Intrinsic functional clustering of ventral premotor F5 in the macaque brain
Bibliographic record
Abstract
Neurophysiological and anatomical data suggest the existence of several functionally distinct regions in the lower arcuate sulcus and adjacent postarcuate convexity of the macaque monkey. Ventral premotor F5c lies on the postarcuate convexity and consists of a dorsal hand-related and ventral mouth-related field. The posterior bank of the lower arcuate contains two additional premotor F5 subfields at different anterior-posterior levels, F5a and F5p. Anterior to F5a, area 44 has been described as a dysgranular zone occupying the deepest part of the fundus of the inferior arcuate. Finally, area GrFO occupies the most rostral portion of the fundus and posterior bank of inferior arcuate and extends ventrally onto the frontal operculum. Recently, data-driven exploratory approaches using resting-state fMRI data have been suggested as a promising non-invasive method for examining the functional organization of the primate brain. Here, we examined to what extent partitioning schemes derived from data-driven clustering analysis of resting-state fMRI data correspond with the proposed organization of the fundus and posterior bank of the macaque arcuate sulcus, as suggested by invasive architectonical, connectional and functional investigations. Using a hierarchical clustering analysis, we could retrieve clusters corresponding to the dorsal and ventral portions of F5c on the postarcuate convexity, F5a and F5p at different antero-posterior locations on the posterior bank of the lower arcuate, area 44 in the fundus, as well as part of area GrFO in the most anterior portion of the fundus. Additionally, each of these clusters displayed distinct whole-brain functional connectivity, in line with previous anatomical tracer and seed-based functional connectivity investigations of F5/44 subdivisions. Overall, our data suggests that hierarchical clustering analysis of resting-state fMRI data can retrieve a fine-grained level of cortical organization that resembles detailed parcellation schemes derived from invasive functional and anatomical investigations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".