Effective Treatment of Chronic Low Back Pain in Humans Reverses Abnormal Brain Anatomy and Function
Bibliographic record
Abstract
Chronic pain is associated with reduced brain gray matter and impaired cognitive ability. In this longitudinal study, we assessed whether neuroanatomical and functional abnormalities were reversible and dependent on treatment outcomes. We acquired MRI scans from chronic low back pain (CLBP) patients before (n = 18) and 6 months after (spine surgery or facet joint injections; n = 14) treatment. In addition, we scanned 16 healthy controls, 10 of which returned 6 months after the first visit. We performed cortical thickness analysis on structural MRI scans, and subjects performed a cognitive task during the functional MRI. We compared patients and controls, as well as patients before versus after treatment. After treatment, patients had increased cortical thickness in the left dorsolateral prefrontal cortex (DLPFC), which was thinner before treatment compared with controls. Increased DLPFC thickness correlated with the reduction of both pain and physical disability. Additionally, increased thickness in primary motor cortex was associated specifically with reduced physical disability, and right anterior insula was associated specifically with reduced pain. Left DLPFC activity during an attention-demanding cognitive task was abnormal before treatment, but normalized following treatment. These data indicate that functional and structural brain abnormalities-specifically in the left DLPFC-are reversible, suggesting that treating chronic pain can restore normal brain function in humans.
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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 | 0.000 | 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) | 0.001 | 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".