A study protocol: resting-state functional magnetic resonance imaging in patients with knee osteoarthritis based on central hyperalgesia
Bibliographic record
Abstract
Abstract Background Pain is the most common symptom of knee osteoarthritis (KOA), with an incidence of 36.8–60.7%, thereby making it a primary cause that impacts patients’ quality of life and forces them to seek medical treatment. However, the KOA pain mechanisms are complex. The resulting joint degeneration provides stimuli to the central nervous system, thus, initiating several plastic changes under pain stimulation. Hence, nerve function changes enhance the responsiveness of neurons to normal or subliminal afferents, resulting in central sensitization. The development of chronic pain is closely related to the reorganization of brain structure and function. However, recent imaging technologies like resting-state functional magnetic resonance imaging (rs-fMRI) can inhibit the non-specific signals caused by cerebrospinal fluid fluctuations better and detect spontaneous human neural activity with accuracy and sensitivity. Therefore, we intend to explore the characteristics of spontaneous neural activity in KOA patients by utilizing rs-fMRI technology in combination with the changes in clinical-related variables. Our findings might help in revealing the neuropathological mechanism of KOA pain from the perspective of central pain sensitization. Methods Being a cross-sectional study, it will include all KOA patients who will be visiting the Joint Diagnosis and Treatment Center of Gansu Provincial Hospital from September 2023 to September 2024 and healthy volunteers with matching gender, age, and education levels as healthy controls. The clinical data, Central Sensitization Scale (CSI), Visual Analogue Scale (VAS), Western Ontario McMaster University Osteoarthritis Index (WOMAC), and radiological indicators of the two groups will be collected. After processing rs-fMRI scan results by image data processing, the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) will be calculated for both groups. Based on the variance analysis results, the abnormal brain regions will be superimposed as regions of interest (ROI) for assessing whole-brain functional connectivity (FC). Pearson’s correlation analysis will be employed for analyzing the correlation between the fALFF and FC values of abnormal brain regions as well as the clinical data, rating scales, and radiological indicators of KOA patients. Discussion We will use rs-fMRI technology to analyze the abnormal brain function patterns in KOA patients and imaging data to reveal the specific central pain sensitization mechanisms in KOA. Thus, this study aims to provide reliable and comprehensive evidence for clinical practice and determine a reasonable intervention plan for effectively reducing the discomfort and pain of such patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".