Magnetic Resonance Imaging of Neuronal Function in the Spinal Cord: Spinal fMRI
Bibliographic record
Abstract
A review of the current literature on magnetic resonance imaging of neuronal function in the spinal cord (spinal fMRI) is presented. The unique challenges of spinal fMRI are identified as being the small cross-sectional dimensions of the spinal cord, magnetic field inhomogeneity caused by the bone and cartilage in the spine, and motion of cerebrospinal fluid, blood, adjacent tissues and organs and of the spinal cord itself. Techniques have been developed to overcome or compensate for these challenges and the result is a fMRI method which is distinct from that used for mapping function in the brain. Evidence that the current spinal fMRI method provides accurate and sensitive maps of neuronal function is also discussed. Studies presented in the literature have demonstrated areas of neuronal activity corresponding with spinal cord neuroanatomy as a result of thermal and electrical stimuli and motor tasks with the hands, arms and legs. Signal intensity changes detected in active areas have also been demonstrated to depend on the intensity of the stimuli with both thermal stimulation and a motor task, providing evidence of the correspondence between spinal fMRI results and neuronal activity in the spinal cord.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".