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Di T, Zhang L, Meng S, Liu W, Guo Y, Zheng E, Xie C, Xiang S, Jia T, Lu L, Sun Y, Shi J. The impact of REM sleep loss on human brain connectivity. Transl Psychiatry 2024; 14:270. [PMID: 38956035 PMCID: PMC11219886 DOI: 10.1038/s41398-024-02985-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/04/2024] Open
Abstract
Brain function is vulnerable to the consequences of inadequate sleep, an adverse trend that is increasingly prevalent. The REM sleep phase has been implicated in coordinating various brain structures and is hypothesized to have potential links to brain variability. However, traditional imaging research have encountered challenges in attributing specific brain region activity to REM sleep, remained understudied at the whole-brain connectivity level. Through the spilt-night paradigm, distinct patterns of REM sleep phases were observed among the full-night sleep group (n = 36), the early-night deprivation group (n = 41), and the late-night deprivation group (n = 36). We employed connectome-based predictive modeling (CPM) to delineate the effects of REM sleep deprivation on the functional connectivity of the brain (REM connectome) during its resting state. The REM sleep-brain connectome was characterized by stronger connectivity within the default mode network (DMN) and between the DMN and visual networks, while fewer predictive edges were observed. Notably, connections such as those between the cingulo-opercular network (CON) and the auditory network, as well as between the subcortex and visual networks, also made significant contributions. These findings elucidate the neural signatures of REM sleep loss and reveal common connectivity patterns across individuals, validated at the group level.
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Affiliation(s)
- Tianqi Di
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
- Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191, China
| | - Libo Zhang
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
- Shenzhen Key Laboratory for Drug Addiction and Medication Safety, Shenzhen Public Service Platform for Clinical Application of Medical Imaging, Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen-PKU-HKUST Medical Center, Shenzhen, 518036, China
| | - Shiqiu Meng
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
- Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191, China
| | - Wangyue Liu
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
| | - Yang Guo
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
| | - Enyu Zheng
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Lin Lu
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China
| | - Yan Sun
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China.
- Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191, China.
| | - Jie Shi
- Department of Pharmacology, School of Basic Medical Sciences, National Institute on Drug Dependence, Peking University, Beijing, 100191, China.
- Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191, China.
- The Key Laboratory for Neuroscience of the Ministry of Education and Health, Peking University, Beijing, 100191, China.
- The State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, 100191, China.
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang, 453000, China.
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Lemke SM, Celotto M, Maffulli R, Ganguly K, Panzeri S. Information flow between motor cortex and striatum reverses during skill learning. Curr Biol 2024; 34:1831-1843.e7. [PMID: 38604168 PMCID: PMC11078609 DOI: 10.1016/j.cub.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/22/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
The coordination of neural activity across brain areas during a specific behavior is often interpreted as neural communication involved in controlling the behavior. However, whether information relevant to the behavior is actually transferred between areas is often untested. Here, we used information-theoretic tools to quantify how motor cortex and striatum encode and exchange behaviorally relevant information about specific reach-to-grasp movement features during skill learning in rats. We found a temporal shift in the encoding of behaviorally relevant information during skill learning, as well as a reversal in the primary direction of behaviorally relevant information flow, from cortex-to-striatum during naive movements to striatum-to-cortex during skilled movements. Standard analytical methods that quantify the evolution of overall neural activity during learning-such as changes in neural signal amplitude or the overall exchange of information between areas-failed to capture these behaviorally relevant information dynamics. Using these standard methods, we instead found a consistent coactivation of overall neural signals during movement production and a bidirectional increase in overall information propagation between areas during learning. Our results show that skill learning is achieved through a transformation in how behaviorally relevant information is routed across cortical and subcortical brain areas and that isolating the components of neural activity relevant to and informative about behavior is critical to uncover directional interactions within a coactive and coordinated network.
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Affiliation(s)
- Stefan M Lemke
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy; Neurology Service, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121, USA; Department of Neurology, University of California, San Francisco, 1700 Owens Street, San Francisco, CA 94158, USA; Neuroscience Center, University of North Carolina, Chapel Hill, 116 Manning Drive, Chapel Hill, NC 27599, USA.
| | - Marco Celotto
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Via Irnerio 48, 40126 Bologna, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany
| | - Roberto Maffulli
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy
| | - Karunesh Ganguly
- Neurology Service, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121, USA; Department of Neurology, University of California, San Francisco, 1700 Owens Street, San Francisco, CA 94158, USA
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany.
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3
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Chen P, Tang G, Wang Y, Xiong W, Deng Y, Fei S, Zhang J. Spontaneous brain activity in the hippocampal regions could characterize cognitive impairment in patients with Parkinson's disease. CNS Neurosci Ther 2024; 30:e14706. [PMID: 38584347 PMCID: PMC10999557 DOI: 10.1111/cns.14706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/19/2024] [Accepted: 03/15/2024] [Indexed: 04/09/2024] Open
Abstract
OBJECTIVE This study aimed to investigate whether spontaneous brain activity can be used as a prospective indicator to identify cognitive impairment in patients with Parkinson's disease (PD). METHODS Resting-state functional magnetic resonance imaging (RS-fMRI) was performed on PD patients. The cognitive level of patients was assessed by the Montreal Cognitive Assessment (MoCA) scale. The fractional amplitude of low-frequency fluctuation (fALFF) was applied to measure the strength of spontaneous brain activity. Correlation analysis and between-group comparisons of fMRI data were conducted using Rest 1.8. By overlaying cognitively characterized brain regions and defining regions of interest (ROIs) based on their spatial distribution for subsequent cognitive stratification studies. RESULTS A total of 58 PD patients were enrolled in this study. They were divided into three groups: normal cognition (NC) group (27 patients, average MoCA was 27.96), mild cognitive impairment (MCI) group (21 patients, average MoCA was 23.52), and severe cognitive impairment (SCI) group (10 patients, average MoCA was 17.3). It is noteworthy to mention that those within the SCI group exhibited the most advanced chronological age, with an average of 74.4 years, whereas the MCI group displayed a higher prevalence of male participants at 85.7%. It was found hippocampal regions were a stable representative brain region of cognition according to the correlation analysis between the fALFF of the whole brain and cognition, and the comparison of fALFF between different cognitive groups. The parahippocampal gyrus was the only region with statistically significant differences in fALFF among the three cognitive groups, and it was also the only brain region to identify MCI from NC, with an AUC of 0.673. The paracentral lobule, postcentral gyrus was the region that identified SCI from NC, with an AUC of 0.941. The midbrain, hippocampus, and parahippocampa gyrus was the region that identified SCI from MCI, with an AUC of 0.926. CONCLUSION The parahippocampal gyrus was the potential brain region for recognizing cognitive impairment in PD, specifically for identifying MCI. Thus, the fALFF of parahippocampal gyrus is expected to contribute to future study as a multimodal fingerprint for early warning.
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Affiliation(s)
- Peng Chen
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Guoqiang Tang
- Pre‐hospital Emergency Department, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Yanglingxi Wang
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Weiming Xiong
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - Yongbing Deng
- Department of Neurosurgery, Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical CenterChongqing University Central HospitalChongqingChina
| | - She Fei
- Department of EmergencyThe Fourth Medical Center of the Chinese PLA General HospitalBeijingChina
| | - Jun Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
- Department of NeurosurgeryClinical Medical College of Yangzhou UniversityYangzhouChina
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Cabrera Y, Koymans KJ, Poe GR, Kessels HW, Van Someren EJW, Wassing R. Overnight neuronal plasticity and adaptation to emotional distress. Nat Rev Neurosci 2024; 25:253-271. [PMID: 38443627 DOI: 10.1038/s41583-024-00799-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2024] [Indexed: 03/07/2024]
Abstract
Expressions such as 'sleep on it' refer to the resolution of distressing experiences across a night of sound sleep. Sleep is an active state during which the brain reorganizes the synaptic connections that form memories. This Perspective proposes a model of how sleep modifies emotional memory traces. Sleep-dependent reorganization occurs through neurophysiological events in neurochemical contexts that determine the fates of synapses to grow, to survive or to be pruned. We discuss how low levels of acetylcholine during non-rapid eye movement sleep and low levels of noradrenaline during rapid eye movement sleep provide a unique window of opportunity for plasticity in neuronal representations of emotional memories that resolves the associated distress. We integrate sleep-facilitated adaptation over three levels: experience and behaviour, neuronal circuits, and synaptic events. The model generates testable hypotheses for how failed sleep-dependent adaptation to emotional distress is key to mental disorders, notably disorders of anxiety, depression and post-traumatic stress with the common aetiology of insomnia.
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Affiliation(s)
- Yesenia Cabrera
- Department of Integrative Biology and Physiology, Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Karin J Koymans
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Gina R Poe
- Department of Integrative Biology and Physiology, Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Helmut W Kessels
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
- Department of Synaptic Plasticity and Behaviour, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Society for Arts and Sciences, Amsterdam, Netherlands
| | - Eus J W Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Society for Arts and Sciences, Amsterdam, Netherlands
- Department of Integrative Neurophysiology and Psychiatry, VU University, Amsterdam UMC, Amsterdam, Netherlands
- Center for Neurogenomics and Cognitive Research, VU University, Amsterdam UMC, Amsterdam, Netherlands
| | - Rick Wassing
- Sleep and Circadian Research, Woolcock Institute of Medical Research, Macquarie University, Sydney, New South Wales, Australia.
- School of Psychological Sciences, Faculty of Medicine Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
- Sydney Local Health District, Sydney, New South Wales, Australia.
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Hong J, Lozano DE, Beier KT, Chung S, Weber F. Prefrontal cortical regulation of REM sleep. Nat Neurosci 2023; 26:1820-1832. [PMID: 37735498 DOI: 10.1038/s41593-023-01398-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/28/2023] [Indexed: 09/23/2023]
Abstract
Rapid eye movement (REM) sleep is accompanied by intense cortical activity, underlying its wake-like electroencephalogram. The neural activity inducing REM sleep is thought to originate from subcortical circuits in brainstem and hypothalamus. However, whether cortical neurons can also trigger REM sleep has remained unknown. Here we show in mice that the medial prefrontal cortex (mPFC) strongly promotes REM sleep. Bidirectional optogenetic manipulations demonstrate that excitatory mPFC neurons promote REM sleep through their projections to the lateral hypothalamus and regulate phasic events, reflected in accelerated electroencephalogram theta oscillations and increased eye movement density during REM sleep. Calcium imaging reveals that the majority of lateral hypothalamus-projecting mPFC neurons are maximally activated during REM sleep and a subpopulation is recruited during phasic theta accelerations. Our results delineate a cortico-hypothalamic circuit for the top-down control of REM sleep and identify a critical role of the mPFC in regulating phasic events during REM sleep.
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Affiliation(s)
- Jiso Hong
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - David E Lozano
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin T Beier
- Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Shinjae Chung
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Franz Weber
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Gao JX, Yan G, Li XX, Xie JF, Spruyt K, Shao YF, Hou YP. The Ponto-Geniculo-Occipital (PGO) Waves in Dreaming: An Overview. Brain Sci 2023; 13:1350. [PMID: 37759951 PMCID: PMC10526299 DOI: 10.3390/brainsci13091350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
Rapid eye movement (REM) sleep is the main sleep correlate of dreaming. Ponto-geniculo-occipital (PGO) waves are a signature of REM sleep. They represent the physiological mechanism of REM sleep that specifically limits the processing of external information. PGO waves look just like a message sent from the pons to the lateral geniculate nucleus of the visual thalamus, the occipital cortex, and other areas of the brain. The dedicated visual pathway of PGO waves can be interpreted by the brain as visual information, leading to the visual hallucinosis of dreams. PGO waves are considered to be both a reflection of REM sleep brain activity and causal to dreams due to their stimulation of the cortex. In this review, we summarize the role of PGO waves in potential neural circuits of two major theories, i.e., (1) dreams are generated by the activation of neural activity in the brainstem; (2) PGO waves signaling to the cortex. In addition, the potential physiological functions during REM sleep dreams, such as memory consolidation, unlearning, and brain development and plasticity and mood regulation, are discussed. It is hoped that our review will support and encourage research into the phenomenon of human PGO waves and their possible functions in dreaming.
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Affiliation(s)
- Jin-Xian Gao
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Departments of Neuroscience, Anatomy, Histology, and Embryology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; (J.-X.G.); (G.Y.); (X.-X.L.); (J.-F.X.)
| | - Guizhong Yan
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Departments of Neuroscience, Anatomy, Histology, and Embryology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; (J.-X.G.); (G.Y.); (X.-X.L.); (J.-F.X.)
| | - Xin-Xuan Li
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Departments of Neuroscience, Anatomy, Histology, and Embryology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; (J.-X.G.); (G.Y.); (X.-X.L.); (J.-F.X.)
| | - Jun-Fan Xie
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Departments of Neuroscience, Anatomy, Histology, and Embryology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; (J.-X.G.); (G.Y.); (X.-X.L.); (J.-F.X.)
| | - Karen Spruyt
- NeuroDiderot-INSERM, Université de Paris, 75019 Paris, France;
| | - Yu-Feng Shao
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Departments of Neuroscience, Anatomy, Histology, and Embryology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; (J.-X.G.); (G.Y.); (X.-X.L.); (J.-F.X.)
| | - Yi-Ping Hou
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, Departments of Neuroscience, Anatomy, Histology, and Embryology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; (J.-X.G.); (G.Y.); (X.-X.L.); (J.-F.X.)
- Sleep Medicine Center of Gansu Provincial Hospital, Lanzhou 730000, China
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7
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Tsunematsu T, Matsumoto S, Merkler M, Sakata S. Pontine Waves Accompanied by Short Hippocampal Sharp Wave-Ripples During Non-rapid Eye Movement Sleep. Sleep 2023; 46:zsad193. [PMID: 37478470 PMCID: PMC10485565 DOI: 10.1093/sleep/zsad193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/28/2023] [Indexed: 07/23/2023] Open
Abstract
Ponto-geniculo-occipital or pontine (P) waves have long been recognized as an electrophysiological signature of rapid eye movement (REM) sleep. However, P-waves can be observed not just during REM sleep, but also during non-REM (NREM) sleep. Recent studies have uncovered that P-waves are functionally coupled with hippocampal sharp wave ripples (SWRs) during NREM sleep. However, it remains unclear to what extent P-waves during NREM sleep share their characteristics with P-waves during REM sleep and how the functional coupling to P-waves modulates SWRs. Here, we address these issues by performing multiple types of electrophysiological recordings and fiber photometry in both sexes of mice. P-waves during NREM sleep share their waveform shapes and local neural ensemble dynamics at a short (~100 milliseconds) timescale with their REM sleep counterparts. However, the dynamics of mesopontine cholinergic neurons are distinct at a longer (~10 seconds) timescale: although P-waves are accompanied by cholinergic transients, the cholinergic tone gradually reduces before P-wave genesis during NREM sleep. While P-waves are coupled to hippocampal theta rhythms during REM sleep, P-waves during NREM sleep are accompanied by a rapid reduction in hippocampal ripple power. SWRs coupled with P-waves are short-lived and hippocampal neural firing is also reduced after P-waves. These results demonstrate that P-waves are part of coordinated sleep-related activity by functionally coupling with hippocampal ensembles in a state-dependent manner.
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Affiliation(s)
- Tomomi Tsunematsu
- Department of Integrative Life Sciences, Graduate School of Life Sciences, Tohoku University, Sendai, Japan
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai 980-, Japan
| | - Sumire Matsumoto
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai 980-, Japan
| | - Mirna Merkler
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Shuzo Sakata
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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8
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Kostka JK, Hanganu-Opatz IL. Olfactory-driven beta band entrainment of limbic circuitry during neonatal development. J Physiol 2023; 601:3605-3630. [PMID: 37434507 DOI: 10.1113/jp284401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/22/2023] [Indexed: 07/13/2023] Open
Abstract
Cognitive processing relies on the functional refinement of the limbic circuitry during the first two weeks of life. During this developmental period, when the auditory, somatosensory and visual systems are still largely immature, the sense of olfaction acts as 'door to the world', providing an important source of environmental inputs. However, it is unknown whether early olfactory processing shapes the activity in the limbic circuitry during neonatal development. Here, we address this question by combining simultaneous in vivo recordings from the olfactory bulb (OB), lateral entorhinal cortex (LEC), hippocampus (HP) and prefrontal cortex (PFC) with olfactory stimulation as well as opto- and chemogenetic manipulations of mitral/tufted cells in the OB of non-anaesthetized neonatal mice of both sexes. We show that the neonatal OB synchronizes the limbic circuity in the beta frequency range. Moreover, it drives neuronal and network activity in LEC, as well as subsequently, HP and PFC via long-range projections from mitral cells to HP-projecting LEC neurons. Thus, OB activity shapes the communication within limbic circuits during neonatal development. KEY POINTS: During early postnatal development, oscillatory activity in the olfactory bulb synchronizes the limbic circuit. Olfactory stimulation boosts firing and beta synchronization along the olfactory bulb-lateral entorhinal cortex-hippocampal-prefrontal pathway. Mitral cells drive neuronal and network activity in the lateral entorhinal cortex (LEC), as well as subsequently, the hippocampus (HP) and the prefrontal cortex (PFC) via long-range projections from mitral cells to HP-projecting LEC neurons. Inhibition of vesicle release on LEC targeting mitral cell axons reveals direct involvement of LEC in the olfactory bulb-driven oscillatory entrainment of the limbic circuitry.
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Affiliation(s)
- Johanna K Kostka
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience (HCNS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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9
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Castillo PR. Clinical Neurobiology of Sleep and Wakefulness. Continuum (Minneap Minn) 2023; 29:1016-1030. [PMID: 37590820 DOI: 10.1212/con.0000000000001260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
OBJECTIVE This article focuses on novel neuronal mechanisms of sleep and wakefulness and relates basic science developments with potential translational implications in circadian neurobiology, pharmacology, behavioral factors, and the recently integrated potential pathways of sleep-related motor inhibition. LATEST DEVELOPMENTS During the past decade, remarkable advances in the molecular biology of sleep and wakefulness have taken place, opening a promising path for the understanding of clinical sleep disorders. Newly gained insights include the role of astrocytes in sleep brain homeostasis through the glymphatic system, the promotion of memory consolidation during states of reduced cholinergic activity during slow wave sleep, and the differential functions of melatonin receptors involving regulation of both circadian rhythm and sleep initiation. Ongoing investigations exploring sleep and circadian rhythm disruptions are beginning to unlock pathophysiologic aspects of neurologic, psychiatric, and medical disorders. ESSENTIAL POINTS An understanding of sleep and circadian neurobiology provides coherent and biologically credible approaches to treatments, including the identification of potential targets for neuromodulation.
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10
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Chen B, Tan H, Ding M, Liu L, Wang S, Peng X, Tian H, Jiang J, Gao J, Huang W, Li H, Ye Y, Wang F, Wilson DA, Tu Y, Peng F. Nanorobot-Mediated Synchronized Neuron Activation. ACS NANO 2023; 17:13826-13839. [PMID: 37449804 DOI: 10.1021/acsnano.3c03575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Interactions between active materials lead to collective behavior and even intelligence beyond the capability of individuals. Such behaviors are prevalent in nature and can be observed in animal colonies, providing these species with diverse capacities for communication and cooperation. In artificial systems, however, collective intelligence systems interacting with biological entities remains unexplored. Herein, we describe black (B)-TiO2@N/Au nanorobots interacting through photocatalytic pure water splitting-induced electrophoresis that exhibit periodic swarming oscillations under programmed near-infrared light. The periodic chemical-electric field generated by the oscillating B-TiO2@N/Au nanorobot swarm leads to local neuron activation in vitro. The field oscillations and neurotransmission from synchronized neurons further trigger the resonance oscillation of neuron populations without synaptic contact (about 2 mm spacing), in different ways from normal neuron oscillation requiring direct contact. We envision that the oscillating nanorobot swarm platforms will shed light on contactless communication of neurons and offer tools to explore interactions between neurons.
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Affiliation(s)
- Bin Chen
- School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Haixin Tan
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Miaomiao Ding
- School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
| | - Lu Liu
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shuanghu Wang
- The Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People's Hospital of Lishui, Lishui 323020, China
| | - Xiuyun Peng
- The Laboratory of Clinical Pharmacy, The Sixth Affiliated Hospital of Wenzhou Medical University, The People's Hospital of Lishui, Lishui 323020, China
| | - Hao Tian
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiamiao Jiang
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Junbin Gao
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Weichang Huang
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Huaan Li
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yicheng Ye
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Fei Wang
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Daniela A Wilson
- Institute for Molecules and Materials, Radboud University, Nijmegen, 6525 AJ, The Netherland
| | - Yingfeng Tu
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Fei Peng
- School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
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11
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Schott AL, Baik J, Chung S, Weber F. A medullary hub for controlling REM sleep and pontine waves. Nat Commun 2023; 14:3922. [PMID: 37400467 DOI: 10.1038/s41467-023-39496-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/07/2023] [Indexed: 07/05/2023] Open
Abstract
Rapid-eye-movement (REM) sleep is a distinct behavioral state associated with vivid dreaming and memory processing. Phasic bursts of electrical activity, measurable as spike-like pontine (P)-waves, are a hallmark of REM sleep implicated in memory consolidation. However, the brainstem circuits regulating P-waves, and their interactions with circuits generating REM sleep, remain largely unknown. Here, we show that an excitatory population of dorsomedial medulla (dmM) neurons expressing corticotropin-releasing-hormone (CRH) regulates both REM sleep and P-waves in mice. Calcium imaging showed that dmM CRH neurons are selectively activated during REM sleep and recruited during P-waves, and opto- and chemogenetic experiments revealed that this population promotes REM sleep. Chemogenetic manipulation also induced prolonged changes in P-wave frequency, while brief optogenetic activation reliably triggered P-waves along with transiently accelerated theta oscillations in the electroencephalogram (EEG). Together, these findings anatomically and functionally delineate a common medullary hub for the regulation of both REM sleep and P-waves.
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Affiliation(s)
- Amanda L Schott
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Justin Baik
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Shinjae Chung
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Franz Weber
- Department of Neuroscience, Perelman School of Medicine, Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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12
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Brodt S, Inostroza M, Niethard N, Born J. Sleep-A brain-state serving systems memory consolidation. Neuron 2023; 111:1050-1075. [PMID: 37023710 DOI: 10.1016/j.neuron.2023.03.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 04/08/2023]
Abstract
Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.
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Affiliation(s)
- Svenja Brodt
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany; Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
| | - Marion Inostroza
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Niels Niethard
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Jan Born
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany; Werner Reichert Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
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13
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Chen ZS, Wilson MA. How our understanding of memory replay evolves. J Neurophysiol 2023; 129:552-580. [PMID: 36752404 PMCID: PMC9988534 DOI: 10.1152/jn.00454.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, United States
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, New York, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, New York, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York, United States
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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14
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BaHammam AS, Pirzada AR, Pandi-Perumal SR. Neurocognitive, mood changes, and sleepiness in patients with REM-predominant obstructive sleep apnea. Sleep Breath 2023; 27:57-66. [PMID: 35318576 DOI: 10.1007/s11325-022-02602-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/13/2022] [Accepted: 03/17/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE This article focuses on recent evidence linking rapid eye movement (REM) obstructive sleep apnea (OSA) (REM-OSA) to neurocognitive dysfunction and mood changes; the proposed mechanisms for increased risk of neurocognitive dysfunction in REM-OSA, and future research prospects. METHODS PubMed and Google Scholar records were examined for articles utilizing pre-defined keywords. In this work, we mainly included studies published after 2017; nevertheless, critical studies published prior to 2017 were considered. RESULTS REM-OSA is an under-recognized stage-related sleep-disordered breathing in which obstructive respiratory events happen chiefly in stage REM. The disorder is commonly seen amongst younger patients and females and has recently been linked to cardiometabolic complications. Although less symptomatic than non-REM-OSA and non-stage-specific OSA, current findings indicate that REM-OSA may have neurocognitive repercussions and mood changes and could be linked to insomnia, increased dreams, and nightmares. CONCLUSION Currently available evidence indicates that REM-OSA may present with insomnia and nightmares and could affect cognitive function and mood.
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Affiliation(s)
- Ahmed S BaHammam
- Department of Medicine, The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia. .,Strategic Technologies Program of the National Plan for Sciences and Technology and Innovation in the Kingdom of Saudi, Arabia (08-MED511-02), Riyadh, Saudi Arabia.
| | - Abdul Rouf Pirzada
- Department of Medicine, The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia.,North Cumbria Integrated Care (NCIC), NHS, Carlisle, UK
| | - Seithikurippu R Pandi-Perumal
- Department of Medicine, The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia.,Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
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15
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He H, Guan H, McHugh TJ. The expanded circuitry of hippocampal ripples and replay. Neurosci Res 2022; 189:13-19. [PMID: 36572253 DOI: 10.1016/j.neures.2022.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 12/01/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
The place cells and well-defined oscillatory population rhythms of the rodent hippocampus have served as a powerful model system in linking cells and circuits to memory function. While the initial three decades of place cell research primarily focused on the activity of neurons during exploration, the last twenty-five years have seen growing interest in the physiology of the hippocampus at rest. During slow-wave sleep and quiet wakefulness the hippocampus exhibits sharp-wave ripples (SWRs), short high-frequency, high-amplitude oscillations, that organize the reactivation or 'replay' of sequences of place cells, and interventions that disrupt SWRs impair learning. While the canonical model of SWRs generation have emphasized CA3 input to CA1 as the source of excitatory drive, recent work suggests there are multiple circuits, including the CA2 region, that can both influence, generate and organize SWRs, both from the oscillatory and information content perspectives in a task and state-dependent manner. This extended circuitry and its function must be considered for a true understanding of the role of the hippocampus in off-line processes such as planning and consolidation.
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Affiliation(s)
- Hongshen He
- Laboratory for Circuit & Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, Japan
| | - Hefei Guan
- Laboratory for Circuit & Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, Japan
| | - Thomas J McHugh
- Laboratory for Circuit & Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, Japan.
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16
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Tsunematsu T. What are the neural mechanisms and physiological functions of dreams? Neurosci Res 2022; 189:54-59. [PMID: 36572252 DOI: 10.1016/j.neures.2022.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 12/14/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Dreams are mental experiences, including perceptions, thoughts, and emotions, that occur during sleep. In dreams, hallucinatory perceptions, particularly visual and motoric, are often accompanied by negative emotions. When people dream, they perceive them as real even though they are bizarre and distorted in time and space. People often cannot recall their dreams, even though people dream every night. Dreaming is a strange physiological phenomenon. Research has demonstrated that dreaming is closely associated with rapid eye movement (REM) sleep. It is known that dreaming also occurs during non-REM (NREM) sleep, but the content appears to be different. Dreams during REM sleep tend to be longer, more vivid, more story-like, and more bizarre than those during NREM sleep. In this review, the neural circuits underlying dreaming and the physiological functions associated with it are summarized. Two major theories have been proposed regarding the neural circuits involved in dreaming. One is that dreams are generated by the activation of neural activity in the brainstem and its signal transmission to the cortex. The other is that dreams are caused by forebrain activation by dopamine. Whereas the physiological function of dreams remains unclear, several hypotheses have been proposed that are associated with memory and emotions.
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Affiliation(s)
- Tomomi Tsunematsu
- Department of Integrative Life Sciences, Graduate School of Life Sciences, Tohoku University, Sendai 980-8577, Japan; Creative Interdisciplinary Research Division, Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai 980-8578, Japan.
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17
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Wang Z, Fei X, Liu X, Wang Y, Hu Y, Peng W, Wang YW, Zhang S, Xu M. REM sleep is associated with distinct global cortical dynamics and controlled by occipital cortex. Nat Commun 2022; 13:6896. [PMID: 36371399 PMCID: PMC9653484 DOI: 10.1038/s41467-022-34720-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022] Open
Abstract
The cerebral cortex is spontaneously active during sleep, yet it is unclear how this global cortical activity is spatiotemporally organized, and whether such activity not only reflects sleep states but also contributes to sleep state switching. Here we report that cortex-wide calcium imaging in mice revealed distinct sleep stage-dependent spatiotemporal patterns of global cortical activity, and modulation of such patterns could regulate sleep state switching. In particular, elevated activation in the occipital cortical regions (including the retrosplenial cortex and visual areas) became dominant during rapid-eye-movement (REM) sleep. Furthermore, such pontogeniculooccipital (PGO) wave-like activity was associated with transitions to REM sleep, and optogenetic inhibition of occipital activity strongly promoted deep sleep by suppressing the NREM-to-REM transition. Thus, whereas subcortical networks are critical for initiating and maintaining sleep and wakefulness states, distinct global cortical activity also plays an active role in controlling sleep states.
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Affiliation(s)
- Ziyue Wang
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China ,grid.16821.3c0000 0004 0368 8293Collaborative Innovation Center for Brain Science, Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, 200025 Shanghai, China
| | - Xiang Fei
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Xiaotong Liu
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Yanjie Wang
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China ,grid.16821.3c0000 0004 0368 8293Collaborative Innovation Center for Brain Science, Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, 200025 Shanghai, China
| | - Yue Hu
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China ,grid.8547.e0000 0001 0125 2443Department of Anesthesiology, Huashan Hospital, Fudan University, 200040 Shanghai, China
| | - Wanling Peng
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China
| | - Ying-wei Wang
- grid.8547.e0000 0001 0125 2443Department of Anesthesiology, Huashan Hospital, Fudan University, 200040 Shanghai, China
| | - Siyu Zhang
- grid.16821.3c0000 0004 0368 8293Collaborative Innovation Center for Brain Science, Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, 200025 Shanghai, China
| | - Min Xu
- grid.9227.e0000000119573309Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 200031 Shanghai, China ,grid.511008.dShanghai Center for Brain Science and Brain-Inspired Intelligence Technology, 201210 Shanghai, China
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18
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Gambino G, Bhik-Ghanie R, Giglia G, Puig MV, Ramirez-Villegas J, Zaldivar D. Editorial: Neuromodulatory ascending systems: Their influence at the microscopic and macroscopic levels. Front Neural Circuits 2022; 16:1028154. [DOI: 10.3389/fncir.2022.1028154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
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19
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Gelegen C, Cash D, Ilic K, Sander M, Kim E, Simmons C, Bernanos M, Lama J, Randall K, Brown JT, Kalanj-Bognar S, Cooke S, Ray Chaudhuri K, Ballard C, Francis P, Rosenzweig I. Relevance of sleep and associated structural changes in GBA1 mouse to human rapid eye movement behavior disorder. Sci Rep 2022; 12:7973. [PMID: 35562385 PMCID: PMC9105586 DOI: 10.1038/s41598-022-11516-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/22/2022] [Indexed: 11/29/2022] Open
Abstract
Rapid eye movement (REM) sleep behaviour disorder (RBD) is a REM parasomnia that often predicts the later occurrence of alpha-synucleinopathies. Variants in the gene encoding for the lysosomal enzyme glucocerebrosidase, GBA, strongly increase the risk of RBD. In a GBA1-mouse model recently shown to mimic prodromal stages of α-synucleinopathy, we now demonstrate striking REM and NREM electroencephalographic sleep abnormalities accompanied by distinct structural changes in the more widespread sleep neurocircuitry.
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Affiliation(s)
- Cigdem Gelegen
- Department of Neuroimaging, Sleep and Brain Plasticity Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), De Crespigny Park, Box 089, London, SE5 8AF, UK
- Basic and Clinical Neuroscience, IoPPN, KCL, London, UK
| | - Diana Cash
- Department of Neuroimaging, Sleep and Brain Plasticity Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), De Crespigny Park, Box 089, London, SE5 8AF, UK
- BRAIN, Department of Neuroimaging, KCL, London, UK
| | - Katarina Ilic
- Department of Neuroimaging, Sleep and Brain Plasticity Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), De Crespigny Park, Box 089, London, SE5 8AF, UK
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Millie Sander
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Eugene Kim
- BRAIN, Department of Neuroimaging, KCL, London, UK
| | | | | | - Joana Lama
- Institute of Psychiatry, Psychology and Neuroscience, Wolfson Centre for Age-Related Diseases, Guy's Campus, KCL, London, UK
| | | | - Jonathan T Brown
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Svjetlana Kalanj-Bognar
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Samuel Cooke
- Basic and Clinical Neuroscience, IoPPN, KCL, London, UK
| | - K Ray Chaudhuri
- King's College London and Parkinson's Foundation Centre of Excellence, King's College Hospital, London, UK
| | - Clive Ballard
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Paul Francis
- College of Medicine and Health, University of Exeter, Exeter, UK
- Institute of Psychiatry, Psychology and Neuroscience, Wolfson Centre for Age-Related Diseases, Guy's Campus, KCL, London, UK
| | - Ivana Rosenzweig
- Department of Neuroimaging, Sleep and Brain Plasticity Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), De Crespigny Park, Box 089, London, SE5 8AF, UK.
- Sleep Disorders Centre, GSTT, London, UK.
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20
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Sattari S, Basak US, James RG, Perrin LW, Crutchfield JP, Komatsuzaki T. Modes of information flow in collective cohesion. SCIENCE ADVANCES 2022; 8:eabj1720. [PMID: 35138896 PMCID: PMC8827646 DOI: 10.1126/sciadv.abj1720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 12/20/2021] [Indexed: 05/23/2023]
Abstract
Pairwise interactions are fundamental drivers of collective behavior-responsible for group cohesion. The abiding question is how each individual influences the collective. However, time-delayed mutual information and transfer entropy, commonly used to quantify mutual influence in aggregated individuals, can result in misleading interpretations. Here, we show that these information measures have substantial pitfalls in measuring information flow between agents from their trajectories. We decompose the information measures into three distinct modes of information flow to expose the role of individual and group memory in collective behavior. It is found that decomposed information modes between a single pair of agents reveal the nature of mutual influence involving many-body nonadditive interactions without conditioning on additional agents. The pairwise decomposed modes of information flow facilitate an improved diagnosis of mutual influence in collectives.
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Affiliation(s)
- Sulimon Sattari
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan
| | - Udoy S. Basak
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan
- Pabna University of Science and Technology, Pabna 6600, Bangladesh
| | - Ryan G. James
- Reddit Inc., 420 Taylor Street, San Francisco, CA 94102, USA
- Department of Physics, Complexity Sciences Center, University of California, Davis, Davis, CA 95616, USA
| | - Louis W. Perrin
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan
- École Normale Supérieure de Rennes, Robert Schumann, Campus de, Av. de Ker Lann, 35170 Bruz, France
| | - James P. Crutchfield
- Department of Physics, Complexity Sciences Center, University of California, Davis, Davis, CA 95616, USA
| | - Tamiki Komatsuzaki
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- Graduate School of Chemical Sciences and Engineering Materials Chemistry and Energy Course, Hokkaido University Kita 13, Nishi 8, Kita-ku Sapporo, Hokkaido 060-0812, Japan
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21
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The diversity and specificity of functional connectivity across spatial and temporal scales. Neuroimage 2021; 245:118692. [PMID: 34751153 DOI: 10.1016/j.neuroimage.2021.118692] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 01/01/2023] Open
Abstract
Macroscopic neuroimaging modalities in humans have revealed the organization of brain-wide activity into distributed functional networks that re-organize according to behavioral demands. However, the inherent coarse-graining of macroscopic measurements conceals the diversity and specificity in responses and connectivity of many individual neurons contained in each local region. New invasive approaches in animals enable recording and manipulating neural activity at meso- and microscale resolution, with cell-type specificity and temporal precision down to milliseconds. Determining how brain-wide activity patterns emerge from interactions across spatial and temporal scales will allow us to identify the key circuit mechanisms contributing to global brain states and how the dynamic activity of these states enables adaptive behavior.
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22
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Abstract
[Figure: see text].
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Affiliation(s)
- Gabrielle Girardeau
- Institut du Fer a Moulin, UMR-S 1270 INSERM and Sorbonne Université, 75005 Paris, France
| | - Vítor Lopes-Dos-Santos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
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23
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Byron N, Semenova A, Sakata S. Mutual Interactions between Brain States and Alzheimer's Disease Pathology: A Focus on Gamma and Slow Oscillations. BIOLOGY 2021; 10:707. [PMID: 34439940 PMCID: PMC8389330 DOI: 10.3390/biology10080707] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/17/2021] [Accepted: 07/21/2021] [Indexed: 12/26/2022]
Abstract
Brain state varies from moment to moment. While brain state can be defined by ongoing neuronal population activity, such as neuronal oscillations, this is tightly coupled with certain behavioural or vigilant states. In recent decades, abnormalities in brain state have been recognised as biomarkers of various brain diseases and disorders. Intriguingly, accumulating evidence also demonstrates mutual interactions between brain states and disease pathologies: while abnormalities in brain state arise during disease progression, manipulations of brain state can modify disease pathology, suggesting a therapeutic potential. In this review, by focusing on Alzheimer's disease (AD), the most common form of dementia, we provide an overview of how brain states change in AD patients and mouse models, and how controlling brain states can modify AD pathology. Specifically, we summarise the relationship between AD and changes in gamma and slow oscillations. As pathological changes in these oscillations correlate with AD pathology, manipulations of either gamma or slow oscillations can modify AD pathology in mouse models. We argue that neuromodulation approaches to target brain states are a promising non-pharmacological intervention for neurodegenerative diseases.
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Affiliation(s)
- Nicole Byron
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK
| | - Anna Semenova
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK
| | - Shuzo Sakata
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK
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