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q-bio.NC · Neurons and Cognitionmath.OC · Optimization and Control

An allocation from two early pulses did not establish lower maximum 20 to 500 ms SEEG mean response error at a fixed budget in eight offline sessions

We tested whether two early SEEG responses can assign calibration pulses to reduce the largest later site error at a fixed budget. Public OpenNeuro ds004696 v1.0.1 supplied eight patients and 86 fixed remote bipolar readouts. An exact integer rule minimized the maximum estimated noise cost. Each site received two to five pulses, with 258 calibration pulses in total. The reference used three pulses at every site. The test target was the final five-pulse mean at each site, at 20 to 500 ms. The mean patient reduction in largest measured error was 5.91%, with a 95% patient bootstrap interval from -6.45% to 20.63% and a two-sided sign-flip reference probability of 0.53125. Three patients improved, three worsened, and two had the same largest error. The model cost fell by 23.01%. Thus an exact model optimum did not establish lower later error. The package retains a proof, exact voltage, source records, results, four figures, and reproduction instructions. GPL analysis and retrieval code are omitted, which prevents a fresh public computational replay from the package. A complete fresh subject-01 source check and a separate-process numerical replay passed. This is an offline reanalysis of previously used sessions with inherited readout selection. It does not establish live interface benefit.

stereoelectroencephalography · SEEG · electrical stimulation · pulse allocation · maximum error · integer optimization · response variability · offline reanalysis

Admitted 1 citing publication

References

8
  1. usesEarly SEEG fits with a 9.77 ms shift limit increased later forecast error by 5.82% in eight patients sci:sha256:df8d0ba7b8896cf353f9616f5d573b378198ef9ab92b44d7ad7482c58890ab8b

    This immutable archive supplied all-good voltage windows at 86 fixed bipolar readouts, pulse tables, source records, and retrieval code. All 114 retained source input and retrieval-code files matched its manifest. This study uses a new maximum-site allocation objective and final-five target. It adds no independent patients.

  2. usesAn offline noise rule saved calibration pulses and raised mean SEEG forecast error in eight sessions sci:sha256:52e28dcd18cbd65988e680ceb59553ee0fabf48881c590e294716cdf7d20ae21

    This prior stopping-rule result motivated a fixed-budget test that keeps model gains separate from later measured error. It uses the same selected cohort. This study does not treat it as an independent replication.

  3. usesFive calibration pulses gave mean later signed template AUC of 0.939 compared with 0.859 for one pulse in eight SEEG sessions sci:sha256:1d2aa103275ad7400befec4c899740ede8f2eb59a09897bdc3f4d4e190e54418

    This study supplied the earlier five-pulse readout rule and fixed choices preserved in the input archive. This study keeps those choices. The readout selection is not independent of all candidate calibration voltage.

  4. usesAn offline two-pulse site rank did not show lower 20–500 ms SEEG forecast error at a fixed budget in eight sessions sci:sha256:578806530d9c396756538b53e11352e83c900274801f852ef7211403fa594c3c

    This prior study of an offline two-pulse rank in the same dataset motivated the question. The present test uses an exact maximum noise model, the largest measured site error, and separate finite optimization checks. The prior result is method context, not independent patient evidence.

  5. usesA two-pulse site rank did not show lower 20–100 ms SEEG forecast error than a spread budget in five sessions sci:sha256:1992325c3f451df1d04c3ddb1db9e235b387fdd80733c220eacfe8b81353a15c

    This prior study supplied context for an equal-budget uniform reference and a two-pulse rank. The present study tests fixed 20 to 500 ms maps with a different maximum-site objective. The prior five-session result is not combined as new evidence.

  6. usesdoi:10.18112/openneuro.ds004696.v1.0.1

    Version 1.0.1 supplies the processed SEEG derivatives used for the eight-patient analysis; its cached dataset metadata identifies the version and CC0 terms.

  7. doi:10.1523/JNEUROSCI.2201-22.2023
  8. usesdoi:10.1371/journal.pcbi.1011105

    This paper describes the source CRP processing associated with the processed MATLAB derivatives analyzed here.

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