Spatiotemporal Developmental Gradient of Thalamic Morphology, Microstructure, and Connectivity fromthe Third Trimester to Early Infancy

Abstract

Thalamus is a critical component of the limbic system that is extensively involved in both basic and high-order brain functions. However, how the thalamic structure and function develops at macroscopic and microscopic scales during the perinatal period development is not yet well characterized. Here, we used multishell high-angular resolution diffusion MRI of 144 preterm-born and full-term infants in both sexes scanned at 32-44 postmenstrual weeks (PMWs) from the Developing Human Connectome Project database to investigate the thalamic development in morphology, microstructure, associated connectivity, and subnucleus division. We found evident anatomic expansion and linear increases of fiber integrity in the lateral side of thalamus compared with the medial part. The tractography results indicated that thalamic connection to the frontal cortex developed later than the other thalamocortical connections (parieto-occipital, motor, somatosensory, and temporal). Using a connectivity-based segmentation strategy, we revealed that functional partitions of thalamic subdivisions were formed at 32 PMWs or earlier, and the partition developed toward the adult pattern in a lateral-to-medial pattern. Collectively, these findings revealed faster development of the lateral thalamus than the central part as well as a posterior-to-anterior developmental gradient of thalamocortical connectivity from the third trimester to early infancy.SIGNIFICANCE STATEMENT This is the first study that characterizes the spatiotemporal developmental pattern of thalamus during the third trimester to early infancy. We found that thalamus develops in a lateral-to-medial pattern for both thalamic microstructures and subdivisions; and thalamocortical connectivity develops in a posterior-to-anterior gradient that thalamofrontal connectivity appears later than the other thalamocortical connections. These findings may enrich our understanding of the developmental principles of thalamus and provide references for the atypical brain growth in neurodevelopmental disorders.

Keywords: diffusion MRI; early development; microstructure; subdivisions; thalamocortical connectivity; thalamus.

Figure 1.

Flowchart for the subject exclusion procedure used in the present study.

Figure 2.

Morphologic development of the bilateral thalamus during 32–44 PMWs relative to the thalamus of 40 PMWs. A, Visualization of the thalamus in the 40 PMWs template. B, The extent of morphologic deformation of the thalamus at each PMW relative to the 40 PMWs template, as quantified by the logarithm of the J value of the transformation matrices. J > 0 indicate a morphologic expansion compared with the reference image and values < 0 indicate opposite changes. C, Average deformation of left and right thalamus at each PMW. No significant laterality in deformation measurement was found during development (p values > 0.05, paired t test, FWE corrected).

Figure 3.

Developmental changes of the macrostructural and microstructural features of bilateral thalamus during 32–44 PMWs, including volume, FD, FA, MD, AD, and RD. These features were calculated in native dMRI space. Thalamic volume, FD, and FA significantly increased with PMA (r values > 0.39, p values < 0.0001), whereas the diffusivity indices (i.e., MD, AD, and RD) showed opposite trend (r values < −0.59, p values < 0.0001). The asymmetry of these metrics did not significantly correlate with PMA (last row). Note that an asymmetry index >0 indicates higher values in the left, and an index <0 indicates the opposite.

Figure 4.

Correlation (in t-values) maps between PMA and the thalamic microstructural features, and the corresponding FOD maps in axial view at several slice locations. The correlation was calculated by registering all infant images to a population template space. FA, FD, and FC showed significant positive correlations with PMA, whereas diffusivity metrics showed negative correlations (p values < 0.05, FWE corrected). The t-values of the correlations were shown in the fixels/voxels with statistical significance.

Figure 5.

Development of thalamocortical connectivity during 32–44 PMWs. A, Visualization of the thalamocortical fiber tracts at each PMW for the left and right hemispheres. B, The ratio of streamlines between the thalamus and individual cortices (i.e., frontal, parietal, temporal, and occipital). The ratio was defined as the number of streamlines connected to each cortex divided by the total number of thalamocortical streamlines. C, Density of thalamocortical fiber tracts in the thalamus. Note: results shown in this figure were computed based on the group template of each PMW.

Figure 6.

Development of thalamic subdivisions based on their connections to five cortical regions. A, The thalamic subdivisions at each PMW were identified via a connectivity-based approach. The thalamic connectivity atlas of adults in MNI space was also given as a reference. B, The five cortical seed regions that were used for thalamic nuclei segmentation, including frontal, temporal, motor, somatosensory, and parietal–occipital cortices. C, Number of voxels within each thalamic subdivision during development. Note: results shown in this figure were computed based on the group template at each PMW.

Figure 7.

Comparisons of microstructures and subdivisions of the thalamus, and thalamocortical connectivity among PB-AB, PB-TEA, and TB infants. A, Between-group differences in FA, FD, FC (log), MD, AD, and RD. Compared with PB-AB, PB-TEA showed significant increases of FA, FD, and FC (paired t test, p values < 0.05, FWE corrected), and significant decreases of diffusivity measures (paired t test, p values < 0.05, FWE corrected). No significant difference was found in these metrics between PB-TEA and TB (ANCOVA, p values > 0.05). No comparison was made between PB-AB and TB cohorts. B, Visualization of the thalamocortical fiber tracts in the three groups for the left and right hemispheres. C, The thalamic subdivisions in each group that were identified via a connectivity-based approach. Note: results shown in B and C were computed based on the FOD template of each group.