Preliminary evidence of a relationship between sleep spindles and treatment response in epileptic encephalopathy

McLaren, John R.; Luo, Yancheng; Kwon, Hyuk-Min; Shi, Wen; Ma, Kramer; Chu, Catherine J.

doi:10.1002/acn3.51840

Cited by 6 publications

(4 citation statements)

References 58 publications

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“…Frthermore, as it is clearly shown in the epilepsy population, pathology can disturb the sleep architecture in such a way that sleep stages, as such, might not be classifiable anymore. A microstructural approach [presence of spindles (25,26), rapid eye movements (27) or cyclic alternating pattern (28)] or spectral power analysis are then helpful but are often still combined with sleep staging. Complete unsupervised learning could reveal intrinsic patterns, however, translating these patterns into clinically relevant information will be challenging.…”

Section: Discussionmentioning

confidence: 99%

Automated sleep staging on reduced channels in children with epilepsy

Proost,

Heremans,

Lagae

et al. 2024

Front. Neurol.

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ObjectivesThis study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE).MethodsOvernight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG.ResultsIn 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep.ConclusionWe concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.

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Section: Discussionmentioning

confidence: 99%

Automated sleep staging on reduced channels in children with epilepsy

Proost,

Heremans,

Lagae

et al. 2024

Front. Neurol.

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“…Sleep spindle rate has been reported to be decreased in autism, developmental delay, and attention deficit hyperactivity disorder 40,41 and correlate with cognitive abilities in children with dyslexia 42 and epilepsy 8 . Critically, several studies have found that medications and interventions that increase sleep spindles improve memory consolidation, and medications that disrupt spindles impair memory consolidation 9,[43][44][45] , suggesting a mechanistic link between sleep spindles and memory performance. Further supporting sleep spindles as generalizable biomarker for sleep-dependent memory processes across ages and conditions, we find that focal spindle rate predicts sleep dependent memory consolidation on a motor procedural task in healthy children and children with focal epilepsy.…”

Section: Discussionmentioning

confidence: 99%

“…The mechanism of cognitive dysfunction in RE is an area of active research. We recently discovered that epileptiform spikes interfere with sleep spindles, prominent bursts of 9-15 Hz oscillations mechanistically linked to memory consolidation during stage 2 sleep 6,7 , in RE and severe sleep activated epileptic encephalopathies [8][9][10] . In contrast to measures of spike rate, sleep spindle rate predicts IQ, processing speed, and sensorimotor coordination in RE 8 , and changes in spindle rate predict cognitive response to high-dose benzodiazepine treatment in severe epileptic encephalopathies 9 .…”

Section: Introductionmentioning

confidence: 99%

“…We recently discovered that epileptiform spikes interfere with sleep spindles, prominent bursts of 9-15 Hz oscillations mechanistically linked to memory consolidation during stage 2 sleep 6,7 , in RE and severe sleep activated epileptic encephalopathies [8][9][10] . In contrast to measures of spike rate, sleep spindle rate predicts IQ, processing speed, and sensorimotor coordination in RE 8 , and changes in spindle rate predict cognitive response to high-dose benzodiazepine treatment in severe epileptic encephalopathies 9 . Similar findings have subsequently been reported in other epilepsy populations 11,12 , suggesting that sleep spindle deficits may provide a robust, generalizable biomarker for several cognitive symptoms observed in epilepsy.…”

Section: Introductionmentioning

confidence: 99%

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Impaired sleep-dependent memory consolidation predicted by reduced sleep spindles in Rolandic epilepsy

Kwon,

Chinappen,

Kinard

et al. 2024

Preprint

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Background and Objectives: Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in Rolandic epilepsy and related sleep-activated epileptic encephalopathies. We investigate the relationship between sleep spindle deficits and deficient sleep dependent memory consolidation in children with Rolandic epilepsy. Methods: In this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using a validated automated detector. Results: Twenty-three children with Rolandic epilepsy (14 with resolved disease), and 19 age- and sex-matched controls were enrolled. Children with active Rolandic epilepsy had decreased memory consolidation compared to control children (p=0.001, mean percentage reduction: 25.7%, 95% CI [10.3, 41.2]%) and compared to children with resolved Rolandic epilepsy (p=0.007, mean percentage reduction: 21.9%, 95% CI [6.2, 37.6]%). Children with active Rolandic epilepsy had decreased sleep spindle rates in the centrotemporal region compared to controls (p=0.008, mean decrease 2.5 spindles/min, 95% CI [0.7, 4.4] spindles/min). Spindle rate positively predicted sleep-dependent memory consolidation (p=0.004, mean MST improvement of 3.9%, 95% CI [1.3, 6.4]%, for each unit increase in spindles per minute). Discussion: Children with Rolandic epilepsy have a sleep spindle deficit during the active period of disease which predicts deficits in sleep dependent memory consolidation. This finding provides a mechanism and noninvasive biomarker to aid diagnosis and therapeutic discovery for cognitive dysfunction in Rolandic epilepsy and related sleep activated epilepsy syndromes.

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