Prior-guided individualized thalamic parcellation based on local diffusion characteristics

Abstract

Comprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior-guided parcellation (PG-par) method to achieve robust individualized thalamic parcellation based on a central-boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high-quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high-probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test-retest assessment, the proposed prior-guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG-par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior-guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior-guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.

Keywords: diffusion MRI; individualized brain mapping; machine learning; orientation distribution function; thalamic parcellation.

FIGURE 1

Pipeline of the prior‐guided individualized thalamic parcellation method. (a) Pipeline of probabilistic atlas construction. Local diffusion characteristics are extracted in the individual space to calculate the similarity matrix for clustering. Then, the clustering results of 100 unrelated subjects are registered to the MNI space to construct the probabilistic atlas of the thalamic nuclei. (b) Pipeline of prior‐guidance extraction. Areas with high probability values in the probabilistic atlas are extracted and registered to the individual space as the individual prior parcellation to provide prior guidance for individualized parcellation. (c) Pipeline of prior‐guided individualized thalamic parcellation. Individual prior parcellation is used to train a classification model to predict the cluster labels for unlabeled thalamic voxels. Merging the prior parcellation with the unlabeled parcellation to construct the individualized parcellation.

FIGURE 2

Probabilistic atlas of the thalamus based on local diffusion characteristics. (a) Local maxima in the Dice coefficient were observed in both the left and right hemispheres when the number of clusters was set to 7, and the Tpd coefficient approached zero. (b) The voxel count for each thalamic nucleus progressively decreased with increasing probability threshold. (c) With the rise in probability threshold, the boundary voxels of thalamic nuclei were gradually eliminated, while the central area was retained.

FIGURE 3

Results of the test–retest examination for the prior‐guided individualized thalamic parcellation method. Consistency between test and retest for the prior‐guided individualized thalamic parcellations. The intrasubject consistency is significantly higher than the intersubject one.

FIGURE 4

Clustering goodness of the individualized thalamic parcellation methods. (a–d) Silhouette coefficients of the GA‐reg, IC‐par, and PG‐par thalamic parcellations on the Human Connectome Project (HCP) 7T 100, HCP 3T 100, MASiVar, and NC10 datasets. The silhouette coefficients of the PG‐par thalamic parcellations are significantly higher than those of the GA‐reg and IC‐par on each dataset. GA‐reg, group atlas registration; IC‐par, individual clustering parcellation; PG‐par, prior‐guided parcellation.

FIGURE 5

Comparison of accuracy of methods compared to THOMAS structural segmentation. (a–d) Dice with THOMAS of the GA‐reg, IC‐par, and PG‐par thalamic parcellations on the Human Connectome Project (HCP) 7T 100, HCP 3T 100, MASiVar, and NC10 datasets. PG‐par provides significantly higher consistency with THOMAS than GA‐reg and IC‐par on each dataset. GA‐reg, group atlas registration; IC‐par, individual clustering parcellation; PG‐par, prior‐guided parcellation.

FIGURE 6

Histological comparison of the individualized thalamic parcellation methods. (a) The prior‐guided parcellation (PG‐par) exhibits a parcellation pattern that is more likely to align with the histological atlas (pointed by the orange arrow). (b) All three methods yield similar parcellation patterns to the histological atlas.