Optimal transcranial magnetic stimulation coil placement for targeting the dorsolateral prefrontal cortex using novel magnetic resonance image‐guided neuronavigation
Bibliographic record
Abstract
The dorsolateral prefrontal cortex (DLPFC) has been implicated in the pathophysiology of several psychiatric illnesses including major depressive disorder and schizophrenia. In this regard, the DLPFC has been targeted in repetitive transcranial magnetic stimulation (rTMS) studies as a form of treatment to those patients who are resistant to medications. The '5-cm method' and the '10-20 method' for positioning the transcranial magnetic stimulation (TMS) coil over DLPFC have been scrutinised due to poor targeting accuracies attributed to inter-subject variability. We evaluated the accuracy of such methods to localise the DLPFC on the scalp in 15 healthy subjects and compared them with our novel neuronavigational method, which first estimates the DLPFC position in the cortex based on a standard template and then determines the most appropriate position on the scalp in which to place the TMS coil. Our neuronavigational method yielded a scalp position for the left DLPFC between electrodes F3 and F5 in standard space and was closest to electrode F5 in individual space. Further, we found that there was significantly less inter-subject variability using our neuronavigational method for localising the DLPFC on the scalp compared with the '5-cm method' and the '10-20 method'. Our findings also suggest that the '10-20 method' is superior to the '5-cm method' in reducing inter-subject variability and that electrode F5 should be the stimulation location of choice when MRI co-registration is not available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".