Database of MNI stereotactic coordinates for deep brain stimulation targets in neuropsychiatric disorders
Bibliographic record
Abstract
Introduction Deep brain stimulation (DBS) is a promising therapy option for otherwise treatment-resistant neuropsychiatrie disorders, especially in obsessive-compulsive disorder (OCD), major depression (TRD) and Tourette's Syndrome (TS). Objective The brain coordinates of the DBS targets are mainly reported using measurements in original, unnormalized brains. In the neuroimaging community stereotactic data are mainly indicated in the standardized Montreal Neurological Institute (MNI) space, i.e. a three-dimensional proportional grid system. Aims Improved comparability between targets in DBS studies and molecular and functional neuroimaging data from PET, SPECT, MRI, fMRI, mostly published with stereotactic data. Methods A comprehensive and systematic literature search for published DBS case reports or studies in TRD, OCD and TS was performed. We extracted the tip positions of electrode leads as provided in the publications or by the authors, and transferred individual coordinates to the standard brain in the MNI space. Results 46 publications fulfilled the inclusion criteria. The main targets for the specific disorders and one or two examples of their calculated MNI coordinates are indicated in the table: [MNI coordinates of the main DBS targets] Conclusions We provide DBS data of neuropsychiatrie disorders in the MNI space, improving the comparability to molecular, functional and structural neuroimaging data.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".