Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues

Rayan, Abdelrahman; Agarwal, Anjali; Samanta, Anumita; Severijnen, None Eva; Meij, Jacqueline van der; Genzel, Lisa

doi:10.1111/ejn.15884

Cited by 27 publications

(18 citation statements)

References 86 publications

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“…We adopted visual scoring criteria established in previous studies to assess the electrophysiological recordings 11,32,33 . Each sleep recording from the cohort of 18 rats underwent meticulous annotation, employing a dual‐expert approach for visual scoring in 10‐s segments, with each expert specializing in different aspects of the data.…”

Section: Methodsmentioning

confidence: 99%

Automatic sleep–wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

Shen,

Huai,

Wang

et al. 2024

CNS Neurosci Ther

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AimsSleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time‐consuming and labor‐intensive. In this study, we performed an automatic sleep–wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats.MethodsThe study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep–wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD.ResultsFeatures extracted from occipital ECoG signals were more suitable for constructing sleep–wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep–wake states of rats (average Cohen's kappa: 0.79).ConclusionThis study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep–wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

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Section: Methodsmentioning

confidence: 99%

Automatic sleep–wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

Shen,

Huai,

Wang

et al. 2024

CNS Neurosci Ther

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show abstract

“…Most of the time, the animal will be quietly resting, which might better correspond to wakeful rest or N1 sleep in humans rather than N2 or N3 sleep [170]. As most reactivation studies in rodents do not perform sleep scoring, it is often unclear which state the memory reactivations are recorded from; even when sleep scoring is done, studies rarely distinguish between different NREM sleep stages, but may instead consider all NREM stages as "slow-wave sleep" (SWS) [5].…”

Section: Box 3 Investigating Memory Reactivation In Rodentsmethodolog...mentioning

confidence: 99%

Memory reactivations during sleep: a neural basis of dream experiences?

Picard-Deland

Bernardi

Genzel

et al. 2023

Trends in Cognitive Sciences

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“…Because the assessment of vigilance states is central to basic and clinical studies, a large numbers of automatic scoring methods have been developed, both for human and rodent models (reviewed in (54)). Most approaches rely on a combination of neuronal and movement signals (e.g.…”

Section: Scoring Vigilance States With Nasal Pressure Signalsmentioning

confidence: 99%

Respiratory pauses highlight sleep architecture in mice

Casali,

Miermon,

Terral

et al. 2024

Preprint

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Respiratory patterns share bidirectional links with brain functions: they are modulated by sensory stimuli, attention and emotions, are affected in some cognitive disorders, but they also can influence perception, emotions and cognition. In particular, brain activity undergoes drastic changes when switching between vigilance states, such as transitions between wake and sleep - as do the overall respiratory rate. However, how the fine features of respiration, beyond its rate, accompany these transitions remains unclear. To address this question, we equipped freely-moving mice with both intra-nasal pressure sensors and hippocampus-targeted electrodes. The unprecedented accuracy of the respiratory signals in mice spontaneously alternating between wake, non-rapid-eye-movement (NREM) and REM sleep revealed that periods of respiratory pause with low or no airflow, are interspersed within phases of exhalation and inhalation. If pause durations impacted the respiratory rate in individual states, the effect of pauses differed across sleep and wake. This stemmed from strikingly different patterns across states: mainly pauses after inhalation during wake, mainly after exhalation in REM, and a mixture of both for NREM sleep. We verified that respiratory patterns are distinctive signatures for states by building an accurate machine-learning algorithm relying solely on respiration information for the prediction of vigilance states. Our experiments demonstrated that the information of missing pauses after inhalation and of breathing variability were instrumental to precise REM sleep prediction. Moreover, results highlighted that these vigilance state-respiration relationships can be generalized across animals. In agreement, kinetic indicators for exhalation and inhalation co-varied across states and were spared by animal-to-animal variations, while the duration of pauses after inhalation stood as an isolated, state-discriminant feature. Finally, dynamical analysis revealed that distinct breathing features adapt with different kinetics at the transition time points between different states, possibly accompanying distinct cortical changes. Our work therefore clarifies how different features of respiration, and in particular pauses in nasal airflow, are associated to the specific physiology of individual vigilance states and suggest new links with brain functions.

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Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues

Cited by 27 publications

References 86 publications

Automatic sleep–wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

Automatic sleep–wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

Memory reactivations during sleep: a neural basis of dream experiences?

Respiratory pauses highlight sleep architecture in mice

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