Cervical spondylotic myelopathy (CSM) is a common spinal cord dysfunction disease with complex symptoms in clinical presentation. Resting state fMRI (rsfMRI) has been introduced to study the mechanism of neural development of CSM. However, most of those studies focused on intrinsic functional connectivity rather than intrinsic regional neural activity level which is also frequently analyzed in rsfMRI studies. Thus, this study aims to explore whether the level of neural activity changes on the myelopathic cervical cord and evaluate the possible relationship between this change and clinical symptoms through amplitude of low frequency fluctuation (ALFF). Eighteen CSM patients and twenty five healthy subjects participated in rsfMRI scanning. ALFF was investigated on each patient and subject. The results suggested that ALFF values were higher in the CSM patients at all cervical segments, compared to the healthy controls. The severity of myelopathy was associated with the increase of ALFF. This finding would enrich our understanding on the neural development mechanism of CSM.

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Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy-five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one-year follow-up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k-nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF-SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10-fold cross-validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF-SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF-SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM. KEYWORDS artificial intelligence (AI), cervical myelopathy (CM), diffusion tensor imaging (DTI), prognosis 1 | INTRODUCTION Cervical myelopathy (CM) is a common spinal cord dysfunction that affects millions of people worldwide. 1 Currently, surgical intervention is considered to be the most immediate way to provide relief from the spinal cord compression and to promote neurological recovery when coupled with active post-operative neural rehabilitation. 2,3 An accurate prognosis for the surgery would provide useful assistance to surgeons, helping them to decide on the most appropriate treatment option, manage patient expectations, and plan postoperative rehabilitation. 4 Several prognosticators of surgical outcomes have already been proposed. 5,6 However, the value of these prognosticators remains controversial, and researchers continue to search for more effective prognostic methods.Diffusion tensor imaging (DTI) has been demonstrated to have prognostic value for CM. [7][8][9] In CM, demyelination is believed to be the main pathological change in the spinal cord, and this demyelination changes the organization of nerve fiber bundles, resulting in changes to DTI-related signals. Based on this principle, researchers have used DTI to study CM. Also, it has been reported that DTI has great potential for distinguishing CM patients from healthy subjects and for predicting the exact cervical levels causing symptoms. [10][11][12] The pathological status of the spinal cord,

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1421-1431.

Some clinical studies have shown promising effects of transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) on pain relief. Nevertheless, a few studies reported no significant analgesic effects of tDCS, likely due to the complexity of clinical pain conditions. Human experimental pain models that utilize indices of pain in response to well-controlled noxious stimuli can avoid many confounds that are present in the clinical data. This study aimed to investigate the effects of high-definition tDCS (HD-tDCS) stimulation over M1 on sensitivity to experimental pain and assess whether these effects could be influenced by the pain-related cognitions and emotions. A randomized, double-blinded, crossover, and sham-controlled design was adopted. A total of 28 healthy participants received anodal, cathodal, or sham HD-tDCS over M1 (1 mA for 20 min) in different sessions, in which montage has the advantage of producing more focal stimulation. Using a cold pressor test, several indices reflecting the sensitivity to cold pain were measured immediately after HD-tDCS stimulation, such as cold pain threshold and tolerance and cold pain intensity and unpleasantness ratings. Results showed that only anodal HD-tDCS significantly increased cold pain threshold when compared with sham stimulation. Neither anodal nor cathodal HD-tDCS showed significant analgesic effects on cold pain tolerance, pain intensity, and unpleasantness ratings. Correlation analysis revealed that individuals that a had lower level of attentional bias to negative information benefited more from attenuating pain intensity rating induced by anodal HD-tDCS. Therefore, single-session anodal HD-tDCS modulates the sensory-discriminative aspect of pain perception as indexed by the increased pain threshold. In addition, the modulating effects of HD-tDCS on attenuating pain intensity to suprathreshold pain could be influenced by the participant’s negative attentional bias, which deserves to be taken into consideration in the clinical applications.