Altered Amplitude of Low-Frequency Fluctuations of rs-fMRI Signal followed by rTMS Analgesic Effects in Non-Specific Chronic Low Back Pain (CLBP) Patients
Bibliographic record
Abstract
Background: Non-specific chronic low back pain (CLBP) is a common painful condition and is responsible for different physical disorders. Despite alternative therapies, patients still suffer from persistent pain. Repetitive transcranial magnetic stimulation (rTMS) has provided much evidence of pain reduction, but results have not been examined deeply in CLBP symptoms.Objective: The analgesic effect of rTMS in non-specific CLBP patients was evaluated by the amplitude of low-frequency fluctuation (ALFF) analysis in resting-state fMRI.Material and Methods: In this experimental study, fifteen non-specific CLBP participants (46.87±10.89 years) received 20 Hz rTMS over the motor cortex. The pain intensity and brain functional scan were obtained during pre and post-stimulation for all participants. The ALFF maps of the brain in two scan sessions were identified and the percentage of pain reduction (PPR%) was determined using paired t-test. Also, correlation analysis was used to find a relationship between ALFFs and pain intensity.Results: Pain intensity was significantly reduced after induced-rTMS in non-specific CLBP (36.22%±13.28, P<0.05). Positive correlation was found between ALFF in the insula (INS) and pain intensity (rpre-rTMS=0.59, rpost-rTMS=0.58) while ALFF in medial prefrontal cortex (mPFC) and pain intensity had negatively correlated (rpre-rTMS=-0.54, rpost-rTMS=-0.56) (P<0.05). ALFF increased in mPFC while INS, thalamus (THA), and supplementary motor area (SMA) showed decremental ALFF followed by rTMS. Conclusion: This study demonstrated that ALFF in INS, THA, mPFC, and SMA is associated with CLBP symptoms and analgesic effects of rTMS. ALFF potentially seems to be a proper objective neuroimaging parameter to link spontaneous brain activity with pain intensity in non-specific CLBP 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 | 0.000 | 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".