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Four low-repeatability guides failed a shared prediction test in six SEEG patients and 16 fMRI adults

We tested whether four sites with low repeat correlation improve prediction beyond four sites with high repeat correlation. Separate public studies supplied human SEEG high-gamma activity and BOLD fMRI. Training sounds set site ranks, class means, scales, regression maps and correction weights. Other sounds tested predictions at other sites in different repeats. A local code lock preceded the reserved tests. Mean gains were 0.020% in six patients and 0.349% in 16 B2021 adults. The mean was 0.467% in eight NH2015 adults. Gains refer to target energy. Positive counts were 3/6, 15/16, 7/8. The fixed shared rule failed, p=0.863166. We retain all negative results and fixed controls. A comparison used the next four most repeatable guides. Its gains are descriptive. A Gaussian model shows how weak guides can lower error with one latent signal. Thus a positive gain would not count neural sources. We supply code, source pins and terms, plans, person scores, figures and repeat instructions. An independent solver reproduced losses for all 30 reserved people. Clinical sampling, source selection, separate cohorts, shared schedules and unequal measurement filters limit scope. The test does not establish absent weak-site information, a common neural cause, or positive cross-modal generalization.

auditory cortex · SEEG · fMRI · repeatability · response prediction · negative result

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References

11
  1. usesAdded guide patterns reduced test sound response error in SEEG and two fMRI cohorts with source processing sci:sha256:093bb5db0f1ab9c679b9961c2192c109be77a94fc41333105aeed3164edd3a54

    Prior guide-completion benchmark and source preparation lineage; the present test selects guides by training repeatability.

  2. usesFor 54 sounds, donor panel gains were uncertain in six SEEG patients and positive in 24 fMRI adults sci:sha256:63515a42466fb58197aea42a12d7f1584316262a1a1d72c21841b9b06d4b8937

    Verified source preparation and fetch code, version pins, and the warning that gain alone does not establish useful prediction.

  3. A test that removed 69 acoustic features did not confirm sound agreement in one iEEG panel and eight fMRI adults sci:sha256:2d4c61b53d6c8d4ee01056958f3b4bd9ebd6c48f301dcbc1b62e826006a9443b

    Prior fine sound geometry test inspected during question selection; we do not assume an unaudited SEEG clip identity map.

  4. usesFor 165 fixed sounds, four-component models reduced repeat error in six SEEG patients and 24 fMRI adults sci:sha256:1ffb0217c831625cbe8fc949585c14c1f283f7e9f1b2113e69122af88fa9a59b

    Upstream source preparation attribution.

  5. usesSpeech and song axes gave positive mean repeat scores for nine voice clips in SEEG and two fMRI cohorts sci:sha256:6720e49a50bda02902607508181e2616c7f5e48b21bceee56a2d3fcdc618bdae

    Original source preparation attribution.

  6. usesdoi:10.1371/journal.pbio.3001675

    Public human SEEG source study.

  7. usesdoi:10.5281/zenodo.6544488

    Version v1.1 of the electrical source release.

  8. usesdoi:10.1152/jn.00588.2020

    B2021 human BOLD source study.

  9. usesdoi:10.1016/j.neuron.2015.11.035

    NH2015 human BOLD source study.

  10. usesdoi:10.1371/journal.pbio.3002366

    Public author archive containing both fMRI derivatives and source metadata.

  11. usesdoi:10.5281/zenodo.8349726

    Versioned source code archive for fMRI data distribution.

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