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Under joint pulse order exchangeability, a test did not detect excess late linear waveform change in 183 eight-pulse SEEG trains from five patients

We tested linear waveform change across eight-pulse SEEG trains. We used OpenNeuro ds004457 version 1.0.2. The fixed analysis included 183 trains at 6 mA. It used nine local ventral temporal pairs in five patients with epilepsy. Late scores measured 50 to 250 ms. Before scores measured -250 to -50 ms. The score has an exact mean of one across all pulse orders. The equal-patient late-minus-before difference was -0.01554. A joint order test gave p = 0.7789 under joint order exchangeability. The descriptive patient 95% interval was -0.12678 to 0.09571. This result does not establish stable responses or the absence of adaptation. Two fixed checks reversed the estimated direction. A raw rebuild matched 45 arrays and 10,146,624 voltage samples. A fresh process reproduced all 15 analysis outputs byte for byte. The package contains epochs, source metadata, pinned raw retrieval URLs, code, checks, results, and instructions.

SEEG · electrical stimulation · pulse order · waveform analysis · joint order exchangeability · permutation test · ventral temporal cortex · epilepsy · OpenNeuro

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References

4
  1. usesA sign flip test did not detect a larger pulse order ratio after stimulation in seven limbic SEEG patients under independent symmetric null effects sci:sha256:cfe5decab9ef6dedbce2064ab74781918b6bc93c616274990bc447060bc20894

    We used its question about pulse order and before-pulse structure. This study uses different data and a linear waveform score. It is not an exact replication.

  2. usesMean late response rank agreement decreased from 0.564 to 0.452 after sample variance subtraction in five selected SEEG patients sci:sha256:7f80f9660af79da244b497ffa94124fa1636e976fe409521f938472e9b739669

    We used its uncorrected bipolar epochs, source metadata, pinned raw manifest, and Python raw rebuild code. These files enabled a new analysis of pulse order within source stimulation trains.

  3. usesdoi:10.18112/openneuro.ds004457.v1.0.2

    This is the version-specific OpenNeuro dataset analyzed in the publication.

  4. doi:10.1523/JNEUROSCI.1325-22.2023

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