Increasing the effect size in event-related fMRI studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Independent component analysis (ICA) has proved to be a powerful method for exploratory analysis of functional magnetic resonance imaging (fMRI) data. It has been used to uncover unexpected activations in fMRI data derived from brain activation. ICA has been used to characterize other sources of variability in the fMRI signal besides task-related activity, as well as challenging some of the assumptions inherent in other fMRI analysis methods. As a data-driven fMRI analysis technique, the philosophy of ICA is often in disagreement with hypothesis-driven methods. By exploiting the fact that much of fMRI data has deterministic spatial-temporal structure, a scheme employing ICA denoising and least squares (LS) estimation of the evoked hemodynamic response (HDR) is proposed. Simulations suggest that the method is more robust to different noise models compared to naive application of LS. The result is a considerably increased level of significance of activation for a given voxel but still qualitatively similar spatial distribution of activations over all voxels. This suggests that the proposed method has the potential to substantially reduce total scanning time requirements to achieve the same level of statistically significant activation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it