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
References
4- 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.
- 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.
- 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.
- 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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1 publicationPublic catalog entries on Sciscape.
- supportsSEEG depth-contact N1 latency did not scale with distance in 16 patients, while ECoG latency did
That work found that 55-62% of expert depth N1s are positive. The near-uniform latencies of negative-only automatic N1s at ds004080 depth contacts agree with the conclusion that a negative-only rule is not valid at depth contacts.