Brief Cerebral Applications of Weak, Physiologically-patterned Magnetic Fields Decrease Psychometric Depression and Increase Frontal Beta Activity in Normal Subjects
Bibliographic record
Abstract
Background: Brief (30 min to 60 min) applications of weak, physiologically-patterned magnetic fields have been demonstrated to produce analgesia in rodents and reduction of depression in patients who have sustained Traumatic Brain Injuries (TBI). To discern if the effects from one effective field pattern could be measured by quantitative electroencephalography (QEEG) and reflected in psychometric inferences of depressed mood, normal volunteers were measured. Methods: A total of 22 normal volunteers were exposed for 30 min to sham field conditions or to a burst-firing magnetic field (1 μT) that was applied across the temporal lobes during QEEG measurements. The Profile of Mood Scores short-form (POMS-sf) was administered before and after the exposures and correlations between these scores and bands of QEEG power were completed. Results: The subjects exposed to the burst-firing magnetic field displayed a significant decrease in depression scores compared to those exposed to the sham fields. The treatment effects accommodated about one-quarter of the variance in scores. Like previous studies the other components of the POMS-sf did not differ significantly between treatments. Field exposed subjects displayed significant increases in beta power over the prefrontal regions and left temporoparietal areas the magnitude of which was strongly correlated with the depression mood scores. Conclusion: Brief exposures to weak, physiologically-patterned magnetic fields that can be generated by contemporary computer systems produced reliable changes in QEEG activity and were even reflected in relatively insensitive psychometric indicators in normal individuals. The development of this technology for self-treatment of patients who have sustained TBIs may be a useful adjunctive therapeutic intervention.
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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 | 0.000 |
| 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".