol. 2009;22(4):340–347. doi:10.1097/WCO.0b013e32832d93dd
2. Ye
C, Mori S, Chan P, Ma T. Connectome-wide network analysis of white matter
connectivity in Alzheimer's disease. Neuroimage Clin. 2019;22:101690.
doi:10.1016/j.nicl.2019.101690
3. Harris, J. A., Oh, S. W. & Zeng, H. Adeno-associated
viral vectors for anterograde axonal tracing with fluorescent proteins in
nontransgenic and Cre driver mice. Curr. Protoc. Neurosci. 2012; 59, 1.20.1–1.20.18. doi:10.1002/0471142301.ns0120s59
4. Oh
SW, Harris JA, Ng L, et al. A mesoscale connectome of the mouse brain. Nature.
2014;508(7495):207–214. doi:10.1038/nature13186
5. Aydogan
DB, Jacobs R, Dulawa S, et al. When tractography meets tracer injections: a
systematic study of trends and variation sources of diffusion-based
connectivity. Brain Struct Funct. 2018;223(6):2841–2858.
doi:10.1007/s00429-018-1663-8
6. Thomas
C, Ye FQ, Irfanoglu MO, et al. Anatomical accuracy of brain connections derived
from diffusion MRI tractography is inherently limited. Proc Natl Acad Sci U
S A. 2014;111(46):16574–16579. doi:10.1073/pnas.1405672111
7. Calabrese
E, Badea A, Cofer G, Qi Y, Johnson GA. A Diffusion MRI Tractography Connectome
of the Mouse Brain and Comparison with Neuronal Tracer Data. Cereb Cortex.
2015;25(11):4628–4637. doi:10.1093/cercor/bhv121
8. Arefin
T, Lee C, Aristizabal O, et al. High resolution
diffusion magnetic resonance imaging based atlas of the C57BL/6J adult mouse
brain: a tool for examining mouse brain structures. ISMRM, Montreal, 2019. Proc.
Intl. Soc. Mag. Reson. Med. 27.
9. Aggarwal
M, Mori S, Shimogori T, Blackshaw S, Zhang J. Three-dimensional diffusion
tensor microimaging for anatomical characterization of the mouse brain. Magn
Reson Med. 2010;64(1):249–261. doi:10.1002/mrm.22426
10. Wu
D, Reisinger D, Xu J, et al. Localized diffusion magnetic resonance
micro-imaging of the live mouse brain. Neuroimage. 2014;91:12–20.
doi:10.1016/j.neuroimage.2014.01.014
11. Tournier
J, Calamante F, Gadian G, Connelly A. Direct estimation of the fiber
orientation density function from diffusion-weighted MRI data using spherical
deconvolution. NeuroImage. 2004;23
(3), 1176–1185. doi.org/10.1016/j.neuroimage.2004.07.037
12. Tournier J, Calamante F, Connelly A. Improved
probabilistic streamlines tractography by 2nd order integration over fibre
orientation distributions. Proceedings of the International Society for
Magnetic Resonance in Medicine, 2010, 1670.
13. Smith
R, Tournier J, Calamante F, Connelly A. SIFT: Spherical-deconvolution
informed filtering of tractograms. NeuroImage. 2013; 67:298-312.
doi: 10.1016/j.neuroimage.2012.11.049.
14. Calamante
F, Tournier J, Jackson G, Connelly A. Track-density imaging (TDI):
super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage.
2010;53(4):1233-43. doi:
10.1016/j.neuroimage.2010.07.024.
15. Dice,
L.R. Measures of the Amount of Ecologic Association between Species. Ecology.
1945; 26(3):297–302.