Multiecho coarse voxel acquisition for neurofeedback fMRI
Bibliographic record
Abstract
"Real-time" functional magnetic resonance imaging is starting to be used in neurofeedback applications, enabling individuals to regulate their brain activity for therapeutic purposes. These applications use two-dimensional multislice echo planar or spiral readouts to image the entire brain volume, often with a much smaller region of interest within the brain monitored for feedback purposes. Given that such brain activity should be sampled rapidly, it is worthwhile considering alternative functional magnetic resonance imaging pulse sequences that trade spatial resolution for temporal resolution. We developed a prototype sequence localizing a column of magnetization by outer volume saturation, from which densely sampled transverse relaxation time decays are obtained at coarse voxel locations using an asymmetric gradient echo train. For 5×20×20 mm3 voxels, 256 echoes are sampled at ∼1 msec and then combined in weighted summation to increase functional magnetic resonance imaging signal contrast. This multiecho coarse voxel pulse sequence is shown experimentally at 1.5 T to provide the same signal contrast to noise ratio as obtained by spiral imaging for a primary motor cortex region of interest, but with potential for enhanced temporal resolution. A neurofeedback experiment also illustrates measurement and calculation of functional magnetic resonance imaging signals within 1 sec, emphasizing the future potential of the approach.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".