Bibliographic record
Abstract
Multivariate machine learning algorithms were applied to BOLD fMRI data obtained from human subjects for decoding the orientation of gratings, with voxels larger than the width of orientation columns. Contributions to this successful decoding using low-resolution BOLD fMRI can potentially be made by 1) functionally selective large blood vessels, 2) orientation bias in large-scale organization, and 3) local orientation irregularities. In order to examine this issue, we re-analyzed cerebral blood volume-weighted fMRI data from cat visual cortex (Fukuda et al., J. of Neurosci, 2006). To remove large vessel contributions, ferrous iron oxide contrast agent was injected into blood. The functional data were obtained with 0.156 x 0.156 x 1 mm3. Then, high-resolution data were down-sampled to low-resolution up to 3 mm (the average orientation cycle is 1.0-1.4 mm). Linear support vector machine analysis showed that the presented orientation can be predicted above the chance level, even at 3-mm voxel resolution. To separate contributions from local orientation irregularities and from large-scale organizations, data were band-pass filtered with center frequency of 0.4 cycles/mm (frequency range of local irregularities) and 0.1 cycles/mm (low frequency). In both conditions, the presented orientation can be predicted above the chance level, with slightly better accuracy for the spatial filter of higher frequencies. Our analysis indicates that 1) large vessel contribution is not essential, and 2) local orientation irregularities can contribute for decoding of orientations in low-resolution fMRI data. Meeting abstract presented at VSS 2013
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".