Mapping Human Proprioceptive Projections of Upper Limb Muscles Through Spinal Cord <scp>fMRI</scp>
Bibliographic record
Abstract
The functional organization of the human spinal cord has primarily been derived from clinical observations and invasive electrophysiological studies. Recent methodological advances opened the possibility of studying the neuronal activity of the spinal cord in humans using noninvasive functional magnetic resonance imaging (fMRI). Here, we took advantage of fMRI to map the patterns of activity elicited by muscle-specific proprioceptive information along the whole cervical cord. We quantified the fMRI signals of the cervical spinal cord in 24 healthy participants who received mechanical muscle tendon vibration to stimulate proprioceptive afferents. The wrist flexor, biceps, and anterior deltoid muscles were independently stimulated while the upper limbs were stationary to avoid movement artifacts. To account for anatomical variability among participants, we optimized activity pattern localization by identifying individual rootlets and determining corresponding spinal levels using a trained deep-learning model. Distinct activation patterns emerged based on the stimulated muscle and body side, which coincided with well-established myotome maps. Concretely, the vibration-induced proprioceptive stimuli activity circumscribed to the ipsilateral ventral horn with a rostrocaudal distribution that reflected the proximo-distal location of the stimulated muscles. This spatial organization supported the proprioceptive origin of the response. This study demonstrates that muscle tendon vibration combined with spinal cord fMRI enables the noninvasive identification of upper-limb myotomes within the cervical spinal cord, offering new possibilities for studying the functional organization of the spinal cord and for clinical applications.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".