Objective: Cervical spondylotic myelopathy is regarded as a chronic, special incomplete spinal cord injury, so the sensory components transmitted to thalamus decreased after distal spinal cord injury, which lead the disturbance of thalamus-cortex circuits, which might explain the alterations of clinical function of cervical spondylotic myelopathy patients. However, for lack of effective methods to evaluate the disturbance circuits and how the relative mechanism adapt to the recovery of cervical spondylotic myelopathy patients after decompression. Therefore, this study aim to explore how the possible mechanism of thalamus-cortex circuits reorganization adapt to the recovery of clinical function. Methods: Regard thalamus as the interest area, we evaluate the brain functional connectivity within 43 pre-operative cervical spondylotic myelopathy patients, 21 post-operative (after 3 months) cervical spondylotic myelopathy patients and 43 healthy controls. Functional connectivity difference between pre-/post-operative cervical spondylotic myelopathy group and healthy controls group were obtained by two independent samples t-test, and difference between pre-operative cervical spondylotic myelopathy and post-operative cervical spondylotic myelopathy group were obtained by paired t-test. Clinical function was measured via Neck Disability Index and Japanese Orthopaedic Association scores. Furthermore, Pearson correlation were used to analyse the correlation between functional connectivity values and clinical scores. Results: Compared with healthy controls group, pre-operative cervical spondylotic myelopathy group showed increased functional connectivity between left thalamus and bilateral lingual gyrus/cuneus/right cerebellum posterior lobe (Voxel P-value <0.01, Cluster P-value <0.05, GRF corrected); post-operative cervical spondylotic myelopathy group manifested decreased functional connectivity between right thalamus and bilateral paracentral lobe/precentral gyrus but significantly increased between right thalamus and pons/superior temporal gyrus. In comparison with pre-operative cervical spondylotic myelopathy group, post-operative cervical spondylotic myelopathy group showed increased functional connectivity between bilateral thalamus and posterior cingulate lobe, angular gyrus, medial prefrontal, but significantly decreased functional connectivity between bilateral thalamus and paracentral lobe/precentral gyrus. The functional connectivity between left thalamus and bilateral lingual gyrus/cuneus/right cerebellum posterior lobe in pre-operative cervical spondylotic myelopathy group have a significantly positive correlation with sensory Japanese Orthopaedic Association scores (r = 0.568, P < 0.001). The functional connectivity between thalamus and paracentral lobe/precentral gyrus in post-operative cervical spondylotic myelopathy group have a significantly positive correlation with upper limb movement Japanese Orthopaedic Association scores (r = 0.448, P = 0.042). Conclusion: Pre- or post-operative cervical spondylotic myelopathy patients showed functional connectivity alteration between thalamus and cortex, which suggest adaptive changes may favor the preservation of cortical sensorimotor networks before and after cervical cord decompression, and supply the improvement of clinical function.
Background: Previous studies have shown gray matter(GM) abnormalities in the central nervous system at the group level, but this method is limited because it is based on single or cluster voxels. In contrast, machine learning makes full use of all available empirical information, including differences in brain images or behavioral data, to classify or predict data and ensure good generalization ability. This approach has potential use as a prediction tool at the individual level. We thus hypothesized that a multivariate pattern classification method may distinguish classic trigeminal neuralgia(CTN) patients from healthy controls(HC) based on gray matter volume(GMV).Methods: Resting-state fMRI scans in 24 CTN and 22 HC were processed to extract whole-brain GMV. Based on this feature dataset, take the method of Spearman, T-test, F-score, principal component analysis(PCA) respectively to reduce the feature dimension. Applying a linear support vector machine(SVM) algorithm to differentiate CTN and HC. And extract the features that survive each iteration. Pearson correlation analysis was then used to assess the correlations between the deci_value(the distance from the sample to the optimal hyperplane) and VAS scores and pain duration respectively in CTN patients.Results: Compared with other methods of feature dimension reduction, PCA has a higher ability to correctly classify an individual with an accuracy of 85%(AUC 0.9223; 91.67% sensitivity; 86.36% specificity, p<0.001). And the features that survive each iteration were concentrated in the region of the left anterior cingulate cortex(ACC_L), right superior frontal gyrus(SFG_R), and bilateral cerebellum inferior(CI). In the CTN group, deci_value was positively correlated with the VAS scores in PCA(r=0.42, P=0.041, two-tailed). While there was no difference between deci_value and VAS scores in F-score, T-test, Spearman(r=0.39,p=0.06/r=0.36,p=0.09/r=0.37,p=0.08 respectively). Also, we did not find significant correlations between the pain duration for CTN patients and deci_value among the four methods of feature dimension reduction(P > 0.05).Conclusions: This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN, and not only highlights the high accuracy of the PCA method, but also the role of ACC, SFG, and CI for classification.
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