Evaluation of Sensory Pathways in Spinal Cord by Comparison of fMRI Methodologies
Bibliographic record
Abstract
Introduction: Today, clinicians and neuroscientists need to have a comprehensive survey of neurological pathologies and injuries. For the First-time, SEEP contrast and Spin-Echo pulse sequences was used for functional imaging of the Lumbar spinal cord. This method used by several research groups for Spinal cord mapping, but other researchers tried to improve BOLD fMRI to Spinal cord imaging simultaneously. Here, we present a comparison between useful imaging methods and finally use of certain procedure for Spinal cord mapping with sensory stimulation. Materials and Methods: We planned two imaging protocols on Siemens 3T magnetom trio scanner: HASTE/SSFP, TE/TR: 76mS/6750mS for 9 sagittal slices with 2 mm thickness, FOV= 280×210mm, and Gradient Echo EPI, TE/TR: 30S/3000mS for 21 axial slices with 5mm thickness, FOV= 64×64mm. These image acquisition procedures performed on All 5 subjects (male, Age: 24.6±2.05), stimulated by 60g von Frey filament. Stimulations were used in the block design, 8 blocks with time 40.5 S for SE protocol and 42 S for GRE protocol and performed on right foot L4 dermatome. We entered each subject’s data sets into an individual first-level statistical analysis. Then we calculated signal changes in the mask that generated in the statistical analysis and amount of temporal Signal to Noise Ratio (tSNR). Other subjects group (5 males, Age 25.3±1.5) was imaged with same protocol as GE-EPI and T2W anatomic images (3D-FSE Isotropic, TR/TE:1500/430 ms, FOV= 256×60 mm, slice-thickness: 1mm). Data sets were processed with GLM and Finally, all first-level analysis results are entered in Higher-level analysis as group analysis and obtained statistical maps for spinal cord functions. Results: We observed different tSNR in two separated datasets, 2.619 ± 0.440 for SE-HASTE images and 4.901 ± 0.762 for GRE-EPI. After statistical analysis of images and obtaining signal change in the spinal cord. We comprised them and measured signal change was 1.737 ± 1.252 for SE-EPI and 2.554 ± 1.327 for GE-EPI. Based on these results we decided to use GE- EPI for spinal cord functional imaging. We stimulated L4 dermatome and this localized activation was observed in the T9-T1. The activation maps show ipsilateral synapse and tracks in the dorsal higher than ventral horn. Conclusion: At the first step evaluation of imaging methods show GRE-EPI can more efficient for Lumbar functional MRI and detection of BOLD signal change in the spinal cord. This research demonstrates the benefits of spinal cord fMRI for mapping of sensory stimulation. The resulting activity maps show primarily in ipsilateral dorsal regions and in some ventral regions, consistent with the spinal cord anatomy. These data also illustrate details of the sensory organization of the spinal cord, as well as anatomical detail of the spinous processes and positions of nerve roots.
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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 | 0.004 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".