The Effect of Combining Transcranial Direct Current Stimulation and Pain Neuroscience Education in Patients With Chronic Low Back Pain and High Pain Catastrophizing: An Exploratory Clinical, Cognitive, and fMRI Study
Bibliographic record
Abstract
OBJECTIVES: Priming the neural circuitry likely targeted by pain neuroscience education (PNE) using transcranial direct current stimulation (tDCS) may enhance the efficacy of PNE. This exploratory study aimed to examine the effects of combining tDCS and PNE in those with chronic low back pain (CLBP). METHODS: Six participants experiencing CLBP completed the study. Assessments for pain catastrophizing, kinesiophobia, pressure pain thresholds (PPT), pain intensity, cognitive function, and resting state and task fMRI were collected before and after the combined tDCS and PNE intervention. Each participant received five 20-min sessions of 2.0 mA tDCS targeting the left dorsolateral prefrontal cortex (DLPFC), followed by a 20-min PNE session. RESULTS: The participants had a 58.9% reduction in pain catastrophizing, a 25.9% reduction in kinesiophobia, and an 18.8% improvement in cognitive function (i.e., reduced attentional interference). The MRI results indicated increased gray matter volume within the left DLPFC. Additionally, larger reductions in DLPFC activity at rest were associated with larger reductions in kinesiophobia. Increased modularity within networks responsible for cognitive control and executive functions was evident following the intervention. DISCUSSION: Our clinical and fMRI outcomes shed light on the clinical potential of combining tDCS and PNE, as well as the mechanisms substantiating their effects. We speculate that tDCS alters brain activity and structure, amplifies the effects of PNE, and promotes positive changes in the cognitive-evaluative and sensory-discriminative pain domains investigated. A randomized controlled trial is warranted to determine the effects of tDCS combined with PNE compared with tDCS or PNE alone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".