Higher Brain Grey Matter Density in Mild to Moderate Chronic Low Back Pain Patients
Bibliographic record
Abstract
Abstract Chronic low back pain (CLBP) is the leading cause of disability worldwide. Structural abnormalities in the lumbar region rarely account for the pain symptoms observed. In contrast, the brain plays a central role in chronic pain, with several studies suggesting that chronic pain may result from the persistence of pain memories and/or the inability to extinguish these memories following an initial inciting injury. Previous research has reported a reduction in grey matter density (GMD) in CLBP patients, as measured by magnetic resonance imaging (MRI). Notably, lower GMD has been observed in patients with moderate to severe CLBP who are undergoing prescribed pharmacological treatments. However, it remains unclear whether these differences in GMD could be associated with a less severe condition. This study aimed to investigate whether GMD is altered in a cohort with mild to moderate CLBP symptoms, and who have not been prescribed pharmacological treatments. To achieve this, we acquired T1-weighted MRI scans from 25 healthy controls (HC) and 27 untreated individuals with mild to moderate CLBP. Scans were taken at baseline, 2 months, and 4 months after baseline. GMD analysis was carried out using the FMRIB Software. Our results consistently showed higher GMD in the CLBP group compared to HC. These findings suggest that the observed alterations in GMD may be related to the condition itself, and can occur even in patients with milder symptoms and better physical function, without the influence of opioids, anticonvulsants, or antidepressants. Given that our participants differ from those typically studied in the literature, our results imply that they may be in a distinct brain state relative to the condition. Further research is needed to elucidate the underlying biological mechanisms and confirm whether these brain alterations are indeed a characteristic feature of CLBP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".