Diagnosis of Brain Diseases via Multi-Scale Time-Series Model
The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, thos...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-03-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.00197/full |
| _version_ | 1828391164659630080 |
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| author | Zehua Zhang Junhai Xu Jijun Tang Jijun Tang Quan Zou Fei Guo |
| author_facet | Zehua Zhang Junhai Xu Jijun Tang Jijun Tang Quan Zou Fei Guo |
| author_sort | Zehua Zhang |
| collection | DOAJ |
| description | The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks. |
| first_indexed | 2024-12-10T06:58:00Z |
| format | Article |
| id | doaj.art-a5e3918789634056bd51ef5822f402f4 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T06:58:00Z |
| publishDate | 2019-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a5e3918789634056bd51ef5822f402f42022-12-22T01:58:24ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-03-011310.3389/fnins.2019.00197448097Diagnosis of Brain Diseases via Multi-Scale Time-Series ModelZehua Zhang0Junhai Xu1Jijun Tang2Jijun Tang3Quan Zou4Fei Guo5School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaSchool of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaSchool of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United StatesInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, ChinaThe functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks.https://www.frontiersin.org/article/10.3389/fnins.2019.00197/fullfunctional magnetic resonance imagingmulti-scale time-seriesAlzheimer's diseasemajor depressive disorderfunctional connection |
| spellingShingle | Zehua Zhang Junhai Xu Jijun Tang Jijun Tang Quan Zou Fei Guo Diagnosis of Brain Diseases via Multi-Scale Time-Series Model Frontiers in Neuroscience functional magnetic resonance imaging multi-scale time-series Alzheimer's disease major depressive disorder functional connection |
| title | Diagnosis of Brain Diseases via Multi-Scale Time-Series Model |
| title_full | Diagnosis of Brain Diseases via Multi-Scale Time-Series Model |
| title_fullStr | Diagnosis of Brain Diseases via Multi-Scale Time-Series Model |
| title_full_unstemmed | Diagnosis of Brain Diseases via Multi-Scale Time-Series Model |
| title_short | Diagnosis of Brain Diseases via Multi-Scale Time-Series Model |
| title_sort | diagnosis of brain diseases via multi scale time series model |
| topic | functional magnetic resonance imaging multi-scale time-series Alzheimer's disease major depressive disorder functional connection |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.00197/full |
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