Updated scalp heuristics for localizing the dorsolateral prefrontal cortex based on convergent evidence of lesion and brain stimulation studies in depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is entering wider use as a therapeutic intervention for many psychiatric illnesses. The efficacy of this therapeutic intervention may depend on accurately localizing target brain regions. Recent work investigating whole-brain maps of circuits associated with depression and its successful treatment has identified foci of interest within the dorsolateral prefrontal cortex (DLPFC). OBJECTIVE: To create an updated scalp heuristic for localizing the DLPFC based on convergent evidence of lesion and brain stimulation studies in depression. METHODS: Using the standard MNI ICBM152 anatomical template, we localized the scalp sites at minimum Euclidean distance from target MNI coordinates and performed nasion-inion, tragus-tragus, and head-circumference measurements on the anatomical template. We then derived equations to localize these scalp sites. RESULTS: The derived equations to calculate the arc length X and Y for these new targets are as follows: [Y=((NI+TrTr)/2)×0.3167 ; X=HC×0.1359] for the left anterior DLPFC[ Y=((NI+TrTr)/2)×0.2884; X=HC×0.1352] for the right anterior DLPFC[ Y=((NI+TrTr)/2)×0.2480; X=HC×0.1847] for the left posterior DLPFC[ Y=((NI+TrTr)/2)×0.2316 ; X=HC×0.1968] for the right posterior DLPFC CONCLUSIONS: This heuristic may help localize DLPFC targets identified in previous lesion-/stimulation-mapping work. A spreadsheet calculation tool is offered to support use of this heuristic.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it