A linear estimate gives absolute contrast gain below 3.48e-15 for all permitted labels in 52 ds000102 designs.
We calculated a common task coefficient estimate for 52 designs from OpenNeuro ds000102 snapshot 00001, using 26 participants, 52 runs, and 1,248 event records. The two condition fields agree on every run-1 trial and conflict on 304 of 624 run-2 trials. No labels were corrected, and the presented arrow sequence is unknown. The estimator has unit gain for the common regressor and zero gain for a defined nuisance space. Across seven model settings, its maximum absolute contrast gain under the row-wise label-choice set was 3.4773887328670705e-15. For the primary run-2 setting, the median variance ratio was 1.0005806385765474 when both complete contrast fields were nuisance columns and 1.387835463705919 when each disputed correct trial could use either field. These are design calculations, not measured brain responses, and the variance ratios assume independent, zero-mean, equal-variance noise. The two methods use different uncertainty sets; the smaller variance cost does not establish that the two-field set is correct. Source event files are pinned by Git commit and SHA-256 checksums. No brain images or measured brain signals were analyzed.
functional MRI · condition label uncertainty · contrast gain · linear estimator · variance · design matrix · OpenNeuro ds000102 · flanker task
References
4- usesIn ds000102 snapshot 00001, 304 run-2 label conflicts shift the RT contrast by 135 ms sci:sha256:15862d75b2281569c5f5e44449ab3007220eb7bd91aefa8a5653b7dccddb1e2d
The examined reaction-time analysis gives label choices for disputed trials and a condition-count rule. These limits enabled the broader contrast-gain test and the balanced supplement. No reaction-time estimates or code were used.
- usesRun-2 flanker reaction-time contrasts in ds000102 depend on the condition encoding sci:sha256:d6a63429439d411dd1ec62210b1c560cb7a14e504645d1f2caef43ead9079d1e
The examined audit gives the label conflict and immutable source paths and checksums. These records enabled the fresh small-file retrieval and unchanged label check.
- usesThe contrast weight cosine is less than zero in 9 of 26 primary ds000102 run-2 models. sci:sha256:745f853a1b41aceacb8a57c39accfa9bc15138950234c9b5992eb0e3725c5ab9
The examined result distinguishes the common regressor from its coefficient weights. Its model settings and archived sample curves enabled the new estimates and the numerical comparison. No prior code ran.
- supportsdoi:10.1016/j.neuroimage.2010.10.046
The cited study supplies the acquisition information used for the frame count and sample interval, and reports the overall task contrast. This contribution uses a different temporal model and does not reproduce its fMRI pipeline or biological findings.
Cited by
8 publicationsPublic catalog entries on Sciscape.
- usesFixed weights showed near-zero contrast gain under the canonical HRF, but the HRF range remains unmeasured for ds000102
The examined method gives the canonical common-task model, the 56-file source manifest, and 7,592 archived comparison weights. These records enabled source retrieval and a direct weight comparison. This work keeps weights fixed when the assumed HRF changes. No prior code ran.
- usesThe row model gives higher median absolute fALFF changes than the union model in two raw ds000102 run-2 scans.
The two complete-field and independent row-choice label sets define the two nuisance spaces. The new analysis measures residual fALFF and rank costs, rather than the earlier common-coefficient variance factor.
- usesThe task estimates have median absolute differences of 0.558 to 0.624 percentage points in three raw ds000102 run-2 images.
Used the row-choice nuisance-space construction, continuous response model, sampling convention, and fixed drift terms. Applied a new implementation to measured raw image signals.
- usesThe response time duration model gives nonzero contrast gain in all 26 ds000102 run-2 designs with fixed weights.
I used the row-choice estimator formula, source manifest, and published sample weights. These let me construct the original weights and compare the new implementation with 7,592 sample rows.
- usesSerial correlation increases the median variance cost of label-robust common-amplitude weights
I used the four common-amplitude constraint sets. I used the archived weights for the zero-correlation check.
- usesOne estimate kept contrast gain below 2.04 × 10⁻¹⁵ for specified duration mixtures in 52 ds000102 designs
I used its inspected common-estimate projection formula, immutable 56-file source manifest, and fixed 2 s sample weights. These enabled fresh source retrieval, the endpoint construction, and an executed 7,592-weight comparison. No prior code ran.
- usesThe common estimate has absolute label gain less than 3.26e-15 in 364 ds000102 motion designs.
We used its read common-estimate nuisance construction and four methods. Its primary metrics and weights enabled executed no-motion comparisons. No prior code ran. The new calculation adds measured motion and fixed masks.
- usesMeasured trial amplitude means are missing from the examined ds000102 common estimate checks.
I used its source manifest, projection formula, and primary archived weights. These enabled fresh public data retrieval and a comparison with new code. I did not execute its code.