DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei

The thalamus is a centrally located and heterogeneous brain structure that plays a critical role in various sensory, motor, and cognitive processes. However, visualizing the individual subnuclei of the thalamus using conventional MRI techniques is challenging. This difficulty has posed obstacles in targeting specific subnuclei for clinical interventions such as deep brain stimulation (DBS). In this paper, we present DiMANI, a novel method for directly visualizing the thalamic subnuclei using diffusion MRI (dMRI). The DiMANI contrast is computed by averaging, voxelwise, diffusion-weighted volumes enabling the direct distinction of thalamic subnuclei in individuals. We evaluated the reproducibility of DiMANI through multiple approaches. First, we utilized a unique dataset comprising 8 scans of a single participant collected over a 3-year period. Secondly, we quantitatively assessed manual segmentations of thalamic subnuclei for both intra-rater and inter-rater reliability. Thirdly, we qualitatively correlated DiMANI imaging data from several patients with Essential Tremor with the localization of implanted DBS electrodes and clinical observations. Lastly, we demonstrated that DiMANI can provide similar features at 3T and 7T MRI, using varying numbers of diffusion directions. Our results establish that DiMANI is a reproducible and clinically relevant method to directly visualize thalamic subnuclei. This has significant implications for the development of new DBS targets and the optimization of DBS therapy.

Neurite orientation dispersion and density imaging quantifies microstructural impairment in the thalamus and its connectivity in amyotrophic lateral sclerosis

AIMS

To evaluate microstructural impairment in the thalamus and thalamocortical connectivity using neurite orientation dispersion and density imaging (NODDI) in amyotrophic lateral sclerosis (ALS).

METHODS

This study included 47 healthy controls and 43 ALS patients, whose structural and diffusion-weighted data were collected. We used state-of-the-art parallel transport tractography to identify thalamocortical pathways in individual spaces. Thalamus was then parcellated into six subregions based on its connectivity pattern with the priori defined cortical (i.e., prefrontal/motor/somatosensory/temporal/posterior-parietal/occipital) regions. For each of the thalamic and cortical subregions and thalamo-cortical tracts, we compared the following NODDI metrics between groups: orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (ISO). We also used these metrics to conduct receiver operating characteristic curve (ROC) analyses and Spearman correlation.

RESULTS

In ALS patients, we found decreased ODI and increased ISO in the thalamic subregion connecting the left motor cortex and other extramotor (e.g., somatosensory and occipital) cortex (Bonferroni-corrected p < 0.05). NDI decreased in the bilateral thalamo-motor and thalamo-somatosensory tracts and in the right thalamo-posterior-parietal and thalamo-occipital tracts (Bonferroni-corrected p < 0.05). NDI reduction in the bilateral thalamo-motor tract (p = 0.017 and 0.009) and left thalamo-somatosensory tract (p = 0.029) was correlated with disease severity. In thalamo-cortical tracts, NDI yielded a higher effect size during between-group comparisons and a greater area under ROC (p < 0.05) compared with conventional diffusion tensor imaging metrics.

CONCLUSIONS

Microstructural impairment in the thalamus and thalamocortical connectivity is the hallmark of ALS. NODDI improved the detection of disrupted thalamo-cortical connectivity in ALS.

Functional thalamocortical connectivity development and alterations in preterm infants during the neonatal period

The thalamus is one of the most commonly affected brain regions in preterm infants, particularly in infants with white matter lesions (WML). The aim of this paper is to explore the development and alterations of the functional thalamocortical connectivity in preterm infants with and without punctate white matter lesions (PWMLs) during the period before term equivalent age (TEA). In this study, twenty-two normal preterm infants (NP), twenty-two preterm infants with PWMLs and thirty-one full-term control infants (FT) were enrolled. Thalamus parcellation was performed based on partial correlation between the thalamus and seven well-recognized infant networks obtained from independent component analysis (ICA), and thalamocortical connectivity was further reconstructed between the defined thalamus clusters and the whole brain. Thalamo-salience (SA) and thalamo-sensorimotor (SM) connectivity were predominantly identified, while other types of thalamocortical connectivity remained largely limited during the neonatal period. Both preterm groups exhibited prominent development in thalamo-SA and thalamo-SM connectivity during this period. Compared with NP infants, PWML infants demonstrated increased connectivity in the parietal area in thalamo-SA connectivity but no significant differences in thalamo-SM connectivity. Our results reveal that compared with NP infants, PWML infants exhibit slightly altered thalamo-SA connectivity, and this alteration is deduced to be functional compensations for inefficient thalamocortical processing due to PWMLs.

Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors

Segmentation of the thalamus and thalamic nuclei is useful to quantify volumetric changes from neurodegenerative diseases. Most thalamus segmentation algorithms only use T1-weighted magnetic resonance images and current thalamic parcellation methods require manual interaction. Smaller nuclei, such as the lateral and medial geniculates, are challenging to locate due to their small size. We propose an automated segmentation algorithm using a set of features derived from diffusion tensor image (DTI) and thalamic nuclei location priors. After extracting features, a hierarchical random forest classifier is trained to locate the thalamus. A second random forest classifies thalamus voxels as belonging to one of six thalamic nuclei classes. The proposed algorithm was tested using a leave-one-out cross validation scheme and compared with state-of-the-art algorithms. The proposed algorithm has a higher Dice score compared to other methods for the whole thalamus and several nuclei.

Automatic method for thalamus parcellation using multi-modal feature classification

Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease n brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived fea-fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features. Combining these multiple information sources allows us to identify discriminating dimensions and thus parcellate the thalamic nuclei. A hierarchical random forest framework with a multidimensional feature per voxel, first distinguishes thalamus from background, and then separates each group of thalamic nuclei. Using a leave one out cross-validation on 12 subjects we have a mean Dice score of 0.805 and 0.799 for the left and right thalami, respectively. We also report overlap for the thalamic nuclear groups.

A Novel Contrast for DTI Visualization for Thalamus Delineation

It has been recently shown that thalamic nuclei can be automatically segmented using diffusion tensor images (DTI) under the assumption that principal fiber orientation is similar within a given nucleus and distinct between adjacent nuclei. Validation of these methods, however, is challenging because manual delineation is hard to carry out due to the lack of images showing contrast between the nuclei. In this paper, we present a novel gray-scale contrast for DTI visualization that accentuates voxels in which the orientations of the principal eigenvectors are changing, thus providing an edge map for the delineation of some thalamic nuclei. The method uses the principal fiber orientation computed from the diffusion tensors computed at each voxel. The three-dimensional orientations of the principal eigenvectors are represented as five dimensional vectors and the spatial gradient (matrix) of these vectors provide information about spatial changes in tensor orientation. In particular, an edge map is created by computing the Frobenius norm of this gradient matrix. We show that this process reveals distinct edges between large nuclei in the thalamus, thereby making manual delineation of the thalamic nuclei possible. We briefly describe a protocol for the manual delineation of thalamic nuclei based on this edge map used in conjunction with a registered T1-weighted MR image, and present a preliminary multi-rater evaluation of the volumes of thalamic nuclei in several subjects.