Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness

Zheng, Weihao; Tan, Xiaoxiao; Liu, Tingting; Li, Xiaoxia; Gao, Jian; Hong, Lirong; Zhang, Xiaolin; Zhao, Zhiyong; Yu, Yamei; Zhang, Yi; Luo, Benyan; Wu, Dan

doi:10.1093/texcom/tgab024

Cited by 12 publications

(16 citation statements)

References 93 publications

(133 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…Compared to previous microstructural thalamic parcellation 25,46,47 , the 12-cluster thalamus atlas provides a more fine-grained parcellation pattern of thalamus nuclei based on a higher-order ODF model. And the 12-cluster thalamus atlas is significantly consistent with the thalamic anatomic structure and homologous between hemispheres.…”

Section: Discussionmentioning

confidence: 99%

“…Besides, the PG individualized thalamus atlas can capture some subtle thalamic structures while GR and CL cannot. Compared to previous microstructural thalamic parcellation 25,46,47 , the 12-cluster thalamus atlas provides a more fine-grained parcellation pattern of thalamus nuclei based on a higher-order ODF model.…”

Section: Individualized Thalamus Atlasmentioning

confidence: 99%

“…Guided by the high-probability group prior atlas, we developed an automatic individualized thalamus atlas construction pipeline based on a deep learning algorithm. Existing individualized thalamus atlases are constructed by either single-subject-based clustering 25,47 , white matter tractography quantification 24 , or multi-subject-based group atlas registration 66 . Compared to these approaches, our prior-guided individualization pipeline is the first attempt to construct the microstructural individualized thalamus atlas in the native space of the subject by combining group prior atlas and individual diffusion properties.…”

Section: Individualized Thalamus Atlasmentioning

confidence: 99%

“…Thanks to the HARDI techniques, the high-order ODF model can be estimated to depict detailed diffusion patterns of brain tissue with multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) 44 . The feasibility of constructing the thalamus atlas in a coarser granularity using ODF has been demonstrated in recent years 25,46,47 .…”

Section: Introductionmentioning

confidence: 99%

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Prior-guided Individualized Thalamic Parcellation based on Local Diffusion Characteristics using Deep Learning

Gao

Wang

et al. 2022

Preprint

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As a gateway for projections entering and exiting the cerebral cortex, the human thalamus processes information from sensory to cognition relevant to various neuropsychiatric disorders. It is composed of dozens of nuclei, which have been difficult to identify with clinical MR sequences. However, delineating thalamic nuclei accurately at an individual level is essential for precise neuromodulation treatment. Here, we not only identified the fine-grained thalamic nuclei using local diffusion properties in vivo but also employed a deep learning strategy to achieve highly reproducible individual-level parcellation. Using High-quality diffusion MRI (dMRI), we first constructed a fine-grained group thalamus atlas based on thalamic local diffusion features. Then, the high-probability core area of the group thalamus atlas was wrapped into the native space as prior guidance for individualized thalamus construction. Finally, we trained the semi-supervised multiple classification models to accurately construct the individualized thalamus atlas with single-subject local diffusion characteristics. Compared to group atlas registration and single-subject clustering strategies, our individualized thalamus atlas combines population commonality and individual specificity and is superior in depicting the individual thalamic nuclei boundaries. Besides, our atlas provides a more conspicuous capacity to capture the individual specificity of thalamic nuclei. Through the evaluation by 3.0T\7.0T and test-retest dMRI datasets, the proposed high-probability group prior guided individualized thalamus construction pipeline is robust and repeatable in different magnetic field strengths and scanning batches. In addition, the individual parcellation of the thalamic nuclei has a good correspondence with the histological atlas and captured both higher group consistency and inter-subjects variations, which could be a valuable solution for precision clinical treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Individualized Thalamus Atlasmentioning

confidence: 99%

Section: Individualized Thalamus Atlasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prior-guided Individualized Thalamic Parcellation based on Local Diffusion Characteristics using Deep Learning

Gao

Wang

et al. 2022

Preprint

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Preterm‐birth alters the development of nodal clustering and neural connection pattern in brain structural network at term‐equivalent age

Zheng

Wang

Liu

et al. 2023

Human Brain Mapping

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Preterm‐born neonates are prone to impaired neurodevelopment that may be associated with disrupted whole‐brain structural connectivity. The present study aimed to investigate the longitudinal developmental pattern of the structural network from preterm birth to term‐equivalent age (TEA), and identify how prematurity influences the network topological organization and properties of local brain regions. Multi‐shell diffusion‐weighted MRI of 28 preterm‐born scanned a short time after birth (PB‐AB) and at TEA (PB‐TEA), and 28 matched term‐born (TB) neonates in the Developing Human Connectome Project (dHCP) were used to construct structural networks through constrained spherical deconvolution tractography. Structural network development from preterm birth to TEA showed reduced shortest path length, clustering coefficient, and modularity, and more “connector” hubs linking disparate communities. Furthermore, compared with TB newborns, premature birth significantly altered the nodal properties (i.e., clustering coefficient, within‐module degree, and participation coefficient) in the limbic/paralimbic, default‐mode, and subcortical systems but not global topology at TEA, and we were able to distinguish the PB from TB neonates at TEA based on the nodal properties with 96.43% accuracy. Our findings demonstrated a topological reorganization of the structural network occurs during the perinatal period that may prioritize the optimization of global network organization to form a more efficient architecture; and local topology was more vulnerable to premature birth‐related factors than global organization of the structural network, which may underlie the impaired cognition and behavior in PB infants.

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Prior‐guided individualized thalamic parcellation based on local diffusion characteristics

Gao,

Wu,

Wang

et al. 2024

Human Brain Mapping

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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.

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Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness

Cited by 12 publications

References 93 publications

Prior-guided Individualized Thalamic Parcellation based on Local Diffusion Characteristics using Deep Learning

Prior-guided Individualized Thalamic Parcellation based on Local Diffusion Characteristics using Deep Learning

Preterm‐birth alters the development of nodal clustering and neural connection pattern in brain structural network at term‐equivalent age

Prior‐guided individualized thalamic parcellation based on local diffusion characteristics

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