Connectivity Based Functional Segmentation Of The Brainstem
Bibliographic record
Abstract
The human brainstem is an anatomically complex and compact structure, and many neurologic diseases are frequently associated with brainstem dysfunction. Despite its importance in brain functioning and neurodegenerative processes, the brainstem and its functional sub-structures are relatively unexplored in medical image analysis. Here we present a data-driven framework to extract functional sub-regions from the brainstem. We first apply a novel motion correction scheme to the brainstem. A simple network is then derived by examining the correlation of BOLD signals between brainstem voxels, and a network community quality function is optimized to extract the sub-networks within the brainstem. We applied this technique to fMRI data from fifteen healthy participants and found 84 group-level, spatially contiguous sub-regions within the brainstem. Association of these regions with other cortical and subcortical brain regions were investigated to assist in interpreting what underlying anatomical structures were associated with the subregions. Although the proposed method was originally developed for the brainstem, the proposed framework has the potential to be integrated into studies investigating functional sub-regions from other cortical or subcortical brain regions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".