Oscillating gradient spin echo diffusion time effects implicate variations in neurite beading for the heterogeneous reduced diffusion in human acute ischemic stroke lesions
Bibliographic record
Abstract
Abstract Purpose Monte Carlo simulations and short diffusion time measurements have suggested neurite beading and swelling as the underlying mechanism of reduced diffusion in acute stroke, although the observed diffusion time dependence is often heterogeneous and not yet fully understood. This study aimed to investigate the heterogeneity of diffusion time effects in ischemic lesions and explore the potential microstructural basis with Monte Carlo simulations. Methods Pulsed gradient spin echo (PGSE, diffusion time 40 ms) and oscillating gradient spin echo (OGSE 40 Hz, diffusion time ˜5.1 ms) were acquired within 5 min in 39 acute ischemic stroke patients at 3 T. Mean, axial, and radial diffusivity differences between OGSE and PGSE (ΔMD/ΔAD/ΔRD) were compared between lesion and contralateral tissues (white and gray matter). Monte Carlo diffusion simulations of beaded axons for the experimental waveforms were used to investigate the effects of neurite morphology on time‐dependent diffusivity changes. Results PGSE yielded the typical mean diffusivity (MD) reduction of −40 ± 10% in ischemic lesions, whereas it was less at −29 ± 11% for OGSE 40 Hz. The OGSE‐PGSE diffusion time difference was greater in lesions (ΔMD = 0.12 ± 0.06 × 10−3 mm2/s) than contralateral white matter (ΔMD = 0.04 ± 0.06 × 10−3 mm2/s), consistent with larger beading amplitude (0.18–0.43) and intracellular volume fraction (0.61–0.78) in lesions. ΔMD maps revealed regional variation with the largest effects in internal capsule and corona radiata. Conclusion This study found greater diffusion time effects in ischemic regions with purportedly larger axons. Monte Carlo simulations further support that the pronounced OGSE‐PGSE diffusivity differences are expected in large axons with high beading amplitude.
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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 | 0.000 | 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".