A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation
It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemisphe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10218354/ |
| _version_ | 1797688543872548864 |
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| author | Zhimin Shao Weibei Dou Di Ma Xiaoxue Zhai Quan Xu Yu Pan |
| author_facet | Zhimin Shao Weibei Dou Di Ma Xiaoxue Zhai Quan Xu Yu Pan |
| author_sort | Zhimin Shao |
| collection | DOAJ |
| description | It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in <inline-formula> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula>, revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans. |
| first_indexed | 2024-03-12T01:32:25Z |
| format | Article |
| id | doaj.art-99d6cda4b030425891b7a70f70a35a0c |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-12T01:32:25Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-99d6cda4b030425891b7a70f70a35a0c2023-09-11T23:00:07ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01313375338310.1109/TNSRE.2023.330547410218354A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted RehabilitationZhimin Shao0https://orcid.org/0000-0002-3078-0939Weibei Dou1https://orcid.org/0000-0001-8555-2776Di Ma2Xiaoxue Zhai3Quan Xu4Yu Pan5Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, ChinaDepartment of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, ChinaDepartment of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Beijing, ChinaDepartment of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Beijing, ChinaDepartment of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Beijing, ChinaDepartment of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Beijing, ChinaIt is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in <inline-formula> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula>, revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.https://ieeexplore.ieee.org/document/10218354/Brain-computer interfaceBCImachine learningbrain asymmetryMRI |
| spellingShingle | Zhimin Shao Weibei Dou Di Ma Xiaoxue Zhai Quan Xu Yu Pan A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation IEEE Transactions on Neural Systems and Rehabilitation Engineering Brain-computer interface BCI machine learning brain asymmetry MRI |
| title | A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation |
| title_full | A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation |
| title_fullStr | A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation |
| title_full_unstemmed | A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation |
| title_short | A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation |
| title_sort | novel neurorehabilitation prognosis prediction modeling on separated left right hemiplegia based on brain computer interfaces assisted rehabilitation |
| topic | Brain-computer interface BCI machine learning brain asymmetry MRI |
| url | https://ieeexplore.ieee.org/document/10218354/ |
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