A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images
Patients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and W...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Behavioural Neurology |
| Online Access: | http://dx.doi.org/10.1155/2016/7849526 |
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| author | Wen-Lin Chu Min-Wei Huang Bo-Lin Jian Chih-Yao Hsu Kuo-Sheng Cheng |
| author_facet | Wen-Lin Chu Min-Wei Huang Bo-Lin Jian Chih-Yao Hsu Kuo-Sheng Cheng |
| author_sort | Wen-Lin Chu |
| collection | DOAJ |
| description | Patients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and WMV and incorporated the Wisconsin card sorting test (WCST) and the positive and negative syndrome scale (PANSS) to examine the correlation of obtained brain characteristics. We also used PANSS score to classify schizophrenic patients into acute and subacute cases, to analyze the brain structure differences. Finally, we used brain structure images and the error rate of the WCST as eigenvalues in support vector machine learning and classification. The results of this study showed that the frontal and temporal lobes of a normal brain are more apparent than those of a schizophrenia brain. The highest level of classification recognition reached 91.575%, indicating that the WCST error rate and characteristic changes in brain structure volume can be used to effectively distinguish schizophrenia and normal brains. Similarly, this result confirmed that the WCST and brain structure volume are correlated with the differences between schizophrenia and normal participants. |
| format | Article |
| id | doaj-art-a827dc65f2d04adbb4a43e5bea1b9aec |
| institution | OA Journals |
| issn | 0953-4180 1875-8584 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Behavioural Neurology |
| spelling | doaj-art-a827dc65f2d04adbb4a43e5bea1b9aec2025-08-20T02:20:45ZengWileyBehavioural Neurology0953-41801875-85842016-01-01201610.1155/2016/78495267849526A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance ImagesWen-Lin Chu0Min-Wei Huang1Bo-Lin Jian2Chih-Yao Hsu3Kuo-Sheng Cheng4Institute of Biomedical Engineering, National Cheng Kung University, Tainan 701, TaiwanInstitute of Biomedical Engineering, National Cheng Kung University, Tainan 701, TaiwanDepartment of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, TaiwanInstitute of Biomedical Engineering, National Cheng Kung University, Tainan 701, TaiwanInstitute of Biomedical Engineering, National Cheng Kung University, Tainan 701, TaiwanPatients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and WMV and incorporated the Wisconsin card sorting test (WCST) and the positive and negative syndrome scale (PANSS) to examine the correlation of obtained brain characteristics. We also used PANSS score to classify schizophrenic patients into acute and subacute cases, to analyze the brain structure differences. Finally, we used brain structure images and the error rate of the WCST as eigenvalues in support vector machine learning and classification. The results of this study showed that the frontal and temporal lobes of a normal brain are more apparent than those of a schizophrenia brain. The highest level of classification recognition reached 91.575%, indicating that the WCST error rate and characteristic changes in brain structure volume can be used to effectively distinguish schizophrenia and normal brains. Similarly, this result confirmed that the WCST and brain structure volume are correlated with the differences between schizophrenia and normal participants.http://dx.doi.org/10.1155/2016/7849526 |
| spellingShingle | Wen-Lin Chu Min-Wei Huang Bo-Lin Jian Chih-Yao Hsu Kuo-Sheng Cheng A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images Behavioural Neurology |
| title | A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images |
| title_full | A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images |
| title_fullStr | A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images |
| title_full_unstemmed | A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images |
| title_short | A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images |
| title_sort | correlative classification study of schizophrenic patients with results of clinical evaluation and structural magnetic resonance images |
| url | http://dx.doi.org/10.1155/2016/7849526 |
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