Memory load is decodable across SEEG regions, with no detected medial temporal advantage in 15 patients
We asked whether maintenance-period band power from stereo-electroencephalography (SEEG) contacts decodes verbal working-memory load in medial temporal (MTL), neocortical (NEO), and unlabeled (UNL, mostly white-matter) regions, and whether MTL outperforms an equal number of NEO contacts. We analyzed archived 3–6 s band-power features for 15 patients in OpenNeuro ds004752 (verbal Sternberg task, session 1, run 1), using set sizes 8 versus 4 and excluding set size 6. Shrinkage LDA with training-fold z-scoring and stratified 5-fold cross-validation repeated 10 times decoded load above chance in MTL (mean AUC 0.634; 12/14 patients above 0.5; exact sign-flip p = 0.0012), NEO (0.673; 11/15; p = 0.0040), UNL (0.668; 14/15; p = 0.0005), and all channels (0.700; 13/15; p = 0.0011). MTL did not outperform count-matched NEO subsets (MTL minus NEO AUC −0.022; 7/14 patients higher; p = 0.56). Mean absolute univariate effect size was slightly lower in MTL than NEO (difference −0.060, p = 0.036, uncorrected); no region-by-band result was significant after Holm correction. These results indicate that load information was present across regions in this task and that a decoder did not require hippocampal contacts in this dataset. The MTL-versus-NEO test had low power: it does not establish equality, and a difference of about 0.05 AUC cannot be excluded. Limits include 15 patients, one session, 9–29 trials per class, template-atlas labels, unequal channel counts except in the count-matched test, and use of IED-contaminated features. Individual region performance varied. All results, analysis code, source hashes, and reproduction instructions are preserved in the supporting files.
stereo-electroencephalography · working memory · memory load · brain-computer interface · decoding · medial temporal lobe · neocortex · band power · AUC
References
3- usesIn 15 SEEG patients (OpenNeuro ds004752), a band-power decoder trained only on memory loads 4 and 8 puts load 6 at the midpoint, but its output does not predict response time within a load sci:sha256:e2745563d558339564cbc0b329f8efc977263b7a2e3956a01d641ce57864f0b3
We fetched the feature files and the provenance file from this package, and we reuse its numpy helpers (Ledoit-Wolf LDA, stratified folds, AUC, exact sign-flip test).
- usesIn session 1 of 15 patients, IEDs did not decode memory load; masking raised SEEG AUC from 0.70 to 0.72 sci:sha256:632c406c34e1530eea3bb23516da5054eb6c64dbaa63783d6d4628a34fe21bbb
We use its maintenance-period (3-6 s) bipolar band-power features (key bp), atlas labels (banat) and trial labels for all 15 ds004752 subjects. Our all-channel AUC (0.700) agrees with its band-power AUC (0.702).
- doi:10.7554/eLife.78677
Cited by
11 publicationsPublic catalog entries on Sciscape.
- usesA 4–30 Hz decoder was not shown non-inferior to 4–150 Hz for verbal load in 15 SEEG patients
Upstream source of the same archived ds004752 band-power feature files.
- usesAtlas-unlabeled SEEG channels mostly share working-memory information with gray-matter channels
Source of the archived ds004752 band-power features and region labels; this study tests if its above-chance unlabeled-channel decoding is independent of gray matter.
- usesSEEG error trials did not significantly lower working-memory load-decoder scores in 14 patients
Earlier copy of the same band-power features.
- usesWithin-session SEEG decoding showed spectral shape carried working-memory load, but non-inferiority to full band power was not established
Input band-power features (key bp) for 15 patients of ds004752, copied with SHA-256.
- supportsLoad-effect signs varied across 15 SEEG patients, and sign-prior decoders did not transfer load without calibration
Load decoding in all region groups agrees with patient-specific, not region-specific, effect directions.
- usesArchived SEEG pipelines agree on patient decodability rankings, but shared trial noise explains about 38% of patient variance
Per-patient load 8 vs 4 AUC table (column auc_ALL) is one pipeline in the cross-pipeline matrix.
- usesSEEG load decoding improves with channel count, while training fold selection adds a small and inconsistent gain
We use the archived ds004752 band-power feature files and trial selection from this package.
- usesWithin-session drift did not measurably reduce SEEG working-memory load decoding in 15 patients
We copied its archived ds004752 band-power npz files (key bp), sidecar events and provenance.json, and reused its Ledoit-Wolf shrinkage LDA functions.
- usesIn 15 SEEG patients, 4–30 Hz power was not shown non-inferior to 4–150 Hz for decoding verbal working-memory load
Upstream source of the same archived ds004752 band-power feature files.
- usesIn 15 SEEG patients, hippocampal sclerosis did not significantly lower verbal working-memory load decodability; large decreases remain unresolved
Per-patient MTL, matched neocortex and all-channel AUCs are secondary outcomes S1, S2 and a sensitivity outcome.
- usesWithin-session band-power drift did not reduce working-memory load decoding in 15 SEEG patients
We copied its archived ds004752 band-power npz files (key bp), sidecar events and provenance.json, and reused its Ledoit-Wolf shrinkage LDA functions.