Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study
Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2021.711880/full |
| _version_ | 1818738137261670400 |
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| author | Qian Su Rui Zhao ShuoWen Wang HaoYang Tu Xing Guo Fan Yang |
| author_facet | Qian Su Rui Zhao ShuoWen Wang HaoYang Tu Xing Guo Fan Yang |
| author_sort | Qian Su |
| collection | DOAJ |
| description | Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure. The FC was calculated from the automated anatomical labeling (AAL) template and used as features for machine learning analysis. After that, three analyses were used, namely, the classification of CSM patients from healthy adults using the support vector machine (SVM) within and across datasets, the prediction of preoperative neurological function in CSM patients via support vector regression (SVR) within and across datasets, and the prediction of neurological recovery in CSM patients via SVR within and across datasets. The results showed that CSM patients could be successfully identified from HCs with high classification accuracies (84.2% for dataset 1, 95.2% for dataset 2, and 73.0% for cross-site validation). Furthermore, the rs-FC combined with SVR could successfully predict the neurological recovery in CSM patients. Additionally, our results from cross-site validation analyses exhibited good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence toward the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique. |
| first_indexed | 2024-12-18T01:04:09Z |
| format | Article |
| id | doaj.art-89dd3bd42edc41138f322087329c9546 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-18T01:04:09Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-89dd3bd42edc41138f322087329c95462022-12-21T21:26:17ZengFrontiers Media S.A.Frontiers in Neurology1664-22952021-10-011210.3389/fneur.2021.711880711880Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning StudyQian Su0Rui Zhao1ShuoWen Wang2HaoYang Tu3Xing Guo4Fan Yang5Tianjin Key Laboratory of Cancer Prevention and Therapy, Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for China, Tianjin, ChinaDepartment of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, ChinaSchool of Basic Medical Sciences, Tianjin Medical University, Tianjin, ChinaSchool and Hospital of Stomatology, Tianjin Medical University, Tianjin, ChinaDepartment of Orthopedics Surgery, Tianjin Medical University General Hospital, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, ChinaCurrently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure. The FC was calculated from the automated anatomical labeling (AAL) template and used as features for machine learning analysis. After that, three analyses were used, namely, the classification of CSM patients from healthy adults using the support vector machine (SVM) within and across datasets, the prediction of preoperative neurological function in CSM patients via support vector regression (SVR) within and across datasets, and the prediction of neurological recovery in CSM patients via SVR within and across datasets. The results showed that CSM patients could be successfully identified from HCs with high classification accuracies (84.2% for dataset 1, 95.2% for dataset 2, and 73.0% for cross-site validation). Furthermore, the rs-FC combined with SVR could successfully predict the neurological recovery in CSM patients. Additionally, our results from cross-site validation analyses exhibited good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence toward the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique.https://www.frontiersin.org/articles/10.3389/fneur.2021.711880/fullrs-fMRImachine learningcervical spondylotic myelopathysupport vector machinefunctional connectivity |
| spellingShingle | Qian Su Rui Zhao ShuoWen Wang HaoYang Tu Xing Guo Fan Yang Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study Frontiers in Neurology rs-fMRI machine learning cervical spondylotic myelopathy support vector machine functional connectivity |
| title | Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study |
| title_full | Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study |
| title_fullStr | Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study |
| title_full_unstemmed | Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study |
| title_short | Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study |
| title_sort | identification and therapeutic outcome prediction of cervical spondylotic myelopathy based on the functional connectivity from resting state functional mri data a preliminary machine learning study |
| topic | rs-fMRI machine learning cervical spondylotic myelopathy support vector machine functional connectivity |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2021.711880/full |
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