Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring

An Excitatory Circuit in the Perioculomotor Midbrain for Non-REM Sleep Control.

The perioculomotor (pIII) region of the midbrain was postulated as a sleep-regulating center in the 1890s but largely neglected in subsequent studies. Using activity-dependent labeling and gene expression profiling, we identified pIII neurons that promote non-rapid eye movement (NREM) sleep. Optrode recording showed that pIII glutamatergic neurons expressing calcitonin gene-related peptide alpha (CALCA) are NREM-sleep active; optogenetic and chemogenetic activation/inactivation showed that they strongly promote NREM sleep. Within the pIII region, CALCA neurons form reciprocal connections with another population of glutamatergic neurons that express the peptide cholecystokinin (CCK). Activation of CCK neurons also promoted NREM sleep. Both CALCA and CCK neurons project rostrally to the preoptic hypothalamus, whereas CALCA neurons also project caudally to the posterior ventromedial medulla. Activation of each projection increased NREM sleep. Together, these findings point to the pIII region as an excitatory sleep center where different subsets of glutamatergic neurons promote NREM sleep through both local reciprocal connections and long-range projections

Robust, automated sleep scoring by a compact neural network with distributional shift correction

Accurately determining the sleep stage of experimental subjects is a key step in sleep research. Despite years of research into automated methods for scoring rodent sleep recordings, most scoring is still performed manually. Here, I present an automated, machine learning-based sleep scoring method that avoids the subjective and labor-intensive task of manual scoring. In the first chapter, I review recent advances in the field of sleep scoring. New algorithms have, over time, extracted more and more useful information from underlying physiological signals used as inputs. However, inter-laboratory and inter-subject differences have thus far prevented any single automated method from being widely applicable.In the second chapter, I present a feature scaling algorithm, mixture z-scoring, that can eliminate many of these differences. Importantly, it also preserves changes in the amount of time a given subject spends in each sleep stage, which is not attainable using existing algorithms. I then present a neural network architecture which efficiently learns to score sleep from spectrograms of electroencephalogram recordings and evaluate it using a large, high-quality dataset. When mixture z-scoring is used as a preprocessing step, the network achieves state-of-the-art performance. I also introduce a free, open-source software package that allows even novice users to make use of the network and mixture z-scoring. This work is presented in the form of a published, first-author manuscript.In the final chapter, I discuss the limitations of the scoring algorithm and its potential application for scoring data from other species. I also examine some remaining challenges in the field of sleep scoring as well as their possible solutions. As a whole, this work providescomputational tools that are designed to meet the data processing needs of the sleep research community

Robust, automated sleep scoring by a compact neural network with distributional shift correction

Accurately determining the sleep stage of experimental subjects is a key step in sleep research. Despite years of research into automated methods for scoring rodent sleep recordings, most scoring is still performed manually. Here, I present an automated, machine learning-based sleep scoring method that avoids the subjective and labor-intensive task of manual scoring. In the first chapter, I review recent advances in the field of sleep scoring. New algorithms have, over time, extracted more and more useful information from underlying physiological signals used as inputs. However, inter-laboratory and inter-subject differences have thus far prevented any single automated method from being widely applicable.In the second chapter, I present a feature scaling algorithm, mixture z-scoring, that can eliminate many of these differences. Importantly, it also preserves changes in the amount of time a given subject spends in each sleep stage, which is not attainable using existing algorithms. I then present a neural network architecture which efficiently learns to score sleep from spectrograms of electroencephalogram recordings and evaluate it using a large, high-quality dataset. When mixture z-scoring is used as a preprocessing step, the network achieves state-of-the-art performance. I also introduce a free, open-source software package that allows even novice users to make use of the network and mixture z-scoring. This work is presented in the form of a published, first-author manuscript.In the final chapter, I discuss the limitations of the scoring algorithm and its potential application for scoring data from other species. I also examine some remaining challenges in the field of sleep scoring as well as their possible solutions. As a whole, this work providescomputational tools that are designed to meet the data processing needs of the sleep research community

Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring

Sleep Regulation by Neurotensinergic Neurons in a Thalamo-Amygdala Circuit.

A crucial step in understanding the sleep-control mechanism is to identify sleep neurons. Through systematic anatomical screening followed by functional testing, we identified two sleep-promoting neuronal populations along a thalamo-amygdala pathway, both expressing neurotensin (NTS). Rabies-mediated monosynaptic retrograde tracing identified the central nucleus of amygdala (CeA) as a major source of GABAergic inputs to multiple wake-promoting populations; gene profiling revealed NTS as a prominent marker for these CeA neurons. Optogenetic activation and inactivation of NTS-expressing CeA neurons promoted and suppressed non-REM (NREM) sleep, respectively, and optrode recording showed they are sleep active. Further tracing showed that CeA GABAergic NTS neurons are innervated by glutamatergic NTS neurons in a posterior thalamic region, which also promote NREM sleep. CRISPR/Cas9-mediated NTS knockdown in either the thalamic or CeA neurons greatly reduced their sleep-promoting effect. These results reveal a novel thalamo-amygdala circuit for sleep generation in which NTS signaling is essential for both the upstream glutamatergic and downstream GABAergic neurons

An Excitatory Circuit in the Perioculomotor Midbrain for Non-REM Sleep Control.

The perioculomotor (pIII) region of the midbrain was postulated as a sleep-regulating center in the 1890s but largely neglected in subsequent studies. Using activity-dependent labeling and gene expression profiling, we identified pIII neurons that promote non-rapid eye movement (NREM) sleep. Optrode recording showed that pIII glutamatergic neurons expressing calcitonin gene-related peptide alpha (CALCA) are NREM-sleep active; optogenetic and chemogenetic activation/inactivation showed that they strongly promote NREM sleep. Within the pIII region, CALCA neurons form reciprocal connections with another population of glutamatergic neurons that express the peptide cholecystokinin (CCK). Activation of CCK neurons also promoted NREM sleep. Both CALCA and CCK neurons project rostrally to the preoptic hypothalamus, whereas CALCA neurons also project caudally to the posterior ventromedial medulla. Activation of each projection increased NREM sleep. Together, these findings point to the pIII region as an excitatory sleep center where different subsets of glutamatergic neurons promote NREM sleep through both local reciprocal connections and long-range projections

Maladaptive cortical hyperactivity upon recovery from experimental autoimmune encephalomyelitis

Multiple sclerosis (MS) patients exhibit neuropsychological symptoms in early disease despite the immune attack occurring predominantly in white matter and spinal cord. It is unclear why neurodegeneration may start early in the disease and is prominent in later stages. We assessed cortical microcircuit activity by employing spiking-specific two-photon Ca2+ imaging in proteolipid protein-immunized relapsing-remitting SJL/J mice in vivo. We identified the emergence of hyperactive cortical neurons in remission only, independent of direct immune-mediated damage and paralleled by elevated anxiety. High levels of neuronal activity were accompanied by increased caspase-3 expression. Cortical TNFα expression was mainly increased by excitatory neurons in remission; blockade with intraventricular infliximab restored AMPA spontaneous excitatory postsynaptic current frequencies, completely recovered normal neuronal network activity patterns and alleviated elevated anxiety. This suggests a dysregulation of cortical networks attempting to achieve functional compensation by synaptic plasticity mechanisms, indicating a link between immune attack and early start of neurodegeneration