The effect of MR scanner noise on auditory cortex activity using fMRI
Bibliographic record
Abstract
Auditory functional magnetic resonance imaging (fMRI) studies are limited by the presence of noise produced by echo planar imaging (EPI). The current study quantifies the effect of MR scanner noise on psychophysical measures of the perception of loudness and on measures of tonotopy, the representation of auditory frequencies within the auditory cortex. Seven normal hearing adults were examined using tones of 5 different auditory frequencies (250, 500, 1,000, 2,000, 4,000 Hz) of equal loudness. Using an imaging protocol with peak MR scanner noise at 1,460 Hz, the perception of loudness and detectable fMRI activity in response to a 1-kHz tone was less compared to other frequencies. When the imaging protocol was changed such that peak MR scanner noise occurred at 2,080 Hz, the perception of loudness and detectable fMRI activity in response to a 2-kHz tone was less compared to other frequencies. The reduction in the measured fMRI activity for tones near scanner frequencies may be due to an inflated scanner-induced baseline at those frequencies. In addition, fMRI activity decreased with increasing frequency, possibly due to the upward spread of masking of low-frequency, high-intensity tonal stimuli or the proximity of low-frequency core and belt areas of the auditory cortex. These results demonstrate the direct effect of scanner noise and high-intensity tonal stimuli on measurements of auditory cortex tonotopy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".