2021
DOI: 10.1093/texcom/tgab019
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Interictal Epileptiform Discharges are Task Dependent and are Associated with Lasting Electrocorticographic Changes

Abstract: The factors that control the occurrence of interictal epileptiform discharges (IEDs) are not well understood. We suspected that this phenomenon reflects an attention-dependent suppression of interictal epileptiform activity. We hypothesized that IEDs would occur less frequently when a subject viewed a task-relevant stimulus compared to viewing a blank screen. Furthermore, IEDs have been shown to impair memory when they occur in certain regions during the encoding or recall phases of a memory task. Although the… Show more

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Cited by 13 publications
(14 citation statements)
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“…Second, what is the relationship between our findings and interictal discharges (IEDs)? Although IEDs are generally rare, their occurrence is correlated with impaired memory 42 , 43 , 44 and they transiently entrain neurons within the MTL. 45 It remains an open question whether the selective impairment of MS but not VS cells can be explained by selective entrainment of these neurons by IEDs.…”
Section: Discussionmentioning
confidence: 99%
“…Second, what is the relationship between our findings and interictal discharges (IEDs)? Although IEDs are generally rare, their occurrence is correlated with impaired memory 42 , 43 , 44 and they transiently entrain neurons within the MTL. 45 It remains an open question whether the selective impairment of MS but not VS cells can be explained by selective entrainment of these neurons by IEDs.…”
Section: Discussionmentioning
confidence: 99%
“…We coregistered postimplant head computed tomographies with preoperative brain MRIs to obtain accurate 3D location data for each electrode 11 (Figure 1), using a rigid-body registration method from Statistical Parametric Mapping 12 toolbox (SPM12, https://www.fil.ion.ucl.ac.uk/spm/). 12-16 After manual segmentation, electrodes were projected to the nearest cortical surface using the iterative closest point algorithm to account for brain shift 17 and translated into Montreal Neurologic Institute (MNI) brain space on a normalized brain model 18 to facilitate comparison of mapping across subjects.…”
Section: Methodsmentioning
confidence: 99%
“…ucl.ac.uk/spm/). [12][13][14][15][16] After manual segmentation, electrodes were projected to the nearest cortical surface using the iterative closest point algorithm to account for brain shift 17 and translated into Montreal Neurologic Institute (MNI) brain space on a normalized brain model 18 to facilitate comparison of mapping across subjects.…”
Section: Electrode Implantation and Localizationmentioning
confidence: 99%
“…The final spectrum was then utilized to calculate the average spectral power within the following frequency bands: delta (2–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (25–40 Hz), and high‐frequency activity (40–100 Hz) 19 . We used a 1‐s gap to measure relevant power changes surrounding an IED, while minimizing IED perturbations from post‐IED slow waves; this was motivated by our previous work, which revealed that the optimal powerband change was in the +1‐ to +2‐s post‐IED window 20 …”
Section: Methodsmentioning
confidence: 99%
“…This method convolved orthogonal Slepian sequences with the preprocessed iEEG, providing periodograms that were used to compute a final spectrum. The final spectrum was then utilized to calculate the average spectral power within the following frequency bands: delta (2-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), gamma (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), and high-frequency activity (40-100 Hz). 19 We used a 1-s gap to measure relevant power changes surrounding an IED, while minimizing IED perturbations from post-IED slow waves; this was F I G U R E 1 Task design and analysis methods.…”
Section: Spectralpoweranalysismentioning
confidence: 99%