Functional tracing of medial nociceptive pathways using activity-dependent manganese-enhanced MRI
Bibliographic record
Abstract
Manganese ion (Mn(2+)) was used as a paramagnetic contrast agent in T1-weighted magnetic resonance imaging (MRI) images. They enter neural cells though voltage-gated calcium channels and are activity-dependently transported along axons and across synapses. The aim of the present study was to investigate the nociceptive medial thalamus projection in rats by activity-dependent manganese-enhanced magnetic resonance imaging (MEMRI). Rats under urethane and α-chloralose anesthesia were microinjected with manganese chloride (MnCl(2), 120mmol/L, iontophoretically with a 5-μA current for 15min) into the right medial thalamus. Innocuous (at a 50-μA intensity for 0.2ms) or noxious (at a 5-mA intensity for 2ms) electrical stimuli were applied through a pair of needles in the left forepaw pads once every 6s for 5h. Enhanced transport of Mn(2+) were found in the anterior cingulate cortex, midcingulate cortex, retrosplenial cortex, ventral medial caudate-putamen, nucleus accumbens, and amygdala in the noxious-stimulated group. Enhancements in the anterior cingulate cortex, midcingulate cortex, ventral medial caudate-putamen, nucleus accumbens, and amygdala, but not the retrosplenial cortex, were attenuated by an intraperitoneal injection of morphine (5mg/kg and 1mg/kg/h, intraperitoneal). These results indicate that a combination of MEMRI with activity-induced manganese-dependent contrast is useful for delineating functional connections in the pain pathway. Noxious stimulation induced enhancement of manganese ion transportation from medial thalamus to cingulate cortex and medial striatum, but not motor cortex. A combination of manganese-enhanced magnetic resonance imaging with activity-dependent contrast is useful for delineating functional connections of the medial pain pathway.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".