Variations in BOLD response latency estimated from event‐related fMRI at 3T: Comparisons between gradient‐echo and Spin‐echo
Bibliographic record
Abstract
ABSTRACT Functional magnetic resonance imaging (fMRI) commonly uses gradient‐recalled echo (GRE) signals to detect regional hemodynamic variations originating from neural activities. While the spatial localization of activation shows promising applications, indexing temporal response remains a poor mechanism for detecting the timing of neural activity. Particularly, the hemodynamic response may fail to resolve sub‐second temporal differences between brain regions because of its signal origin or noise in data, or both. This study aimed at evaluating the performance of latency estimation using different fMRI techniques, with two event‐related experiments at 3T. Experiment I evaluated latency variations within the visual cortex and their relationship with contrast‐to‐noise ratios (CNRs) for GRE, spin echo (SE), and diffusion‐weighted SE (DWSE). Experiment II used delayed visual stimuli between two hemifields (delay time = 0, 250, and 500 ms, respectively) to assess the temporal resolving power of three protocols: GRETR1000, GRETR500, and SETR1000. The results of experiment I showed the earliest latency with DWSE, followed by SE, and then GRE. Latency variations decreased as CNR increased. However, similar variations were found between GRE and SE, when the latter had lower CNR. In experiment II, measured stimulus delays from all conditions were significantly correlated with preset stimulus delays. Inter‐subject variation in the measured delay was found to be greatest with GRETR1000, followed by GRETR500, and the least with SETR1000. Conclusively, blood oxygenation level‐dependent responses obtained from GRE exhibit greater CNR but no compromised latency variations in the visual cortex. SE is potentially capable of improving the performance of latency estimation, especially for group analysis. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 215–221, 2013
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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 | 0.001 | 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 | 0.000 |
| 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".