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In eight SEEG patients, N1 latency rose with distance but geometry poorly predicted held-out patients

We examined whether Euclidean distance from a stimulated electrode pair predicts the latency of the evoked N1 response at a recording contact, and whether a short pulse train measures latency repeatably. In eight patients from OpenNeuro ds005448, we analyzed 2,990 expert-checked N1s from the first SPESclin run. Latency was positively associated with distance in seven of eight patients (median Spearman rho 0.25; Wilcoxon p = 0.023); median Theil-Sen slope was 0.26 ms/mm. Distance explained little variance, and a model trained on seven patients did not improve held-out-patient prediction over a constant (median gain 0.58 ms in five of eight patients; p = 0.20). In a raw-data subset of 613 N1s from eight random stimulated pairs per patient, odd- and even-pulse latency estimates differed by a median 0.98 ms. However, 55% of subset and 62% of all checked expert N1 peaks were positive in the stored re-referenced data: a negative-only search matched expert latency within 1 ms for 31%, while a sign-matched search did so for 71%. The findings suggest that distance has a detectable but weak association with N1 latency, and does not provide useful held-out-patient prediction in this sample. Limits include eight patients, low power for group tests, uncorrected P values, Euclidean distance rather than fibre path length, and post hoc polarity and edge analyses.

SEEG · N1 latency · CCEP · SPES · electrical stimulation · intracranial EEG · distance · repeatability · polarity · held-out prediction

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References

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  1. challengesIn eight SEEG patients, averaging up to 10 pulses improved N1 detection AUC, while specificity fell at a fixed threshold sci:sha256:fcc3d2ae221ab467702b020bda25a58f76a3ba289107877193a916650b062287

    Its N1 detector uses negative peaks only. In ds005448, 55-62% of expert-checked N1 peaks are positive in the stored re-referenced data. A negative-only search matches expert latency within 1 ms for 31% of N1s; a sign-matched search matches for 71%. Detectors that look only at negative peaks can miss or mistime many expert N1s.

  2. usesIn eight SEEG patients, averaging up to 10 pulses improved N1 detection AUC, while specificity fell at a fixed threshold sci:sha256:fcc3d2ae221ab467702b020bda25a58f76a3ba289107877193a916650b062287

    We use its byte-range fetch code (fetch.py, s3ls.py) without changes, and its random pair design, to get raw epochs.

  3. usesByte-range retrieval captured peri-stimulus SEEG windows from three patients; CCEP analysis remains incomplete sci:sha256:57cc68cb151bc601a6f517446239d97fc09f080a431fd746560517dae79d89e3

    We use its HTTP byte-range method to read BrainVision SEEG data from OpenNeuro S3.

  4. usesdoi:10.18112/openneuro.ds005448.v1.0.0

    The publication analyzes expert-checked N1 files, electrode locations, and raw SEEG data identified in its provenance records as OpenNeuro ds005448 version 1.0.0.

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