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Individual spindle detection and analysis in high-density recordings across the night and in thalamic stroke


Mensen, A; Poryazova, R; Huber, R; Bassetti, C L (2018). Individual spindle detection and analysis in high-density recordings across the night and in thalamic stroke. Scientific Reports, 8:17885.

Abstract

Sleep spindles are thalamocortical oscillations associated with several behavioural and clinical phenomena. In clinical populations, spindle activity has been shown to be reduced in schizophrenia, as well as after thalamic stroke. Automatic spindle detection algorithms present the only feasible way to systematically examine individual spindle characteristics. We took an established algorithm for spindle detection, and adapted it to high-density EEG sleep recordings. To illustrate the detection and analysis procedure, we examined how spindle characteristics changed across the night and introduced a linear mixed model approach applied to individual spindles in adults (n = 9). Next we examined spindle characteristics between a group of paramedian thalamic stroke patients (n = 9) and matched controls. We found a high spindle incidence rate and that, from early to late in the night, individual spindle power increased with the duration and globality of spindles; despite decreases in spindle incidence and peak-to-peak amplitude. In stroke patients, we found that only left-sided damage reduced individual spindle power. Furthermore, reduction was specific to posterior/fast spindles. Altogether, we demonstrate how state-of-the-art spindle detection techniques, applied to high-density recordings, and analysed using advanced statistical approaches can yield novel insights into how both normal and pathological circumstances affect sleep.

Abstract

Sleep spindles are thalamocortical oscillations associated with several behavioural and clinical phenomena. In clinical populations, spindle activity has been shown to be reduced in schizophrenia, as well as after thalamic stroke. Automatic spindle detection algorithms present the only feasible way to systematically examine individual spindle characteristics. We took an established algorithm for spindle detection, and adapted it to high-density EEG sleep recordings. To illustrate the detection and analysis procedure, we examined how spindle characteristics changed across the night and introduced a linear mixed model approach applied to individual spindles in adults (n = 9). Next we examined spindle characteristics between a group of paramedian thalamic stroke patients (n = 9) and matched controls. We found a high spindle incidence rate and that, from early to late in the night, individual spindle power increased with the duration and globality of spindles; despite decreases in spindle incidence and peak-to-peak amplitude. In stroke patients, we found that only left-sided damage reduced individual spindle power. Furthermore, reduction was specific to posterior/fast spindles. Altogether, we demonstrate how state-of-the-art spindle detection techniques, applied to high-density recordings, and analysed using advanced statistical approaches can yield novel insights into how both normal and pathological circumstances affect sleep.

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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Department of Child and Adolescent Psychiatry
04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:14 December 2018
Deposited On:22 Jan 2019 13:59
Last Modified:25 Sep 2019 00:02
Publisher:Nature Publishing Group
ISSN:2045-2322
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41598-018-36327-x
PubMed ID:30552388

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