The brainstem and basal ganglia are important in the pathophysiology of Parkinson's disease (PD). Reliable and sensitive detection of neural activity changes in these regions should be helpful in scientific and clinical research on PD. In this study, we used resting state functional MRI and amplitude of low frequency fluctuation (ALFF) methods to examine spontaneous neural activity in 109 patients with PD. We examined activity in two frequency bands, slow-4 (between 0.027 and 0.073 Hz) and slow-5 (0.010-0.027 Hz). Patients had decreased ALFF in the striatum and increased ALFF in the midbrain, and changes were more significant in slow-4. Additionally, changes in slow-4 in both basal ganglia and midbrain correlated with the severity of the parkinsonism. The ALFF in the caudate nucleus positively correlated with the dose of levodopa, while the ALFF in the putamen negatively correlated with the disease duration in both slow-4 and slow-5 bands. In addition, the ALFF in the rostral supplementary motor area negatively correlated with bradykinesia subscale scores. Our findings show that with a large cohort of patients and distinguishing frequency bands, neural modulations in the brainstem and striatum in PD can be detected and may have clinical relevance. The physiological interpretation of these changes needs to be determined.

The chronic pain of knee osteoarthritis in the elderly is investigated in detail in this paper, as well as the complexity of chronic pain utilising neuroimaging recognition techniques. Chronic pain in knee osteoarthritis (KOA) has a major effect on patients’ quality of life and functional activities; therefore, understanding the causes of KOA pain and the analgesic advantages of different therapies is important. In recent years, neuroimaging techniques have become increasingly important in basic and clinical pain research. Thanks to the application and development of neuroimaging techniques in the study of chronic pain in KOA, researchers have found that chronic pain in KOA contains both injury-receptive and neuropathic pain components. The neuropathic pain mechanism that causes KOA pain is complicated, and it may be produced by peripheral or central sensitization, but it has not gotten enough attention in clinical practice, and there is no agreement on how to treat combination neuropathic pain KOA. As a result, using neuroimaging techniques such as magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS), this review examines the changes in brain pathophysiology-related regions caused by KOA pain, compares the latest results in pain assessment and prediction, and clarifies the central brain analgesic mechanistic. The capsule network model is introduced in this paper from the perspective of deep learning network structure to construct an information-complete and reversible image low-level feature bridge using isotropic representation, predict the corresponding capsule features from MRI voxel responses, and then, complete the accurate reconstruction of simple images using inverse transformation. The proposed model improves the structural similarity index by about 10%, improves the reconstruction performance of low-level feature content in simple images by about 10%, and achieves feature interpretation and analysis of low-level visual cortical fMRI voxels by visualising capsule features, according to the experimental results.

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