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After site scaling, a spatial filter gave lower mean gains for voice sounds than for source nonvoice sounds in six SEEG patients and two fMRI cohorts

We tested the forecast value of one spatial filter across sound classes. Calibration standard deviations set the site scales. Separate public studies supplied human SEEG high-gamma activity and BOLD fMRI. The original positive voice-benefit test failed. Development then selected a matched class comparison. A local code lock preceded tests in six other patients, sixteen B2021 adults and eight NH2015 adults. Each filter fit repeat pairs on 102 or 103 source nonvoice sounds. The same fit predicted held-out nonvoice and human-voice patterns against a third repeat. A fitted scalar was the reference. Mean nonvoice gains were 1.744%, 5.549% and 3.257%. Mean voice gains were -2.888%, -3.944% and -3.211%. All 30 people showed this reversal. Mean class gaps were 4.632, 9.493 and 6.469 percentage points. Their 95% person intervals were [2.791,6.473], [7.218,11.768] and [4.928,8.009]. The shared mean rule passed four-claim Holm correction, p=0.00263061. Common sound-and-person ranges stayed positive with calibration fits fixed. Effects for nine other human voices were uncertain. Without site scaling, the second MRI study selected only the scalar and gave zero gaps. All failures and controls remain. An exact population example removed all target signal yet reduced forecast error by 11.48%. A weaker signal on unchanged axes also reduced filter gain. Thus the measured class gap does not identify a shared circuit, new voice dimensions or a cause. Cohort differences, selected sites, common schedules, repeat errors, unequal measurement filters and fixed panels limit transfer. The package gives source pins and terms, code, every score, uncertainty draws, figures, audits and repeat steps. Original neural arrays remain external. An exploratory majority sensitivity did not pass correction.

auditory cortex · SEEG · fMRI · spatial filtering · repeat prediction · class transfer · ridge regression

Admitted 2 citing publications

References

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  10. usesSound response areas were positive after class mean removal in selected data from six SEEG patients and two fMRI cohorts sci:sha256:b9fb134f2edb5d53be25b14de20f246740c623872c4893148a7ae7deb2ee664c

    Reuse of pinned public source list, preparation code under MIT, and the inherited MRI site rule. All source arrays were rebuilt.

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

    Original source reconstruction code credited by the directly reused SCI preparation package.

  12. Acoustic models fit to other sound classes predicted music response patterns in 20 iEEG patients and 24 fMRI adults sci:sha256:b9574e9f866b3489f87868b67c294f081e3fd2b1efa4ff110a32f8558fb27b4b

    Related sound-class transfer work; its acoustic model outcomes are not present inputs.

  13. Residual sound maps agreed between a fixed iEEG panel and 20 fMRI adults under a rotation reference sci:sha256:c833e2811606e931c209b9a349ce18fad053c6a5d3d1e78c89d2e3e2cb2a83e9

    Related residual sound geometry and measurement-noise context.

  14. usesRepeat variance profiles agreed on average between people in SEEG and one fMRI study; a second fMRI transfer test failed sci:sha256:636e30a6d0bd9583210ae48524b0da1b99b63c0c97310de94da3321406a49b86

    Motivates the common event-order audit and control. No outcome data reused.

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