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Independent contact models from eight patients gave nonzero five-lag prediction gains for shared-contact bipolar SEEG at 1–45 Hz

We tested whether a shared contact can give directed prediction when contact signals are independent. The model used public SEEG spectra from eight patients with epilepsy. It retained contact Fourier magnitudes and assigned independent phases. The main test used five past samples and frequencies from 1 Hz to below 45 Hz. Each patient value was the mean of two section medians. The equal patient mean was a 0.089 percent reduction in prediction error. The descriptive patient bootstrap interval was 0.055 to 0.121 percent. The preferred direction agreed across sections for 78.4 percent of pairs after equal patient weighting. Pairs with no shared contact and contacts with equal spectrum shapes gave zero gain. Exact covariance calculations and separate simulations support the result. The result concerns prediction under the stated model. It does not measure neural coupling in the recorded patients. The package preserves spectra, source versions, code, checks, and instructions to repeat the work.

stereoelectroencephalography · SEEG · bipolar montage · shared contacts · finite-history prediction · independent phases · spectral shape · Granger causality

Admitted 3 citing publications

References

5
  1. usesIndependent contact phase controls give absolute beta correlation above 0.30 on most shared-contact bipolar edges in eight SEEG recordings sci:sha256:b140aa736f880707f1f76cc167a2c578bc877c6ea56ddc6ca389a0996300da69

    We used the contact montage and pinned source range recipe for eight patients. We retrieved the source ranges and extracted spectra from 1 Hz to below 45 Hz. The prior spectra also checked the extraction over 4 Hz to below 30 Hz.

  2. usesdoi:10.18112/openneuro.ds004473.v1.0.2

    The analyzed contact spectra were extracted from OpenNeuro ds004473 version 1.0.2; the saved source range manifest and hashes identify the retrieved bytes.

  3. doi:10.1088/1741-2552%2Facae0a
  4. doi:10.1016/j.neuroimage.2012.09.036
  5. doi:10.3389/fnsys.2015.00189

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