<i>In vivo</i> parcellation of the human spinal cord functional architecture
Bibliographic record
Abstract
The spinal cord is a critical component of the central nervous system, transmitting and integrating signals between the brain and the periphery via topographically organized functional levels. Despite its central role in sensorimotor processes and several neuromotor disorders, mapping the functional organization of the spinal cord in vivo in humans has been a long-standing challenge. Here, we test the efficacy of two data-driven connectivity approaches to produce a reliable and temporally stable functional parcellation of the cervical spinal cord through resting-state networks in two different functional magnetic resonance imaging (fMRI) datasets. Our results demonstrate robust and replicable patterns across methods and datasets, effectively capturing the spinal functional levels. Furthermore, we present the first evidence of spinal resting-state networks organized in functional levels in individual participants, unveiling personalized maps of the spinal functional organization. These findings underscore the potential of non-invasive, data-driven approaches to reliably outline the spinal cord’s functional architecture. The implications are far-reaching, from spinal cord fMRI processing to personalized investigations of healthy and impaired spinal cord function.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.032 | 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 teacher head, 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".