Constrained source space imaging: Application to fast, region‐based functional MRI
Bibliographic record
Abstract
A new technique called constrained source space imaging is introduced that holds promise for ultrafast acquisition of functional magnetic resonance imaging data. A sparse set of arbitrarily positioned, coarse voxels is first localized using radiofrequency selective excitation, from which magnetization signals are separated using only the spatial sensitivities of multichannel receiver coils, without the need for k-space encoding using imaging gradients. This method permits very fast acquisitions of targeted magnetization without complex or time-consuming image reconstruction techniques. Furthermore, because the data acquisition is performed without imaging gradients, T2* decays can be densely sampled and processed for contrast enhancement to improve functional magnetic resonance imaging data quality. Here, the constrained source space imaging technique is validated in proof-of-concept form, for a simple functional magnetic resonance imaging motor task using a prototype dual-band stimulated echo acquisition mode excitation to image four voxels at TR = 250 ms. Results demonstrate good voxel signal separation and good characterization of hemodynamic responses in primary motor cortices (M1) and supplementary motor areas through T2* fitting of the measured signals. With further refinement, the constrained source space imaging method has potential utility in a priori ROI-based functional magnetic resonance imaging experiments with TR values under 100 ms. Rapid, multivoxel measurements of other sources of MR signal contrast are also possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".