Balancing Cohort Size and Variability in Iterative Deep Brain Template Creation
Bibliographic record
Abstract
Most studies conducting probabilistic mapping of the effect of deep brain stimulation (DBS) use an external anatomical reference such as those of the MNI (Montreal Neurological Institute, [1]), alternatively, group-specific templates can be generated to avoid external anatomical bias. This study investigates the effect of cohort size on the creation of such anatomical references for movement disorders. Pre-operative MRI data from 70 patients implanted with DBS systems were used to generate anatomical templates with varying cohort sizes (5 to 67 subjects). An iterative non-linear normalization pipeline was employed to optimize template generation. Template variability was assessed using Dice overlap of anatomical structures. The templates created with 44 subjects achieved an optimal balance between variability and precision. Tukey's HSD test confirmed significant differences between iterations and cohort sizes. This study underscores the importance of cohort size and iterative registration methods in creating high-quality anatomical templates.Clinical relevance- The findings provide insights into the optimal cohort size for creating group-specific anatomical brain templates.
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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 | 0.033 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".