Use Of Diffusion Tensor Magnetic Resonance Imaging For Assessment Of Musculoskeletal Structure Following An Acute Bout Of Downhill Running
Bibliographic record
Abstract
Ultrastructural evidence of exercise-induced muscle disruption (EIMD) following endurance exercise has traditionally been demonstrated through the use of skeletal muscle biopsy sampling. Noninvasive methods such as T2 and proton-density fat saturated magnetic resonance imaging (MRI) have been applied to examine EIMD, but these are limited by their sensitivity and dynamic range. Diffusion Tensor MRI (DT-MRI) measures the self-diffusion of water in biological tissues and can be used to visualize fiber patterns in various tissues including skeletal muscle. DT-MRI has previously been utilized to assess skeletal muscle abnormalities induced by traumatic injury (e.g., acute hematoma and muscle tears) and after high-force eccentric exercise; however, it remains unknown whether this method can be applied to detect EIMD after an acute bout of endurance running. PURPOSE: To investigate changes in musculoskeletal structure using DT-MRI after an acute bout of downhill running previously shown to induce ultrastructural disruption. METHODS: Ten healthy men (25 ± 1 y; VO2 peak = 52 ± 3 ml·kg-1·min-1) ran downhill on a treadmill (-10 degrees) for 45 min at 70% of maximal heart rate. DT-MRI measurements were performed using a GE 3T excite-HD MRI system using 15 diffusion encoding directions (8 NEX, FOV=20cm, TE/TR=67/6000, 64×64 matrix, 4mm thick, 0 skip, b=300s/mm2). RESULTS: Isometric (238 ±12 vs. 209 ± 11 N/m) and isokinetic (194 ± 8 vs. 178 ± 5 N/m) peak torque decreased at 24 h post-exercise compared to baseline while serum creatine kinase activity increased (22 ± 3 vs. 106 ± 21 U/L) (all P<0.05). DT-MRI revealed a decrease in fractional anisotropy (FA) in the vastus lateralis at 24 h post-exercise compared to baseline (0.282 ± 0.006 vs. 0.262 ± 0.005 arbitrary units; P<0.05), which is indicative of changes in overall fibre organization. CONCLUSION: These data suggest that DT-MRI may be a suitable technique to non-invasively detect, and possibly quantify, exercise-induced changes in skeletal muscle ultrastructure. Supported by NSERC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".