p38 MAPK patterned EMF affects PC-12 neurite outgrowth after 2 days of treatment
Bibliographic record
Abstract
Background and Objectives: Previous research has demonstrated that anatomical complexities of cortical regions determine transcranial magnetic stimulation (TMS) induced electric fields in the brain, which impact the response to TMS-based therapies.The current objective was to investigate the effect of individual neuroanatomy on first dorsal interosseous (FDI) and biceps brachii resting motor thresholds (RMT) in response to TMS.Methods: We performed a cross-sectional study using a convenience sample of ten healthy individuals (7 females, 23.5 ± 5 years).Each participant completed two TMS sessions (one each targeting the FDI and biceps cortical hotspots) and an MRI of the head on separate days.RMTs were determined using a Magstim Super BiStim stimulator via a 70 mm figure-of-eight coil to the left primary motor cortex and electromyography signals were measured from right FDI and biceps.Head models were generated based on T1 & T2 weighted MRI, while diffusion tensor imaging was used to determine fiber tract geometry for FDI and biceps corticospinal tracts.Via the models, we established neuroanatomical parameters including: fiber tract surface area (FTSA), tract fiber count (TFC), and brain scalp distance (BSD).Cortical electric field strength (EFS) was calculated using simulated stimulation of head models and finite element analysis.Results: For the FDI, RMT was dependent on the interaction between individually modeled parameters: 1) EFS and FTSA (p ¼ 0.036), and 2) EFS and TFC (p ¼ 0.004).For the biceps, RMT was dependent on the interaction between 1) EFS and FTSA (p ¼ 0.022) and 2) EFS and BSD (p ¼ 0.010).Conclusions: Our study results show that MRI-based measures of neuroanatomy, specifically cortical architecture and tract anatomy, differentially impact how the motor system responds to TMS.MRI-based modeling of individual neuroanatomy may be a useful approach to select appropriate motor targets when designing TMS based therapies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".