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 Magnetic Resonance Images (MRIs) 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 Magnetic Resonance Imaging data (rs-fMRI). Two independent data, including 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 Labelling (AAL) template and used as features for machine learning analysis. After that, three analyses 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 within and cross datasets and the prediction of neurological recovery in CSM patients via support vector regression within and across datasets. The results showed that CSM patients could be successfully identified from healthy controls with high classification accuracies (84.2% for Dataset one, 95.2% for Dataset two, 73.0% for cross-site validation). Furthermore, the rs-FC offers potential biomarkers for presurgical evaluation and prediction of neurological recovery in CSM patients. Additionally, our results showed good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence towards the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique.