Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering
In this paper, a new method for fine brain functional parcellation based on resting-state functional magnetic resonance imaging (rs-fMRI) data was proposed. The method combines the t-distribution stochastic neighbor embedding (t-SNE) and automatic spectral clustering (ASC) algorithms. First, correla...
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Science Press
2021-09-01
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| Series: | Chinese Journal of Magnetic Resonance |
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| Online Access: | http://121.43.60.238/bpxzz/article/2021/1000-4556/20210310.shtml |
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doaj-c9a1eb080abd49edbf706577497305982021-09-14T07:14:41ZzhoScience PressChinese Journal of Magnetic Resonance1000-45562021-09-0138339240210.11938/cjmr20202876Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral ClusteringYing HU0Li-jia WANG1Sheng-dong NIE2Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaInstitute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaInstitute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaIn this paper, a new method for fine brain functional parcellation based on resting-state functional magnetic resonance imaging (rs-fMRI) data was proposed. The method combines the t-distribution stochastic neighbor embedding (t-SNE) and automatic spectral clustering (ASC) algorithms. First, correlation analyses are conducted between the time courses of the brain region to be parcellated and the whole brain. Second, t-SNE is used to extract the high-dimensional functional connectivity patterns. Last, the number of clusters is automatically determined by the ASC algorithm, and to divide the brain region of interest to generate the fine brain subregions. The results of simulated seed regions proved that the method proposed had higher accuracy than the commonly-used spectral clustering and spectral clustering with principal component analysis. Moreover, the method was successfully applied to parcellate the parahippocampal gyrus into 3 functional subregions in the left and right hemispheres. In conclusion, the algorithm combining t-SNE and ASC is an effective method for fine brain functional parcellation and construction of functional brain atlas.http://121.43.60.238/bpxzz/article/2021/1000-4556/20210310.shtmlresting-state fmrifunctional connectivityfunctional parcellationt-sneasc |
| collection |
DOAJ |
| language |
zho |
| format |
Article |
| sources |
DOAJ |
| author |
Ying HU Li-jia WANG Sheng-dong NIE |
| spellingShingle |
Ying HU Li-jia WANG Sheng-dong NIE Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering Chinese Journal of Magnetic Resonance resting-state fmri functional connectivity functional parcellation t-sne asc |
| author_facet |
Ying HU Li-jia WANG Sheng-dong NIE |
| author_sort |
Ying HU |
| title |
Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering |
| title_short |
Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering |
| title_full |
Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering |
| title_fullStr |
Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering |
| title_full_unstemmed |
Fine Brain Functional Parcellation Based on t-Distribution Stochastic Neighbor Embedding and Automatic Spectral Clustering |
| title_sort |
fine brain functional parcellation based on t-distribution stochastic neighbor embedding and automatic spectral clustering |
| publisher |
Science Press |
| series |
Chinese Journal of Magnetic Resonance |
| issn |
1000-4556 |
| publishDate |
2021-09-01 |
| description |
In this paper, a new method for fine brain functional parcellation based on resting-state functional magnetic resonance imaging (rs-fMRI) data was proposed. The method combines the t-distribution stochastic neighbor embedding (t-SNE) and automatic spectral clustering (ASC) algorithms. First, correlation analyses are conducted between the time courses of the brain region to be parcellated and the whole brain. Second, t-SNE is used to extract the high-dimensional functional connectivity patterns. Last, the number of clusters is automatically determined by the ASC algorithm, and to divide the brain region of interest to generate the fine brain subregions. The results of simulated seed regions proved that the method proposed had higher accuracy than the commonly-used spectral clustering and spectral clustering with principal component analysis. Moreover, the method was successfully applied to parcellate the parahippocampal gyrus into 3 functional subregions in the left and right hemispheres. In conclusion, the algorithm combining t-SNE and ASC is an effective method for fine brain functional parcellation and construction of functional brain atlas. |
| topic |
resting-state fmri functional connectivity functional parcellation t-sne asc |
| url |
http://121.43.60.238/bpxzz/article/2021/1000-4556/20210310.shtml |
| work_keys_str_mv |
AT yinghu finebrainfunctionalparcellationbasedontdistributionstochasticneighborembeddingandautomaticspectralclustering AT lijiawang finebrainfunctionalparcellationbasedontdistributionstochasticneighborembeddingandautomaticspectralclustering AT shengdongnie finebrainfunctionalparcellationbasedontdistributionstochasticneighborembeddingandautomaticspectralclustering |
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