Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study
The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delin...
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| Published in | Frontiers in neuroscience Vol. 12; p. 270 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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26.04.2018
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| ISSN | 1662-453X 1662-4548 1662-453X |
| DOI | 10.3389/fnins.2018.00270 |
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| Abstract | The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions
even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala
. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions. |
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| AbstractList | The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions. The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it’s difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions. The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo. In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions. The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala . In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions. |
| Author | Zhuo, Yan Zhou, Ke Wang, Bo Cheng, Hewei Zuo, Zhentao Cong, Fei Chen, Lin Xue, Rong Zhang, Xianchang Fan, Yong |
| AuthorAffiliation | 3 Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications , Chongqing , China 4 College of Psychology and Sociology, Shenzhen University , Shenzhen , China 2 College of Life Sciences, University of Chinese Academy of Sciences , Beijing , China 7 Beijing Institute for Brain Disorders , Beijing , China 8 Department of Radiology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA , United States 1 State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences , Beijing , China 5 Center for Language and Brain, Shenzhen Institute of Neuroscience , Shenzhen , China 6 Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University , Shenzhen , China |
| AuthorAffiliation_xml | – name: 1 State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences , Beijing , China – name: 6 Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University , Shenzhen , China – name: 2 College of Life Sciences, University of Chinese Academy of Sciences , Beijing , China – name: 4 College of Psychology and Sociology, Shenzhen University , Shenzhen , China – name: 5 Center for Language and Brain, Shenzhen Institute of Neuroscience , Shenzhen , China – name: 7 Beijing Institute for Brain Disorders , Beijing , China – name: 3 Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications , Chongqing , China – name: 8 Department of Radiology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA , United States |
| Author_xml | – sequence: 1 givenname: Xianchang surname: Zhang fullname: Zhang, Xianchang – sequence: 2 givenname: Hewei surname: Cheng fullname: Cheng, Hewei – sequence: 3 givenname: Zhentao surname: Zuo fullname: Zuo, Zhentao – sequence: 4 givenname: Ke surname: Zhou fullname: Zhou, Ke – sequence: 5 givenname: Fei surname: Cong fullname: Cong, Fei – sequence: 6 givenname: Bo surname: Wang fullname: Wang, Bo – sequence: 7 givenname: Yan surname: Zhuo fullname: Zhuo, Yan – sequence: 8 givenname: Lin surname: Chen fullname: Chen, Lin – sequence: 9 givenname: Rong surname: Xue fullname: Xue, Rong – sequence: 10 givenname: Yong surname: Fan fullname: Fan, Yong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29755313$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_neubiorev_2023_105245 crossref_primary_10_1117_1_NPh_6_4_045014 crossref_primary_10_1007_s00429_020_02046_1 crossref_primary_10_1016_j_neuroimage_2022_119333 crossref_primary_10_1097_ACO_0000000000001176 crossref_primary_10_1007_s11682_023_00782_6 crossref_primary_10_1016_j_neuron_2021_09_001 crossref_primary_10_1038_s41598_020_73328_1 |
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| Keywords | parcellation amygdala resting-state fMRI 7T semi-supervised clustering |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Co-first author. This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience Reviewed by: Jiaojian Wang, University of Electronic Science and Technology of China, China; Hyunjin Park, Sungkyunkwan University, South Korea These authors have contributed equally to this work. Edited by: John Ashburner, University College London, United Kingdom |
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| SubjectTerms | Algorithms Alzheimer's disease Amygdala Animal cognition Brain Brain architecture Brain mapping Clustering Functional magnetic resonance imaging Laboratories Medical imaging Mental disorders Methods Neural networks Neuroscience NMR Nuclear magnetic resonance parcellation resting-state fMRI semi-supervised clustering Studies |
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| Title | Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study |
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