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Pre-stimulation high-gamma/theta power did not predict recall or modify the 50 Hz stimulation effect in 11 FR2 patients

We tested whether the recall cost of open-loop 50 Hz stimulation during word encoding depended on brain state before stimulation began, as proposed for closed-loop memory stimulation. In 27 sessions from 11 patients in OpenNeuro ds005489, we measured log power from 1.0 to 0.25 seconds before each word pair. A training-free high-gamma/theta tilt (80–140 Hz minus 3–8 Hz) did not predict recall in control pairs (AUC 0.495). The stimulation effect was similar in low- and high-tilt pairs: the high-minus-low difference was −1.2 percentage points (95% CI −12 to +12; sign-flip p = 0.87). A logistic classifier produced a +16 percentage point contrast (p = 0.004), but its cross-validated control-pair scores were anti-correlated with recall (AUC 0.40), making this contrast an artifact rather than valid evidence of state dependence. The results are limited by low power, 11 of 38 patients, and only 3 patients with mostly SEEG contacts.

memory · free recall · intracranial EEG · closed-loop stimulation · brain state · high gamma · theta power · state dependence · classifier bias

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References

4
  1. usesIn 18 session-0 SEEG patients, pre-word band power did not predict free recall within serial position or improve the encoding decoder sci:sha256:e895a140704262417a90debba35aa4ba4bcfe623d7b6e7bbff951cc86120f151

    It found that pre-onset band power did not predict recall in FR1; our control-pair AUC near 0.5 in FR2 agrees.

  2. uses50 Hz open-loop depth stimulation during word encoding decreased later recall in ds005489; surface stimulation showed no effect sci:sha256:4636af3ff0cc862353979c5b008d8dee3803276e9ab70b7bea23c76a9bba0bf0

    It reported the FR2 depth-stimulation recall cost; this work tests if that cost depends on the pre-stimulation state.

  3. usesCross-patient SEEG recall decoding was above chance but weaker than a three-list within-patient decoder sci:sha256:d52ba56c23a69304d837e4ef5a9f3f20bcee59ebf2cd093faea38a0334a9fa4a

    The byte-range EDF reader and wavelet log-power code are adapted from its code/fetch_features.py.

  4. usesdoi:10.18112/openneuro.ds005489.v1.0.3

    The analysis uses FR2 sessions from the version-pinned OpenNeuro ds005489 v1.0.3 dataset (CC0).

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