Multiple classifier systems for automatic sleep scoring in mice

Gao, Vance D.; Turek, Fred W.; Vitaterna, Martha Hotz

doi:10.1016/j.jneumeth.2016.02.016

Cited by 37 publications

(30 citation statements)

References 27 publications

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“…Sleep was recorded continuously throughout the remainder of the experiment. Data were collected using Pinnacle Acquisition software (Pinnacle Technologies), then scored as non-rapid eye movement sleep (NREM), rapid eye movement sleep (REM), or Wake in 10 second epochs using machine learning-assisted sleep scoring software developed in the Turek/Vitaterna laboratory [29]. The initiation of a bout of NREM, REM, or Wake was defined by the occurrence of two consecutive epochs of NREM, REM, or Wake (respectively).…”

Section: Sleep Recording and Analysismentioning

confidence: 99%

Repeated sleep disruption in mice leads to persistent shifts in the fecal microbiome and metabolome

et al. 2020

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It has been established in recent years that the gut microbiome plays a role in health and disease, potentially via alterations in metabolites that influence host physiology. Although sleep disruption and gut dysbiosis have been associated with many of the same diseases, studies investigating the gut microbiome in the context of sleep disruption have yielded inconsistent results, and have not assessed the fecal metabolome. We exposed mice to five days of sleep disruption followed by four days of ad libitum recovery sleep, and assessed the fecal microbiome and fecal metabolome at multiple timepoints using 16S rRNA gene amplicons and untargeted LC-MS/MS mass spectrometry. We found global shifts in both the microbiome and metabolome in the sleep-disrupted group on the second day of recovery sleep, when most sleep parameters had recovered to baseline levels. We observed an increase in the Firmicutes:Bacteroidetes ratio, along with decreases in the genus Lactobacillus, phylum Actinobacteria, and genus Bifidobacterium in sleep-disrupted mice compared to control mice. The latter two taxa remained low at the fourth day post-sleep disruption. We also identified multiple classes of fecal metabolites that were differentially abundant in sleep-disrupted mice,

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Section: Sleep Recording and Analysismentioning

confidence: 99%

Repeated sleep disruption in mice leads to persistent shifts in the fecal microbiome and metabolome

et al. 2020

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“…In a previous study, researchers adopted multiple models and chest computed tomography images to predict whether a lung cancer patient would survive more than 2 years; they compared several machine‐learning models with different feature selection methods to find the most suitable model for their study and found that RF had the highest average AUC. The SVM was the most accurate single classifier in another study on automatic sleep scoring where researchers compared multiple classifier systems with many different single classifiers. The multiple classifier system used in that study surpassed the support vector machine and showed the potential of utilizing a multiple classifier system.…”

Section: Discussionmentioning

confidence: 99%

Predicting chemo‐brain in breast cancer survivors using multiple MRI features and machine‐learning

Chen

Lin

Yeh

et al. 2018

Magnetic Resonance in Med

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Purpose Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo‐brain. In this study, we aimed to construct machine‐learning models to detect the subtle alternations of the brain in postchemotherapy BC patients. Methods Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study. Both groups underwent resting‐state functional MRI and generalized q‐sampling imaging (GQI). Results Logistic regression (LR) with GQI indices in standardized voxel‐wise analysis, LR with mean regional homogeneity in regional summation analysis, decision tree classifier (CART) with generalized fractional anisotropy in voxel‐wise analysis, and XGBoost (XGB) with normalized quantitative anisotropy had formidable performances in classifying subjects into a chemo‐brain group or an HC group. Classifying the brain MRIs of HC and postchemotherapy patients by conducting leave‐one‐out cross‐validation resulted in the highest accuracy of 84%, which was attained by LR, CART, and XGB with multiple feature sets. Conclusions In our study, we constructed the machine‐learning models that were able to identify chemo‐brains from normal brains. We are hopeful that these results will be helpful in clinically tracking chemo‐brains in the future.

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“…EMG signals were high pass filtered at 10 Hz and subjected to a 100 Hz low pass cutoff. EEG and EMG recordings were collected in PAL 8200 sleep recording software (Pinnacle Technology) and scored, using a previously described, multiple classifier, automatic sleep scoring system, into 10-sec epochs as wakefulness, NREM sleep, or REM sleep on the basis of rodent sleep criteria (Gao et al, 2016). Light source for all sleep experiments was a 3000 Kelvin light source at 500 lux.…”

Section: Methodsmentioning

confidence: 99%

Distinct ipRGC subpopulations mediate light's acute and circadian effects on body temperature and sleep

Rupp

Ren

Altimus

et al. 2018

Preprint

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1The light environment greatly impacts human alertness, mood, and cognition by acute 2 regulation of physiology and indirect alignment of circadian rhythms. Both processes 3 require the melanopsin-expressing intrinsically photosensitive retinal ganglion cells 4 (ipRGCs), but the relevant downstream brain areas remain elusive. ipRGCs project 5 widely in the brain, including to the central circadian pacemaker, the suprachiasmatic 6 nucleus (SCN). Here we show that body temperature and sleep responses to light are 7 absent after genetic ablation of all ipRGCs except a subpopulation that projects to the 8 SCN. Furthermore, by chemogenetic activation of the ipRGCs that avoid the SCN, we 9show that these cells are sufficient for acute changes in body temperature. Our results 10 challenge the idea that the SCN is a major relay for the acute effects of light on non-11 image forming behaviors and identify the sensory cells that initiate light's profound 12 effects on body temperature and sleep. 13 14 15 16 1 Main body

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Multiple classifier systems for automatic sleep scoring in mice

Cited by 37 publications

References 27 publications

Repeated sleep disruption in mice leads to persistent shifts in the fecal microbiome and metabolome

Repeated sleep disruption in mice leads to persistent shifts in the fecal microbiome and metabolome

Predicting chemo‐brain in breast cancer survivors using multiple MRI features and machine‐learning

Distinct ipRGC subpopulations mediate light's acute and circadian effects on body temperature and sleep

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