The nucleus accumbens-prefrontal connectivity as a predictor of chronic low back pain
Bibliographic record
Abstract
ABSTRACT: The nucleus accumbens (NAc) and its prefrontal connections are implicated in the aetiology of chronic low back pain (CLBP). Animal and human studies suggest that the NAc and its connections play a critical role in the transition from acute to CLBP. However, whole-brain connectivity in individuals with longstanding CLBP has not been systematically investigated. Using a functional connectomics approach, we examined whether the 2 NAc subregions-shell and core-exhibit different whole-brain connectivity between CLBP patients and healthy controls (HCs; total N = 197). The identified connections were correlated with CLBP intensity (multiple comparisons corrected), and their reproducibility was validated in 2 independent cohorts. These clinically relevant and reproducible connections were further leveraged to classify CLBP using machine learning. Compared with HC (n = 41), individuals with CLBP (n = 39) exhibited hyperconnectivity between the NAc shell and core and the prefrontal cortex (PFC). Although several NAc-PFC connections were linked to higher CLBP intensity, only the connections between the left NAc shell and core to the right dorsolateral PFC were reproduced in validation cohorts (total CLBP n = 53; HC n = 64). Nucleus accumbens-right dorsolateral PFC connections achieved 84% classification accuracy using logistic regression. The machine learning analyses demonstrate how knowledge-based feature selection can reliably detect CLBP. Overall, we report that NAc-PFC connectivity consistently distinguishes people with CLBP from HC and suggest an abnormal interaction between the NAc and brain regions involved in motivation, decision-making, and pain regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".