The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach
Background. Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white ma...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2022/1478048 |
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| author | Huibin Ma Zhou Xie Lina Huang Yanyan Gao Linlin Zhan Su Hu Jiaxi Zhang Qingguo Ding |
| author_facet | Huibin Ma Zhou Xie Lina Huang Yanyan Gao Linlin Zhan Su Hu Jiaxi Zhang Qingguo Ding |
| author_sort | Huibin Ma |
| collection | DOAJ |
| description | Background. Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA. Methods. We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm. Results. Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87. Conclusion. The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function. |
| format | Article |
| id | doaj-art-4a4d74a938e0481e8bd2639f141924e9 |
| institution | Kabale University |
| issn | 1687-5443 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-4a4d74a938e0481e8bd2639f141924e92025-02-03T06:12:25ZengWileyNeural Plasticity1687-54432022-01-01202210.1155/2022/1478048The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning ApproachHuibin Ma0Zhou Xie1Lina Huang2Yanyan Gao3Linlin Zhan4Su Hu5Jiaxi Zhang6Qingguo Ding7School of Information and Electronics TechnologySchool of Information and Electronics TechnologyDepartment of RadiologySchool of Teacher EducationFaculty of Western LanguagesSchool of Teacher EducationSchool of Teacher EducationDepartment of RadiologyBackground. Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA. Methods. We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm. Results. Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87. Conclusion. The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function.http://dx.doi.org/10.1155/2022/1478048 |
| spellingShingle | Huibin Ma Zhou Xie Lina Huang Yanyan Gao Linlin Zhan Su Hu Jiaxi Zhang Qingguo Ding The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach Neural Plasticity |
| title | The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach |
| title_full | The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach |
| title_fullStr | The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach |
| title_full_unstemmed | The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach |
| title_short | The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach |
| title_sort | white matter functional abnormalities in patients with transient ischemic attack a reinforcement learning approach |
| url | http://dx.doi.org/10.1155/2022/1478048 |
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