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The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Neuroimaging research has revealed significant changes in brain structure and function in patients with cervical spondylotic myelopathy(CSM). The thalamus plays a crucial role in this process, although its mechanisms of action remain incompletely understood. This study aimed to investigate whether spinal cord compression leads to alterations in the functional connectivity between the thalamus and the cerebral cortex, and to determine if such changes are associated with structural and functional remodeling of the brain in patients with CSM, and to identify potential neuroimaging biomarkers for classification. The study included 40 patients with CSM and 34 healthy controls(HCs) who underwent resting-state functional magnetic resonance imaging(fMRI) and structural MRI scans. Brain structural and functional metrics were quantified using functional connectivity(FC), fractional amplitude of low-frequency fluctuations(fALFF), surface-based morphometry(SBM), and independent component analysis(ICA) based on functional and structural MRI. Patients with CSM exhibited significantly reduced fALFF in the bilateral lateral lingual gyrus, bilateral calcarine fissure, left precentral gyrus and postcentral gyrus, left middle and superior occipital gyrus, left superior marginal gyrus, left inferior parietal gyrus, and right Rolandic operculum. ICA results revealed weakened functional connectivity between the sensorimotor network (SMN) and the left and right frontoparietal network(FPN), and lateral visual network (lVN), along with decreased connectivity between lVN and rFPN, and increased connectivity between lFPN and rFPN. Patients with CSM also had decreased sulcus depth in the bilateral insula, left precentral and postcentral gyrus, and right lingual gyrus and calcarine fissure. Furthermore, cervical spondylotic myelopathy patients showed decreased functional connectivity between the left ventral posterolateral nucleus (VPL) of the thalamus and the right middle occipital gyrus (MOG). Finally,multimodal neuroimaging with support vector machine(SVM) classified patients with CSM and healthy controls with 86.00% accuracy. Our study revealed that the decrease in functional connectivity between the thalamus and cortex mediated by spinal cord compression leads to structural and functional reorganization of the cortex. Features based on neuroimaging markers have the potential to become neuroimaging biomarkers for CSM.