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. 2021 Nov;90(5):821-833.
doi: 10.1002/ana.26217. Epub 2021 Sep 30.

Thalamic Influence on Slow Wave Slope Renormalization During Sleep

Affiliations

Thalamic Influence on Slow Wave Slope Renormalization During Sleep

Valeria Jaramillo et al. Ann Neurol. 2021 Nov.

Abstract

Objective: Slow waves are thought to mediate an overall reduction in synaptic strength during sleep. The specific contribution of the thalamus to this so-called synaptic renormalization is unknown. Thalamic stroke is associated with daytime sleepiness, along with changes to sleep electroencephalography and cognition, making it a unique "experiment of nature" to assess the relationship between sleep rhythms, synaptic renormalization, and daytime functions.

Methods: Sleep was studied by polysomnography and high-density electroencephalography over 17 nights in patients with thalamic (n = 12) and 15 nights in patients with extrathalamic (n = 11) stroke. Sleep electroencephalographic overnight slow wave slope changes and their relationship with subjective daytime sleepiness, cognition, and other functional tests were assessed.

Results: Thalamic and extrathalamic patients did not differ in terms of age, sleep duration, or apnea-hypopnea index. Conversely, overnight slope changes were reduced in a large cluster of electrodes in thalamic compared to extrathalamic stroke patients. This reduction was related to increased daytime sleepiness. No significant differences were found in other functional tests between the 2 groups.

Interpretation: In patients with thalamic stroke, a reduction in overnight slow wave slope change and increased daytime sleepiness was found. Sleep- and wake-centered mechanisms for this relationship are discussed. Overall, this study suggests a central role of the thalamus in synaptic renormalization. ANN NEUROL 2021;90:821-833.

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Conflict of interest statement

Nothing to report.

Figures

FIGURE 1
FIGURE 1
Magnetic resonance diffusion‐weighted images (b1000 maps) for each included participant. The numbers above the images indicate the patient IDs and correspond to the IDs provided in Table S1.
FIGURE 2
FIGURE 2
Flowchart of excluded patients and nights. The upper part of the figure (ovals) indicates how many participants were excluded and the reasons for exclusion. The lower part indicates how many nights of the included participants were excluded and the reasons for exclusion. NREM = non–rapid eye movement; SW = slow wave.
FIGURE 3
FIGURE 3
Comparison of all night low slow wave activity (SWA) and power in other frequency bands between thalamic and extrathalamic stroke patients. To test significant differences, a linear mixed‐effects model, with low SWA (average across all electrodes in [A] and for each electrode in [B]) as dependent variable, age, stroke, time after stroke, and the interaction between stroke and time after stroke as fixed factors, and patient as random factor, was calculated. (A) Red dots show global SWA (average across all electrodes) for each night recording. The numbers beside the dots indicate the patient IDs and correspond to the IDs provided in Table S1. Black dots and error bars represent the mean and standard deviation for each group. The horizontal bar and asterisk denote a significant difference between thalamic and extrathalamic patients (factor stroke F[1, 19.2] = 6.22, p = 0.022; factor age F[1,19.1] = 11.8, p = 0.003; all other factors and interactions p > 0.5). (B) Topographical representation of standardized β coefficients for the fixed factor stroke. Negative β coefficients (blue) indicate reduced SWA in thalamic compared to extrathalamic patients. Significant electrodes (after statistical nonparametric cluster correction) are indicated with white dots. (C) The same analysis as in B was performed for other frequency bands. Note that no significant electrode cluster was found.
FIGURE 4
FIGURE 4
Comparison of overnight slow wave slope change between thalamic and extrathalamic stroke patients. To test significant differences, a linear mixed‐effects model, with global overnight slope change (average across all electrodes in [A] and for each electrode in [B]) as dependent variable, age, stroke, time after stroke, and the interaction between stroke and time after stroke as fixed factors, and patient as random factor, was calculated. (A) Red dots show global overnight slope change (average across all electrodes) for each night recording. The numbers beside the dots indicate the patient IDs and correspond to the IDs provided in Table S1. Black dots and error bars represent the mean and standard deviation for each group. The horizontal bar and asterisk denote a significant difference between thalamic and extrathalamic patients (factor stroke F[1, 20.9] = 7.03, p = 0.015; all other factors and interactions p > 0.4). (B) Topographical representation of standardized β coefficients for the fixed factor stroke. Negative β coefficients (blue) indicate reduced overnight slope changes in thalamic compared to extrathalamic patients. Significant electrodes (after statistical nonparametric cluster correction) are indicated with white dots.
FIGURE 5
FIGURE 5
Association of overnight slope changes with subjective sleepiness and functional outcome measures across all patients. To test significant associations, linear mixed‐effects models, with the subjective sleepiness or the functional outcome measure tested as dependent variable, age and overnight slope change (average across significant electrode cluster in Fig 4B) as fixed factors, and patient as random factor, were calculated. Negative standardized β coefficients indicate that reduced overnight slope changes were associated with increased subjective sleepiness or reduced performance. Significant associations are indicated with an asterisk (for SSSM: factor slope change F[1, 22] = 6.07, p = 0.022; factor age p > 0.2; for ESS: factor slope change F[1, 19.9] = 4.34, p = 0.050; factor age p > 0.4). Note that CBBS and CBRBS were multiplied by −1 to rate good performance in the same direction as for the other measures. Because for the NIHSS only the acute time point was available, a simple linear model was calculated (no random factor). Residual analysis of the linear mixed‐effects model with RSCD1O as dependent variable indicated that the assumption of normally distributed residuals was violated and these results should be viewed with caution. CBBS = Corsi block‐tapping block span; CBRBS = Corsi block‐tapping reverse block span; ESS = Epworth Sleepiness Scale; NIHSS = National Institutes of Health Stroke Scale; RSCD1O = random shape cancellation difficulty level 1 omissions; RSCD1T = random shape cancellation difficulty level 1 time; RSCD2O = random shape cancellation difficulty level 2 omissions; RSCD2T = random shape cancellation difficulty level 2 time; RSCD3O = random shape cancellation difficulty level 3 omissions; RSCD3T = random shape cancellation difficulty level 3 time; SSSE1 = Stanford Sleepiness Scale in the evening before the motor and neurocognitive test batteries; SSSE2 = Stanford Sleepiness Scale in the evening before patients were allowed to sleep; SSSM = Stanford Sleepiness Scale in the morning before the motor and neurocognitive test batteries.

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