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In-vivo microstructural characterisation of first and higher order thalamic nuclei: a diffusion kurtosis imaging study
Sebastian Hübner1, Lisa Novello1, Giacomo Tomezzoli1, and Jorge Jovicich1
1CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto (Trento), Italy

Synopsis

The thalamus is a primary station for information processing within the brain. It is composed of a collection of nuclei with different histological and functional properties, classified as First Order (FO, processing sensory input) or Higher Order (HO, playing associative roles). We used diffusion-weighted MRI (dMRI) to investigate microstructural properties of FO and HO thalamic nuclei, focusing on fractional anisotropy, mean diffusivity, and mean kurtosis derived from Diffusion Kurtosis Imaging (DKI). This initial evidence suggests that DKI might capture distinctive features of FO and HO thalamic nuclei, and paves the way to further studies investigating nucleus-specific microstructural effects with DKI.

Introduction

The thalamus is a collection of grey matter nuclei primarily involved in gating ascending cortical input and modulating cortical activity via transthalamic cortico-cortical pathways1. Histological research2,3 has differentiated thalamic nuclei in terms of microstructural features such as myelination, cell size and cell density. A more functional classification distinguishes thalamic nuclei as supporting First Order (FO) or Higher Order (HO) functions4. Despite this, the thalamus is often considered a uniform structure in MRI studies, except for the recent possibility of segmenting its nuclei from T1 anatomical MRI5. However, it is yet unclear whether diffusion-weighted MRI (dMRI) can capture the microstructural heterogeneity across nuclei. In this study, we evaluate the sensitivity of diffusion MRI to differentiate microstructural properties of thalamic nuclei, in particular to differentiate FO and HO nuclei in terms of DKI-derived fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK)6,7.

Methods

Acquisition: Healthy adults (N=10, mean age (years, sd)=26.7 (4.4)) underwent scanning using a 3T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany) with a 64-channel head-neck RF receive coil, to acquire 3D T1-weighted (T1w, multi-echo-MPRAGE, 1mm-isotropic), and dMRI (2mm-isotropic, TE/TR=76/4200ms, shells: b={0,700,1000,2850} s/mm2, 32/64/64 directions) data.
Image processing: T1w images were skull stripped and processed in FreeSurfer v.7.1.1 recon-all. Thalamic nuclei were segmented from T1w images with FreeSurfer’s thalamic segmentation algorithm5 and volumetric data for each nucleus was obtained. Thalamic nuclei larger than 100 mm3 were considered. FO nuclei: medial geniculate (MGN), lateral geniculate (LGN), ventral lateral anterior (VLa), ventral lateral posterior (VLp), ventral posterolateral (VPL). HO nuclei: centromedian (CM), mediodorsal lateral (MDl), mediodorsal medial (MDm), anterior (PuA), inferior (PuI), lateral (PuL), and medial (PuM) divisions of the pulvinar, and ventral anterior (VA). dMRI data were denoised8, Gibbs-ringing corrected9, and corrected for eddy currents, head motion, and geometric distortions in FSL’s eddy v.6.0.310, and for bias field in MRtrix v.3.0.2. dMRI data was then fitted with DKI and FA, MD, and MK maps were derived7. Linear registrations between each participant’s first b=0 volume and T1w image were computed in ANTs and applied to thalamic segmentations in order to extract the median of each DKI-derived metrics from each nucleus segmentation aligned in the original dMRI image space.
Statistical analyses: The Kruskal-Wallis test was used in R11 to investigate the effect of nucleus for each DKI metric. Independent samples t-tests were used in R to assess differences in DKI indices between FO and HO nuclei. All p-values were Bonferroni corrected for the three DKI metrics investigated. Cohen’s d was used to determine the effect size of differences between FO and HO nuclei for each DKI index.

Results

Fig. 1 shows the thalamic parcellations of a representative subject. Volumetric results agreed with the literature5,12. Scatter plots and distributions of median dMRI metrics for the considered nuclei are shown in Fig. 2 and Fig. 3, respectively. Mean (SD) values for all dMRI metrics and nuclei are listed in Fig. 4. Kruskall-Wallis tests detected a significant effect of nucleus for all investigated metrics (FA: χ²=88.17; MD: χ²=61.34; MK: χ²=83.4; all padj<0.0001). Post-hoc t-tests revealed larger FA and MK for FO as compared to HO nuclei (FA: t=6.92; MK: t=8.37, all padj<0.0001), and lower MD in FO than in HO nuclei (t=-2.59, padj<0.05). Both MK and FA showed large effect sizes, with the largest value for MK (dMK=1.47, dFA=1.40), while MD showed a small effect size (dMD=-0.44).

Discussion and Conclusions

While the thalamus is often considered as a single structure in MRI studies, our findings indicate that dMRI can capture microstructure heterogeneity associated with thalamic nuclear subdivisions, as expected from histological studies2,3. FA and MK provided higher sensitivity than MD to differentiate between FO and HO nuclei. A DKI-based characterisation of thalamic nuclei has already been attempted in an animal study13, and provided to be in good agreement with histology. Our findings suggest that the combination of DKI metrics such as FA, MD, and MK may help characterise thalamic heterogeneity, and possibly differentiate between FO and HO thalamic nuclei. Since both FA14,15 and MK16,17,18 are associated with restrictions from cellular membranes and myelination, we speculate that the biological underpinnings of such differences in DKI-derived metrics might be represented by different densities of myelinated axonal fibers across the two groups of nuclei. Intriguingly, the potentially larger microstructural complexity of sensory nuclei might reflect the high degree of precision required in these nuclei for sensory processing to be effective19. In conclusion, these findings suggest that DKI metrics may be used to study microstructural changes in thalamic nuclei, both in health and disease.

Acknowledgements

The authors would like to thank Stefano Tambalo for his support with anatomical data processing.

References

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Figures

Fig. 1: Freesurfer automated thalamic parcellation from a sample subject. From top left, clockwise: lateral and medial three-dimensional views of thalamic nuclei in the right hemisphere, sagittal (right), axial, and coronal sections. Nuclei colors are as in Iglesias et al. (2018).

Fig. 2: Scatter plots of DKI metrics in first order (red) and higher order (blue) thalamic nuclei. The 3D scatter plot on the left shows DKI-derived FA, MD, and MK in thalamic nuclei, and 2D scatter plots on the right show pairs of DKI-derived metrics. Abbreviations as in text.

Fig. 3: Violin plots of DKI metrics in first order (left, red) and higher order (right, blue) thalamic nuclei. Abbreviations as in text.

Fig. 4 Mean (SD) DKI metrics of thalamic nuclei are presented. a) MD is in 10-3 mm/s2; b) FA: FO>HO, p<0.0001; c) MD: FO<HO, p<0.05; d) MK: FO>HO, p<0.0001. Abbreviations as in text.

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)
1645
DOI: https://doi.org/10.58530/2022/1645