Can Theta Burst Electromagnetic Fields Disrupt Learning in Planaria? Evidence of Impaired Fear‐Conditioned Responses
Bibliographic record
Abstract
This study explored the impact of low-intensity theta burst patterned electromagnetic fields (TBEMF) on fear-related learning in the flatworm species Planaria, a simple model organism known for its regenerative properties and ability to demonstrate basic learning behaviors. Planaria were exposed to an aversive stimulus (light) in a T-maze, and changes in their behavior, including time taken to select an arm and preferred arm selections, were assessed over the course of several days. The TBEMF consisted of five pulsed bursts at 100 Hz with alternating amplitudes and an intensity of 1 μT. In the group exposed to aversive light, a significant decrease in preferred arm selections was observed (p < 0.001), indicating that the planaria successfully learned to avoid the arm associated with the aversive stimulus. However, planaria exposed to TBEMF, either before or after the light exposure phase, did not show the same behavioral adaptation, as their arm selections remained stable, indicating that no fear learning occurred. These findings suggest that TBEMF disrupts the processes involved in fear-related learning, likely by interfering with theta rhythm-dependent mechanisms that are crucial for memory encoding and retrieval. Further exploration of EMF's effects on more complex organisms could reveal additional insights into its broader applications and implications for both basic neuroscience and clinical practice. Bioelectromagnetics. 00:00-00, 2025. © 2025 Bioelectromagnetics Society.
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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".