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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".