{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gao C"],"funding":["National Key R&D Program of China","Beijing Advanced Discipline Fund","Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project of China","Strategic Priority Research Program of Chinese Academy of Sciences","National Natural Science Foundation of China","Youth Innovation Promotion Association","National Key Research and Development Program of China"],"pagination":["e26646"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10910286"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["45(4)"],"pubmed_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."],"journal":["Human brain mapping"],"pubmed_title":["Prior-guided individualized thalamic parcellation based on local diffusion characteristics."],"pmcid":["PMC10910286"],"funding_grant_id":["2017YFA0105203","2021ZD0200203","91432302","31620103905","XDB32030200","82072099"],"pubmed_authors":["Li G","Chu C","Hou Z","Ma L","Madsen KH","Wang C","Fan L","Wang Y","Gao C","Xie S","Wu X"],"additional_accession":[]},"is_claimable":false,"name":"Prior-guided individualized thalamic parcellation based on local diffusion characteristics.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2025-04-05T11:38:36.125Z","creation":"2025-04-05T11:38:36.125Z"},"accession":"S-EPMC10910286","cross_references":{"pubmed":["38433705"],"doi":["10.1002/hbm.26646"]}}