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Ensel S, Uhrig L, Ozkirli A, Hoffner G, Tasserie J, Dehaene S, Van De Ville D, Jarraya B, Pirondini E. Transient brain activity dynamics discriminate levels of consciousness during anesthesia. Commun Biol 2024; 7:716. [PMID: 38858589 PMCID: PMC11164921 DOI: 10.1038/s42003-024-06335-x] [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: 10/20/2023] [Accepted: 05/15/2024] [Indexed: 06/12/2024] Open
Abstract
The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.
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Affiliation(s)
- Scott Ensel
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lynn Uhrig
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Department of Anesthesiology and Critical Care, Necker Hospital, AP-HP, Université Paris Cité, Paris, France
| | - Ayberk Ozkirli
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Guylaine Hoffner
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
| | - Jordy Tasserie
- Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Collège de France, Paris, France
| | - Dimitri Van De Ville
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Béchir Jarraya
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Université Paris-Saclay (UVSQ), Saclay, France
- Neuroscience Pole, Foch Hospital, Suresnes, France
| | - Elvira Pirondini
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
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Dervinis M, Crunelli V. Sleep waves in a large-scale corticothalamic model constrained by activities intrinsic to neocortical networks and single thalamic neurons. CNS Neurosci Ther 2024; 30:e14206. [PMID: 37072918 PMCID: PMC10915987 DOI: 10.1111/cns.14206] [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: 11/02/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/20/2023] Open
Abstract
AIM Many biophysical and non-biophysical models have been able to reproduce the corticothalamic activities underlying different EEG sleep rhythms but none of them included the known ability of neocortical networks and single thalamic neurons to generate some of these waves intrinsically. METHODS We built a large-scale corticothalamic model with a high fidelity in anatomical connectivity consisting of a single cortical column and first- and higher-order thalamic nuclei. The model is constrained by different neocortical excitatory and inhibitory neuronal populations eliciting slow (<1 Hz) oscillations and by thalamic neurons generating sleep waves when isolated from the neocortex. RESULTS Our model faithfully reproduces all EEG sleep waves and the transition from a desynchronized EEG to spindles, slow (<1 Hz) oscillations, and delta waves by progressively increasing neuronal membrane hyperpolarization as it occurs in the intact brain. Moreover, our model shows that slow (<1 Hz) waves most often start in a small assembly of thalamocortical neurons though they can also originate in cortical layer 5. Moreover, the input of thalamocortical neurons increases the frequency of EEG slow (<1 Hz) waves compared to those generated by isolated cortical networks. CONCLUSION Our simulations challenge current mechanistic understanding of the temporal dynamics of sleep wave generation and suggest testable predictions.
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Affiliation(s)
- Martynas Dervinis
- Neuroscience Division, School of BioscienceCardiff UniversityMuseum AvenueCardiffCF10 3AXUK
- Present address:
School of Physiology, Pharmacology and NeuroscienceBiomedical BuildingBristolBS8 1TDUK
| | - Vincenzo Crunelli
- Neuroscience Division, School of BioscienceCardiff UniversityMuseum AvenueCardiffCF10 3AXUK
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Kim J, Lee HJ, Lee DA, Park KM. Cerebellar volumes and the intrinsic cerebellar network in patients with obstructive sleep apnea. Sleep Breath 2024; 28:301-309. [PMID: 37710027 DOI: 10.1007/s11325-023-02916-y] [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: 04/15/2023] [Revised: 08/28/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE This research aimed to explore changes in both cerebellar volume and the intrinsic cerebellar network in patients with obstructive sleep apnea (OSA). METHODS Newly diagnosed OSA patients and healthy controls were included in the study. All participants underwent three-dimensional T1-weighted imaging using a 3-T MRI scanner. Cerebellar volumes, both overall and subdivided, were quantified using the ACAPULCO program. The intrinsic cerebellar network was assessed using the BRAPH program, which applied graph theory to the cerebellar volume subdivision. Comparisons were drawn between the patients with OSA and healthy controls. RESULTS The study revealed that the 26 patients with OSA exhibited a notably lower total cerebellar volume compared to the 28 healthy controls (8.330 vs. 9.068%, p < 0.001). The volume of the left lobule VIIB was reduced in patients with OSA compared to healthy controls (0.339 vs. 0.407%, p = 0.001). Among patients with OSA, there was a negative correlation between the volume of the left lobule X and apnea-hypopnea index during non-rapid eye movement sleep (r = - 0.536, p = 0.005). However, no significant differences were observed in the intrinsic cerebellar network between patients and healthy controls. CONCLUSION This study established that patients with OSA exhibited decreased total cerebellar volumes and particularly reduced volumes in subdivisions such as the left lobule VIIB compared to healthy controls. These findings suggest potential involvement of the cerebellum in the underlying mechanisms of OSA.
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Affiliation(s)
- Jinseung Kim
- Department of Family Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-Ro 875, Haeundae-Gu, 48108, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-Ro 875, Haeundae-Gu, 48108, Busan, Republic of Korea.
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Ricciardiello A, McKinnon AC, Mowszowski L, LaMonica HM, Schrire ZM, Haroutonian C, Lam A, Hickie IB, D'Rozario A, Naismith SL. Assessing sleep architecture and cognition in older adults with depressive symptoms attending a memory clinic. J Affect Disord 2024; 348:35-43. [PMID: 38123073 DOI: 10.1016/j.jad.2023.12.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND While depression is intrinsically and bidirectionally linked with both sleep disturbance and cognition, the inter-relationships between sleep, cognition, and brain integrity in older people with depression, especially those with late-onset depression are undefined. METHODS One hundred and seventy-two older adults (mean age 64.3 ± 6.9 years, Depression: n = 66, Control: n = 106) attending a memory clinic underwent a neuropsychological battery of declarative memory, executive function tasks, cerebral magnetic resonance imaging and overnight polysomnography with quantitative electroencephalography. RESULTS The time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep, slow-wave activity, sleep spindles, hippocampal volume and prefrontal cortex thickness did not differ between depression and control and depression onset groups. However, sleep onset latency (p = 0.005) and REM onset latency (p = 0.02) were later in the Depression group compared to controls. Less SWS was associated with poorer memory (r = 0.31, p = 0.023) in the depression group, and less SWS was related to better memory in the control group (r = -0.20, p = 0.043; Fishers r-to-z = -3.19). LIMITATIONS Longitudinal studies are needed to determine if changes in sleep in those with depressive symptoms predict cognitive decline and illness trajectory. CONCLUSION Older participants with depressive symptoms had delayed sleep initiation, suggestive of delayed sleep phase. The association between SWS and memory suggests SWS may be a useful target for cognitive intervention in older adults with depression symptoms. Reduced hippocampal volumes did not mediate this relationship, indicating a broader distributed neural network may underpin these associations.
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Affiliation(s)
- Andrea Ricciardiello
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Camperdown, NSW, Australia.
| | - Andrew C McKinnon
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CogSleep, Australian National Health and Medical Research Council Centre of Research Excellence, Australia
| | - Loren Mowszowski
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CogSleep, Australian National Health and Medical Research Council Centre of Research Excellence, Australia
| | - Haley M LaMonica
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
| | - Zoe Menczel Schrire
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Camperdown, NSW, Australia; CogSleep, Australian National Health and Medical Research Council Centre of Research Excellence, Australia
| | - Carla Haroutonian
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Camperdown, NSW, Australia
| | - Aaron Lam
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Camperdown, NSW, Australia; School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
| | - Ian B Hickie
- School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
| | - Angela D'Rozario
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, Macquarie University, Camperdown, NSW, Australia; CogSleep, Australian National Health and Medical Research Council Centre of Research Excellence, Australia; School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
| | - Sharon L Naismith
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW, Australia; Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia; Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia; CogSleep, Australian National Health and Medical Research Council Centre of Research Excellence, Australia
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Rahul J, Sharma D, Sharma LD, Nanda U, Sarkar AK. A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front Hum Neurosci 2024; 18:1347082. [PMID: 38419961 PMCID: PMC10899326 DOI: 10.3389/fnhum.2024.1347082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.
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Affiliation(s)
- Jagdeep Rahul
- Department of Electronics and Communication Engineering, Rajiv Gandhi University, Arunachal Pradesh, India
| | - Diksha Sharma
- Department of Electronics and Communication, Indian Institute of Information Technology, Sri City, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amrawati, India
| | - Umakanta Nanda
- School of Electronics Engineering, VIT-AP University, Amrawati, India
| | - Achintya Kumar Sarkar
- Department of Electronics and Communication, Indian Institute of Information Technology, Sri City, India
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6
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Alipour M, Seok S, Mednick SC, Malerba P. A classification-based generative approach to selective targeting of global slow oscillations during sleep. Front Hum Neurosci 2024; 18:1342975. [PMID: 38415278 PMCID: PMC10896842 DOI: 10.3389/fnhum.2024.1342975] [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: 11/22/2023] [Accepted: 01/30/2024] [Indexed: 02/29/2024] Open
Abstract
Background Given sleep's crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/Hypothesis This study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. Methods We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. Results Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. Conclusion Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.
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Affiliation(s)
- Mahmoud Alipour
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
- The Ohio State University School of Medicine, Columbus, OH, United States
| | - SangCheol Seok
- Center for Gene Therapy, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Sara C. Mednick
- Department of Cognitive Sciences, University of California, Irvine, Irvine CA, United States
| | - Paola Malerba
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
- The Ohio State University School of Medicine, Columbus, OH, United States
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Jacob LPL, Bailes SM, Williams SD, Stringer C, Lewis LD. Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577429. [PMID: 38352426 PMCID: PMC10862763 DOI: 10.1101/2024.01.29.577429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.
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Affiliation(s)
- Leandro P L Jacob
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sydney M Bailes
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Boston University, Boston, MA, USA
| | - Stephanie D Williams
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Boston University, Boston, MA, USA
| | | | - Laura D Lewis
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA USA
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Bergamo D, Handjaras G, Petruso F, Talami F, Ricciardi E, Benuzzi F, Vaudano AE, Meletti S, Bernardi G, Betta M. Maturation-dependent changes in cortical and thalamic activity during sleep slow waves: Insights from a combined EEG-fMRI study. Sleep Med 2024; 113:357-369. [PMID: 38113618 DOI: 10.1016/j.sleep.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/24/2023] [Accepted: 12/02/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Studies using scalp EEG have shown that slow waves (0.5-4 Hz), the most prominent hallmark of NREM sleep, undergo relevant changes from childhood to adulthood, mirroring brain structural modifications and the acquisition of cognitive skills. Here we used simultaneous EEG-fMRI to investigate the cortical and subcortical correlates of slow waves in school-age children and determine their relative developmental changes. METHODS We analyzed data from 14 school-age children with self-limited focal epilepsy of childhood who fell asleep during EEG-fMRI recordings. Brain regions associated with slow-wave occurrence were identified using a voxel-wise regression that also modelled interictal epileptic discharges and sleep spindles. At the group level, a mixed-effects linear model was used. The results were qualitatively compared with those obtained from 2 adolescents with epilepsy and 17 healthy adults. RESULTS Slow waves were associated with hemodynamic-signal decreases in bilateral somatomotor areas. Such changes extended more posteriorly relative to those in adults. Moreover, the involvement of areas belonging to the default mode network changes as a function of age. No significant hemodynamic responses were observed in subcortical structures. However, we identified a significant correlation between age and thalamic hemodynamic changes. CONCLUSIONS Present findings indicate that the somatomotor cortex may have a key role in slow-wave expression throughout the lifespan. At the same time, they are consistent with a posterior-to-anterior shift in slow-wave distribution mirroring brain maturational changes. Finally, our results suggest that slow-wave changes may not reflect only neocortical modifications but also the maturation of subcortical structures, including the thalamus.
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Affiliation(s)
- Damiana Bergamo
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | | | - Flavia Petruso
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy; Sant'Anna School of Advanced Studies, Pisa, Italy
| | - Francesca Talami
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Neurology Dept., Azienda Ospedaliera Universitaria di Modena, Italy
| | | | - Francesca Benuzzi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Neurology Dept., Azienda Ospedaliera Universitaria di Modena, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Neurology Dept., Azienda Ospedaliera Universitaria di Modena, Italy
| | - Giulio Bernardi
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Monica Betta
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy.
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Wu R, Ma H, Hu J, Wang D, Wang F, Yu X, Li Y, Fu W, Lai M, Hu Z, Feng W, Shan C, Wang C. Electroacupuncture stimulation to modulate neural oscillations in promoting neurological rehabilitation. Brain Res 2024; 1822:148642. [PMID: 37884179 DOI: 10.1016/j.brainres.2023.148642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023]
Abstract
Electroacupuncture (EA) stimulation is a modern neuromodulation technique that integrates traditional Chinese acupuncture therapy with contemporary electrical stimulation. It involves the application of electrical currents to specific acupoints on the body following acupuncture. EA has been widely used in the treatment of various neurological disorders, including epilepsy, stroke, Parkinson's disease, and Alzheimer's disease. Recent research suggests that EA stimulation may modulate neural oscillations, correcting abnormal brain electrical activity, therefore promoting brain function and aiding in neurological rehabilitation. This paper conducted a comprehensive search in databases such as PubMed, Web of Science, and CNKI using keywords like "electroacupuncture," "neural oscillations," and "neurorehabilitation", covering the period from year 1980 to 2023. We provide a detailed overview of how electroacupuncture stimulation modulates neural oscillations, including maintaining neural activity homeostasis, influencing neurotransmitter release, improving cerebral hemodynamics, and enhancing specific neural functional networks. The paper also discusses the current state of research, limitations of electroacupuncture-induced neural oscillation techniques, and explores prospects for their combined application, aiming to offer broader insights for both basic and clinical research.
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Affiliation(s)
- Ruiren Wu
- The Second Rehabilitation Hospital of Shanghai, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Hongli Ma
- The Second Rehabilitation Hospital of Shanghai, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jun Hu
- The Second Rehabilitation Hospital of Shanghai, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Deheng Wang
- School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Feng Wang
- Department of Neurology, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaoming Yu
- Department of Rehabilitation, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuanli Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Rehabilitation, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Wang Fu
- Department of Neurology, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Minghui Lai
- Department of Rehabilitation, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zekai Hu
- The Second Rehabilitation Hospital of Shanghai, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Wei Feng
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Chunlei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Rehabilitation, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cong Wang
- The Second Rehabilitation Hospital of Shanghai, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Neurology, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Rehabilitation, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China; Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China; Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
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Klar P, Çatal Y, Fogel S, Jocham G, Langner R, Owen AM, Northoff G. Auditory inputs modulate intrinsic neuronal timescales during sleep. Commun Biol 2023; 6:1180. [PMID: 37985812 PMCID: PMC10661171 DOI: 10.1038/s42003-023-05566-8] [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: 08/16/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies have demonstrated that intrinsic neuronal timescales (INT) undergo modulation by external stimulation during consciousness. It remains unclear if INT keep the ability for significant stimulus-induced modulation during primary unconscious states, such as sleep. This fMRI analysis addresses this question via a dataset that comprises an awake resting-state plus rest and stimulus states during sleep. We analyzed INT measured via temporal autocorrelation supported by median frequency (MF) in the frequency-domain. Our results were replicated using a biophysical model. There were two main findings: (1) INT prolonged while MF decreased from the awake resting-state to the N2 resting-state, and (2) INT shortened while MF increased during the auditory stimulus in sleep. The biophysical model supported these results by demonstrating prolonged INT in slowed neuronal populations that simulate the sleep resting-state compared to an awake state. Conversely, under sine wave input simulating the stimulus state during sleep, the model's regions yielded shortened INT that returned to the awake resting-state level. Our results highlight that INT preserve reactivity to stimuli in states of unconsciousness like sleep, enhancing our understanding of unconscious brain dynamics and their reactivity to stimuli.
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Affiliation(s)
- Philipp Klar
- Faculty of Mathematics and Natural Sciences, Institute of Experimental Psychology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Room 6435, Ottawa, ON, K1Z 7K4, Canada
| | - Stuart Fogel
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, K1Z 7K4, Ottawa, ON, Canada
| | - Gerhard Jocham
- Faculty of Mathematics and Natural Sciences, Institute of Experimental Psychology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Adrian M Owen
- Departments of Physiology and Pharmacology and Psychology, Western University, London, ON, N6A 5B7, Canada
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Room 6435, Ottawa, ON, K1Z 7K4, Canada
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Tianmu Road 305, Hangzhou, Zhejiang Province, 310013, China
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11
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Gu Y, Gagnon JF, Kaminska M. Sleep electroencephalography biomarkers of cognition in obstructive sleep apnea. J Sleep Res 2023; 32:e13831. [PMID: 36941194 DOI: 10.1111/jsr.13831] [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: 09/26/2022] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 03/23/2023]
Abstract
Obstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea-hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow-wave activity, sleep spindles, K-complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high-density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.
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Affiliation(s)
- Yusing Gu
- Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jean-François Gagnon
- Department of Psychology, Université du Québec à Montréal, Montréal, Québec, Canada
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Marta Kaminska
- Respiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre, Montreal, Québec, Canada
- Respiratory Division & Sleep Laboratory, McGill University Health Centre, Montreal, Québec, Canada
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12
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Parajuli M, Amara AW, Shaban M. Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease. PLoS One 2023; 18:e0286506. [PMID: 37535549 PMCID: PMC10399849 DOI: 10.1371/journal.pone.0286506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/16/2023] [Indexed: 08/05/2023] Open
Abstract
Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease.
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Affiliation(s)
- Madan Parajuli
- Electrical and Computer Engineering, University of South Alabama, Mobile, Alabama, United States of America
| | - Amy W Amara
- Movement Disorders Center, University of Colorado, Aurora, Colorado, United States of America
| | - Mohamed Shaban
- Electrical and Computer Engineering, University of South Alabama, Mobile, Alabama, United States of America
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13
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Gunasekaran H, Azizi L, van Wassenhove V, Herbst SK. Characterizing endogenous delta oscillations in human MEG. Sci Rep 2023; 13:11031. [PMID: 37419933 PMCID: PMC10328979 DOI: 10.1038/s41598-023-37514-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023] Open
Abstract
Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.
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Affiliation(s)
- Harish Gunasekaran
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Leila Azizi
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Virginie van Wassenhove
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France.
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14
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Yu Y, Qiu Y, Li G, Zhang K, Bo B, Pei M, Ye J, Thompson GJ, Cang J, Fang F, Feng Y, Duan X, Tong C, Liang Z. Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice. Nat Commun 2023; 14:1651. [PMID: 36964161 PMCID: PMC10039056 DOI: 10.1038/s41467-023-37352-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
Sleep is ubiquitous and essential, but its mechanisms remain unclear. Studies in animals and humans have provided insights of sleep at vastly different spatiotemporal scales. However, challenges remain to integrate local and global information of sleep. Therefore, we developed sleep fMRI based on simultaneous electrophysiology at 9.4 T in male mice. Optimized un-anesthetized mouse fMRI setup allowed manifestation of NREM and REM sleep, and a large sleep fMRI dataset was collected and openly accessible. State dependent global patterns were revealed, and state transitions were found to be global, asymmetrical and sequential, which can be predicted up to 17.8 s using LSTM models. Importantly, sleep fMRI with hippocampal recording revealed potentiated sharp-wave ripple triggered global patterns during NREM than awake state, potentially attributable to co-occurrence of spindle events. To conclude, we established mouse sleep fMRI with simultaneous electrophysiology, and demonstrated its capability by revealing global dynamics of state transitions and neural events.
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Affiliation(s)
- Yalin Yu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yue Qiu
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gen Li
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China
| | - Kaiwei Zhang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jingjing Ye
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | | | - Jing Cang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Fang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
- The Central Hospital of Xuhui District, Shanghai, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xiaojie Duan
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China.
| | - Chuanjun Tong
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
| | - Zhifeng Liang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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15
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Benarroch E. What Is the Involvement of the Cerebellum During Sleep? Neurology 2023; 100:572-577. [PMID: 36941065 PMCID: PMC10033165 DOI: 10.1212/wnl.0000000000207161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 03/17/2023] Open
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16
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Northoff G, Scalabrini A, Fogel S. Topographic-dynamic reorganisation model of dreams (TRoD) - A spatiotemporal approach. Neurosci Biobehav Rev 2023; 148:105117. [PMID: 36870584 DOI: 10.1016/j.neubiorev.2023.105117] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/13/2022] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Dreams are one of the most bizarre and least understood states of consciousness. Bridging the gap between brain and phenomenology of (un)conscious experience, we propose the Topographic-dynamic Re-organization model of Dreams (TRoD). Topographically, dreams are characterized by a shift towards increased activity and connectivity in the default-mode network (DMN) while they are reduced in the central executive network, including the dorsolateral prefrontal cortex (except in lucid dreaming). This topographic re-organization is accompanied by dynamic changes; a shift towards slower frequencies and longer timescales. This puts dreams dynamically in an intermediate position between awake state and NREM 2/SWS sleep. TRoD proposes that the shift towards DMN and slower frequencies leads to an abnormal spatiotemporal framing of input processing including both internally- and externally-generated inputs (from body and environment). In dreams, a shift away from temporal segregation to temporal integration of inputs results in the often bizarre and highly self-centric mental contents as well as hallucinatory-like states. We conclude that topography and temporal dynamics are core features of the TroD, which may provide the connection of neural and mental activity, e.g., brain and experience during dreams as their "common currency".
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Affiliation(s)
- Georg Northoff
- Faculty of Medicine, Centre for Neural Dynamics, The Royal's Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, China; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China.
| | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy.
| | - Stuart Fogel
- Sleep and Neuroscience, The Royal's Institute of Mental Health Research, Brain and Mind Research Institute and Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada.
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17
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Leon LES, Sillitoe RV. Disrupted sleep in dystonia depends on cerebellar function but not motor symptoms in mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527916. [PMID: 36798256 PMCID: PMC9934608 DOI: 10.1101/2023.02.09.527916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Although dystonia is the third most common movement disorder, patients often also experience debilitating nonmotor defects including impaired sleep. The cerebellum is a central component of a "dystonia network" that plays various roles in sleep regulation. Importantly, the primary driver of sleep impairments in dystonia remains poorly understood. The cerebellum, along with other nodes in the motor circuit, could disrupt sleep. However, it is unclear how the cerebellum might alter sleep and mobility. To disentangle the impact of cerebellar dysfunction on motion and sleep, we generated two mouse genetic models of dystonia that have overlapping cerebellar circuit miswiring but show differing motor phenotype severity: Ptf1a Cre ;Vglut2 fx/fx and Pdx1 Cre ;Vglut2 fx/fx mice. In both models, excitatory climbing fiber to Purkinje cell neurotransmission is blocked, but only the Ptf1a Cre ;Vglut2 fx/fx mice have severe twisting. Using in vivo ECoG and EMG recordings we found that both mutants spend greater time awake and in NREM sleep at the expense of REM sleep. The increase in awake time is driven by longer awake bouts rather than an increase in bout number. We also found a longer latency to reach REM in both mutants, which is similar to what is reported in human dystonia. We uncovered independent but parallel roles for cerebellar circuit dysfunction and motor defects in promoting sleep quality versus posture impairments in dystonia.
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18
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Baena D, Fang Z, Gibbings A, Smith D, Ray LB, Doyon J, Owen AM, Fogel SM. Functional differences in cerebral activation between slow wave-coupled and uncoupled sleep spindles. Front Neurosci 2023; 16:1090045. [PMID: 36741053 PMCID: PMC9889560 DOI: 10.3389/fnins.2022.1090045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Spindles are often temporally coupled to slow waves (SW). These SW-spindle complexes have been implicated in memory consolidation that involves transfer of information from the hippocampus to the neocortex. However, spindles and SW, which are characteristic of NREM sleep, can occur as part of this complex, or in isolation. It is not clear whether dissociable parts of the brain are recruited when coupled to SW vs. when spindles or SW occur in isolation. Here, we tested differences in cerebral activation time-locked to uncoupled spindles, uncoupled SW and coupled SW-spindle complexes using simultaneous EEG-fMRI. Consistent with the "active system model," we hypothesized that brain activations time-locked to coupled SW-spindles would preferentially occur in brain areas known to be critical for sleep-dependent memory consolidation. Our results show that coupled spindles and uncoupled spindles recruit distinct parts of the brain. Specifically, we found that hippocampal activation during sleep is not uniquely related to spindles. Rather, this process is primarily driven by SWs and SW-spindle coupling. In addition, we show that SW-spindle coupling is critical in the activation of the putamen. Importantly, SW-spindle coupling specifically recruited frontal areas in comparison to uncoupled spindles, which may be critical for the hippocampal-neocortical dialogue that preferentially occurs during sleep.
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Affiliation(s)
- Daniel Baena
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
| | - Zhuo Fang
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Aaron Gibbings
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
| | - Dylan Smith
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
| | - Laura B. Ray
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Julien Doyon
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Adrian M. Owen
- The Brain and Mind Institute, Western University, London, ON, Canada,Department of Physiology and Pharmacology, Western University, London, ON, Canada
| | - Stuart M. Fogel
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada,School of Psychology, University of Ottawa, Ottawa, ON, Canada,The Brain and Mind Institute, Western University, London, ON, Canada,Department of Physiology and Pharmacology, Western University, London, ON, Canada,University of Ottawa Brain and Mind Research Institute, Ottawa, ON, Canada,*Correspondence: Stuart M. Fogel,
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19
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Yoshida K, Toyoizumi T. Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep. PNAS NEXUS 2022; 2:pgac286. [PMID: 36712943 PMCID: PMC9833047 DOI: 10.1093/pnasnexus/pgac286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Slow waves during the non-rapid eye movement (NREM) sleep reflect the alternating up and down states of cortical neurons; global and local slow waves promote memory consolidation and forgetting, respectively. Furthermore, distinct spike-timing-dependent plasticity (STDP) operates in these up and down states. The contribution of different plasticity rules to neural information coding and memory reorganization remains unknown. Here, we show that optimal synaptic plasticity for information maximization in a cortical neuron model provides a unified explanation for these phenomena. The model indicates that the optimal synaptic plasticity is biased toward depression as the baseline firing rate increases. This property explains the distinct STDP observed in the up and down states. Furthermore, it explains how global and local slow waves predominantly potentiate and depress synapses, respectively, if the background firing rate of excitatory neurons declines with the spatial scale of waves as the model predicts. The model provides a unifying account of the role of NREM sleep, bridging neural information coding, synaptic plasticity, and memory reorganization.
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20
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Wunderlin M, Koenig T, Zeller C, Nissen C, Züst MA. Automatized online prediction of slow-wave peaks during non-rapid eye movement sleep in young and old individuals: Why we should not always rely on amplitude thresholds. J Sleep Res 2022; 31:e13584. [PMID: 35274389 PMCID: PMC9787564 DOI: 10.1111/jsr.13584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/02/2022] [Accepted: 02/25/2022] [Indexed: 12/30/2022]
Abstract
Brain-state-dependent stimulation during slow-wave sleep is a promising tool for the treatment of psychiatric and neurodegenerative diseases. A widely used slow-wave prediction algorithm required for brain-state-dependent stimulation is based on a specific amplitude threshold in the electroencephalogram. However, due to decreased slow-wave amplitudes in aging and psychiatric conditions, this approach might miss many slow-waves because they do not fulfill the amplitude criterion. Here, we compared slow-wave peaks predicted via an amplitude-based versus a multidimensional approach using a topographical template of slow-wave peaks in 21 young and 21 older healthy adults. We validate predictions against the gold-standard of offline detected peaks. Multidimensionally predicted peaks resemble the gold-standard regarding spatiotemporal dynamics but exhibit lower peak amplitudes. Amplitude-based prediction, by contrast, is less sensitive, less precise and - especially in the older group - predicts peaks that differ from the gold-standard regarding spatiotemporal dynamics. Our results suggest that amplitude-based slow-wave peak prediction might not always be the ideal choice. This is particularly the case in populations with reduced slow-wave amplitudes, like older adults or psychiatric patients. We recommend the use of multidimensional prediction, especially in studies targeted at populations other than young and healthy individuals.
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Affiliation(s)
- Marina Wunderlin
- University Hospital of Old Age Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
| | - Thomas Koenig
- University Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland,Interfaculty Research Cooperation ‐ Decoding SleepUniversity of BernBernSwitzerland
| | - Céline Zeller
- University Hospital of Old Age Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
| | - Christoph Nissen
- University Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland,Interfaculty Research Cooperation ‐ Decoding SleepUniversity of BernBernSwitzerland
| | - Marc Alain Züst
- University Hospital of Old Age Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
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21
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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: 10] [Impact Index Per Article: 5.0] [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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22
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Chen PC, Zhang J, Thayer JF, Mednick SC. Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory. Proc Natl Acad Sci U S A 2022; 119:e2123417119. [PMID: 36279428 PMCID: PMC9636982 DOI: 10.1073/pnas.2123417119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal-subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.
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Affiliation(s)
- Pin-Chun Chen
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104
| | - Jing Zhang
- Department of Cognitive Sciences, University of California, Irvine, CA 92697
| | - Julian F. Thayer
- Department of Psychological Sciences, University of California, Irvine, CA 92697
| | - Sara C. Mednick
- Department of Cognitive Sciences, University of California, Irvine, CA 92697
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Seok SC, McDevitt E, Mednick SC, Malerba P. Global and non-Global slow oscillations differentiate in their depth profiles. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:947618. [PMID: 36926094 PMCID: PMC10013040 DOI: 10.3389/fnetp.2022.947618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/10/2022] [Indexed: 03/18/2023]
Abstract
Sleep slow oscillations (SOs, 0.5-1.5 Hz) are thought to organize activity across cortical and subcortical structures, leading to selective synaptic changes that mediate consolidation of recent memories. Currently, the specific mechanism that allows for this selectively coherent activation across brain regions is not understood. Our previous research has shown that SOs can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. The functional significance of space-time profiles of SOs hinges on testing if these differential SOs scalp profiles are mirrored by differential depth structure of SOs in the brain. In this study, we built an analytical framework to allow for the characterization of SO depth profiles in space-time across cortical and sub-cortical regions. To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. Multiple algorithms reached high performance across all participants, in particular algorithms based on k-nearest neighbors classification principles. Univariate feature ranking and selection showed that the most differentiating features for Global vs. non-Global SOs appeared around the trough of the SO, and in regions including cortex, thalamus, caudate nucleus, and brainstem. Results also indicated that differentiation during S2 required an extended network of current from cortical-subcortical regions, including all regions found in SWS and other basal ganglia regions, and amygdala and hippocampus, suggesting a potential functional differentiation in the role of Global SOs in S2 vs. SWS. We interpret our results as supporting the potential functional difference of Global and non-Global SOs in sleep dynamics.
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Affiliation(s)
- Sang-Cheol Seok
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, United States
| | | | - Sara C. Mednick
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Paola Malerba
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, United States
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States
- School of Medicine, The Ohio State University, Columbus, OH, United States
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Salazar Leon LE, Sillitoe RV. Potential Interactions Between Cerebellar Dysfunction and Sleep Disturbances in Dystonia. DYSTONIA 2022; 1. [PMID: 37065094 PMCID: PMC10099477 DOI: 10.3389/dyst.2022.10691] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Dystonia is the third most common movement disorder. It causes debilitating twisting postures that are accompanied by repetitive and sometimes intermittent co- or over-contractions of agonist and antagonist muscles. Historically diagnosed as a basal ganglia disorder, dystonia is increasingly considered a network disorder involving various brain regions including the cerebellum. In certain etiologies of dystonia, aberrant motor activity is generated in the cerebellum and the abnormal signals then propagate through a “dystonia circuit” that includes the thalamus, basal ganglia, and cerebral cortex. Importantly, it has been reported that non-motor defects can accompany the motor symptoms; while their severity is not always correlated, it is hypothesized that common pathways may nevertheless be disrupted. In particular, circadian dysfunction and disordered sleep are common non-motor patient complaints in dystonia. Given recent evidence suggesting that the cerebellum contains a circadian oscillator, displays sleep-stage-specific neuronal activity, and sends robust long-range projections to several subcortical regions involved in circadian rhythm regulation, disordered sleep in dystonia may result from cerebellum-mediated dysfunction of the dystonia circuit. Here, we review the evidence linking dystonia, cerebellar network dysfunction, and cerebellar involvement in sleep. Together, these ideas may form the basis for the development of improved pharmacological and surgical interventions that could take advantage of cerebellar circuitry to restore normal motor function as well as non-motor (sleep) behaviors in dystonia.
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Affiliation(s)
- Luis E. Salazar Leon
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, Texas, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, Texas, 77030, USA
| | - Roy V. Sillitoe
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, Texas, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Development, Disease Models & Therapeutics Graduate Program, Baylor College of Medicine, Houston, Texas, USA
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, Texas, 77030, USA
- Address correspondence to: Dr. Roy V. Sillitoe, Tel: 832-824-8913, Fax: 832-825-1251,
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25
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de Freitas PH, Monteiro RC, Bertani R, Perret CM, Rodrigues PC, Vicentini J, de Morais TMG, Rozental SF, Galvão GF, de Mattos F, Vasconcelos FA, Dorio IS, Hayashi CY, dos Santos JR, Werneck GL, Tocquer CTF, Capitão C, da Cruz LCH, Tulviste J, Fiorani M, da Silva MM, Paiva WS, Podell K, Federoff HJ, Patel DH, Lado F, Goldberg E, Llinás R, Bennett MV, Rozental R. E.L., a modern-day Phineas Gage: Revisiting frontal lobe injury. LANCET REGIONAL HEALTH. AMERICAS 2022; 14:100340. [PMID: 36777390 PMCID: PMC9903712 DOI: 10.1016/j.lana.2022.100340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
BACKGROUND How the prefrontal cortex (PFC) recovers its functionality following lesions remains a conundrum. Recent work has uncovered the importance of transient low-frequency oscillatory activity (LFO; < 4 Hz) for the recovery of an injured brain. We aimed to determine whether persistent cortical oscillatory dynamics contribute to brain capability to support 'normal life' following injury. METHODS In this 9-year prospective longitudinal study (08/2012-2021), we collected data from the patient E.L., a modern-day Phineas Gage, who suffered from lesions, impacting 11% of his total brain mass, to his right PFC and supplementary motor area after his skull was transfixed by an iron rod. A systematic evaluation of clinical, electrophysiologic, brain imaging, neuropsychological and behavioural testing were used to clarify the clinical significance of relationship between LFO discharge and executive dysfunctions and compare E.L.´s disorders to that attributed to Gage (1848), a landmark in the history of neurology and neuroscience. FINDINGS Selective recruitment of the non-injured left hemisphere during execution of unimanual right-hand movements resulted in the emergence of robust LFO, an EEG-detected marker for disconnection of brain areas, in the damaged right hemisphere. In contrast, recruitment of the damaged right hemisphere during contralateral hand movement, resulted in the co-activation of the left hemisphere and decreased right hemisphere LFO to levels of controls enabling performance, suggesting a target for neuromodulation. Similarly, transcranial magnetic stimulation (TMS), used to create a temporary virtual-lesion over E.L.'s healthy hemisphere, disrupted the modulation of contralateral LFO, disturbing behaviour and impairing executive function tasks. In contrast to Gage, reasoning, planning, working memory, social, sexual and family behaviours eluded clinical inspection by decreasing LFO in the delta frequency range during motor and executive functioning. INTERPRETATION Our study suggests that modulation of LFO dynamics is an important mechanism by which PFC accommodates neurological injuries, supporting the reports of Gage´s recovery, and represents an attractive target for therapeutic interventions. FUNDING Fundação de Amparo Pesquisa Rio de Janeiro (FAPERJ), Universidade Federal do Rio de Janeiro (intramural), and Fiocruz/Ministery of Health (INOVA Fiocruz).
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Affiliation(s)
- Pedro H.M. de Freitas
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | - Ruy C. Monteiro
- Miguel Couto Municipal Hospital, Rio de Janeiro, RJ, 22430-160, Brazil
| | - Raphael Bertani
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
- Miguel Couto Municipal Hospital, Rio de Janeiro, RJ, 22430-160, Brazil
| | - Caio M. Perret
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
- Miguel Couto Municipal Hospital, Rio de Janeiro, RJ, 22430-160, Brazil
| | - Pedro C. Rodrigues
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | - Joana Vicentini
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | | | | | - Gustavo F. Galvão
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | - Fabricio de Mattos
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | - Fernando A. Vasconcelos
- Miguel Couto Municipal Hospital, Rio de Janeiro, RJ, 22430-160, Brazil
- Dept Neurocirurgia, HUGG, Universidade Federal do Estado do Rio de Janeiro (UNIRIO), RJ, 20270-004, Brazil
| | - Ivan S. Dorio
- Miguel Couto Municipal Hospital, Rio de Janeiro, RJ, 22430-160, Brazil
| | - Cintya Y. Hayashi
- Dept Neurologia, Universidade do Estado de São Paulo, SP, 05402-000, Brazil
| | | | - Guilherme L. Werneck
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | | | | | | | - Jaan Tulviste
- University of Tartu, Institute of Psychology, Tartu, Estonia
| | - Mario Fiorani
- Instituto de Biofísica, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | - Marcos M. da Silva
- Dept Neurologia, HUCFF, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
| | | | - Kenneth Podell
- Neurological Institute, Houston Methodist, TX, 77030, USA
| | | | | | - Fred Lado
- Northwell Health, Manhasset, NY, 11030, USA
| | - Elkhonon Goldberg
- Dept Neurology, New York University, School of Medicine, NY, 10016, USA
| | - Rodolfo Llinás
- Dept. Physiology and Neuroscience, New York University, School of Medicine, NY, 10016, USA
| | | | - Renato Rozental
- Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, RJ, 21941-902, Brazil
- Dept Neuroscience, Albert Einstein Coll Medicine, Bronx, NY, 10461, USA
- Centro Desenvolvimento Tecnológico (CDTS), FIOCRUZ, Rio de Janeiro, 21040-361, Brazil
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26
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Zhang R, Tomasi D, Shokri-Kojori E, Manza P, Feldman DE, Kroll DS, Biesecker CL, McPherson KL, Schwandt M, Wang GJ, Wiers CE, Volkow ND. Effect of detoxification on N3 sleep correlates with brain functional but not structural changes in alcohol use disorder. Drug Alcohol Depend 2022; 238:109545. [PMID: 35779511 PMCID: PMC9444901 DOI: 10.1016/j.drugalcdep.2022.109545] [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: 02/26/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Sleep disturbances are very common in alcohol use disorder (AUD) and contribute to relapse. Detoxification appears to have limited effects on sleep problems. However, inter-individual differences and related brain mechanisms have not been closely examined. METHODS We examined N3 sleep and the associated brain functional and structural changes in 30 AUD patients (9 Females, mean age: 42 years) undergoing a 3-week inpatient detoxification. Patients' N3 sleep, resting state functional connectivity (RSFC), grey matter volume (GMV) and negative mood were measured on week 1 and week 3. RESULTS AUD patients did not show significant N3 sleep recovery after 3-weeks of detoxification. However, we observed large variability among AUD patients. Inter-individual variations in N3 increases were associated with increases in midline default mode network (DMN) RSFC but not with GMV using a whole-brain approach. Exploratory analyses revealed significant sex by detoxification effects on N3 sleep such that AUD females showed greater N3 increases than AUD males. Further, N3 increases fully mediated the effect of mood improvement on DMN RSFC increases. CONCLUSIONS We show a significant relationship between N3 and DMN functional changes in AUD over time/abstinence. The current findings may have clinical implications for monitoring brain recovery in AUD using daily sleep measures, which might help guide individualized treatments. Future investigations on sex differences with a larger sample and with longitudinal data for a longer period of abstinence are needed.
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Affiliation(s)
- Rui Zhang
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA.
| | - Dardo Tomasi
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Ehsan Shokri-Kojori
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Peter Manza
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Dana E Feldman
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Danielle S Kroll
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Catherine L Biesecker
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Katherine L McPherson
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Melanie Schwandt
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Gene-Jack Wang
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Corinde E Wiers
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Nora D Volkow
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA; National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD 20892-1013, USA.
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27
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Malerba P, Whitehurst L, Mednick SC. The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold. Sleep 2022; 45:6603295. [PMID: 35666552 PMCID: PMC9366646 DOI: 10.1093/sleep/zsac132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.
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Affiliation(s)
- Paola Malerba
- Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital , Columbus, OH , USA
- School of Medicine, The Ohio State University , Columbus, OH , USA
| | - Lauren Whitehurst
- Department of Psychology, University of Kentucky , Lexington, KY , USA
| | - Sara C Mednick
- Department of Cognitive Science, University of California Irvine , Irvine, CA , USA
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28
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The microbiota-gut-brain axis in sleep disorders. Sleep Med Rev 2022; 65:101691. [DOI: 10.1016/j.smrv.2022.101691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/04/2022] [Accepted: 08/19/2022] [Indexed: 12/25/2022]
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Abstract
The conventional wisdom that sleep is a global state, affecting the whole brain uniformly and simultaneously, was overturned by the discovery of local sleep, where individual neuronal populations were found to be asleep and the rest of the brain awake. However, due to the difficulty of monitoring local neuronal states in humans, our understanding of local sleep remains limited. Using simultaneous functional MRI (fMRI) and electroencephalography, we find that the oscillations of brain hemodynamic activity provide signatures of sleep at a local neuronal population level. We show that the fMRI signatures of sleep can be employed to monitor local neuronal states and investigate which brain regions are the first to fall asleep or wake up at wake–sleep transitions. Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level–dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake–sleep transitions, and investigate local homeostatic sleep processes.
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30
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Mofrad MH, Gilmore G, Koller D, Mirsattari SM, Burneo JG, Steven DA, Khan AR, Suller Marti A, Muller L. Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load. eLife 2022; 11:75769. [PMID: 35766286 PMCID: PMC9242645 DOI: 10.7554/elife.75769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/27/2022] [Indexed: 11/22/2022] Open
Abstract
Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex. The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas. Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys. The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes. In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.
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Affiliation(s)
- Maryam H Mofrad
- Department of Mathematics, Western University, London, Canada.,Brain and Mind Institute, Western University, London, Canada
| | - Greydon Gilmore
- Brain and Mind Institute, Western University, London, Canada.,Department of Biomedical Engineering, Western University, London, Canada
| | - Dominik Koller
- Advanced Concepts Team, European Space Agency, Noordwijk, Netherlands
| | - Seyed M Mirsattari
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Psychology, Western University, London, Canada
| | - Jorge G Burneo
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - David A Steven
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Ali R Khan
- Brain and Mind Institute, Western University, London, Canada.,Department of Biomedical Engineering, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Ana Suller Marti
- Brain and Mind Institute, Western University, London, Canada.,Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Lyle Muller
- Department of Mathematics, Western University, London, Canada.,Brain and Mind Institute, Western University, London, Canada
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31
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Slow oscillations promote long-range effective communication: The key for memory consolidation in a broken-down network. Proc Natl Acad Sci U S A 2022; 119:e2122515119. [PMID: 35733258 PMCID: PMC9245646 DOI: 10.1073/pnas.2122515119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
This work introduces a noninvasive approach to understanding information processing during sleep. Our results demonstrate that slow oscillations (SOs) provide the temporal-topographical event framework whereby long-range information flow increases dramatically compared to the relatively local activity patterns of the rest of sleep. The findings represent a conceptual leap in understanding how SOs unlock memory consolidation in a broken-down network, which is by promoting long-range, effective communication. This research will promote further investigations of understanding how brain oscillations alone and in nested rhythms promote network communication, as well as investigate how these properties vary and predict patterns of deficits in clinical populations and aging humans. A prominent and robust finding in cognitive neuroscience is the strengthening of memories during nonrapid eye movement (NREM) sleep, with slow oscillations (SOs;<1Hz) playing a critical role in systems-level consolidation. However, NREM generally shows a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present an approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promotes sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement based on independent electrophysiological observations during sleep. This work introduces a noninvasive approach to understanding information processing during sleep and provides a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state. In short, SOs are a gating mechanism for large-scale neural communication, a necessary substrate for systems consolidation and long-term memory formation.
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32
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Abstract
Behavioral states naturally alternate between wakefulness and the sleep phases rapid eye movement and nonrapid eye movement sleep. Waking and sleep states are complex processes that are elegantly orchestrated by spatially fine-tuned neurochemical changes of neurotransmitters and neuromodulators including glutamate, acetylcholine, γ-aminobutyric acid, norepinephrine, dopamine, serotonin, histamine, hypocretin, melanin concentrating hormone, adenosine, and melatonin. However, as highlighted in this brief overview, no single neurotransmitter or neuromodulator, but rather their complex interactions within organized neuronal ensembles, regulate waking and sleep states. The neurochemical pathways presented here are aimed to provide a conceptual framework for the understanding of the effects of currently used sleep medications.
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Affiliation(s)
- Sebastian C Holst
- Neuroscience and Rare Diseases Discovery and Translational Area, Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland.
| | - Hans-Peter Landolt
- Institute of Pharmacology and Toxicology, University of Zürich, Winterthurerstrasse 190, Zürich 8057, Switzerland; Zürich Center for Interdisciplinary Sleep Research (ZiS), University of Zürich, Zürich, Switzerland
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33
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Simon KC, Whitehurst LN, Zhang J, Mednick SC. Zolpidem Maintains Memories for Negative Emotions Across a Night of Sleep. AFFECTIVE SCIENCE 2022; 3:389-399. [PMID: 35791418 PMCID: PMC9249708 DOI: 10.1007/s42761-021-00079-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 09/01/2021] [Indexed: 11/27/2022]
Abstract
Zolpidem, a common medication for sleep complaints, also shows secondary, unexpected memory benefits. We previously found that zolpidem prior to a nap enhanced negative, highly arousing picture memory. As zolpidem is typically administered at night, how it affects overnight emotional memory processing is relevant. We used a double-blind, placebo-controlled, within-subject, cross-over design to investigate if zolpidem boosted negative compared to neutral picture memory. Subjects learned both pictures sets in the morning. That evening, subjects were administered zolpidem or placebo and slept in the lab. Recognition was tested that evening and the following morning. We found that zolpidem maintained negative picture memory compared to forgetting in the placebo condition. Furthermore, zolpidem increased slow-wave sleep time, decreased rapid eye movement sleep time, and increased the fast spindle range in NREM. Our results suggest that zolpidem may enhance negative memory longevity and salience. These findings raise concerns for zolpidem administration to certain clinical populations.
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Affiliation(s)
- Katharine C. Simon
- Department of Cognitive Science, University of California, Irvine, 2201 Social & Behavioral Sciences Gateway, Irvine, CA 92697 USA
| | | | - Jing Zhang
- Department of Cognitive Science, University of California, Irvine, 2201 Social & Behavioral Sciences Gateway, Irvine, CA 92697 USA
| | - Sara C. Mednick
- Department of Cognitive Science, University of California, Irvine, 2201 Social & Behavioral Sciences Gateway, Irvine, CA 92697 USA
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Dell'Acqua C, Dal Bò E, Moretta T, Palomba D, Messerotti Benvenuti S. EEG time-frequency analysis reveals blunted tendency to approach and increased processing of unpleasant stimuli in dysphoria. Sci Rep 2022; 12:8161. [PMID: 35581359 PMCID: PMC9113991 DOI: 10.1038/s41598-022-12263-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/06/2022] [Indexed: 12/02/2022] Open
Abstract
To date, affective and cognitive processing of emotional information in individuals with depressive symptoms have been examined through peripheral psychophysiological measures, event-related potentials, and time–frequency analysis of oscillatory activity. However, electrocortical correlates of emotional and cognitive processing of affective content in depression have not been fully understood. Time–frequency analysis of electroencephalographic activity allows disentangling the brain's parallel processing of information. The present study employed a time–frequency approach to simultaneously examine affective disposition and cognitive processing during the viewing of emotional stimuli in dysphoria. Time–frequency event-related changes were examined during the viewing of pleasant, neutral and unpleasant pictures in 24 individuals with dysphoria and 24 controls. Affective disposition was indexed by delta and alpha power, while theta power was employed as a correlate of cognitive elaboration of the stimuli. Cluster-based statistics revealed a centro-parietal reduction in delta power for pleasant stimuli in individuals with dysphoria relative to controls. Also, dysphoria was characterized by an early fronto-central increase in theta power for unpleasant stimuli relative to neutral and pleasant ones. Comparatively, controls were characterized by a late fronto-central and occipital reduction in theta power for unpleasant stimuli relative to neutral and pleasant. The present study granted novel insights on the interrelated facets of affective elaboration in dysphoria, mainly characterized by a hypoactivation of the approach-related motivational system and a sustained facilitated cognitive processing of unpleasant stimuli.
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Affiliation(s)
- Carola Dell'Acqua
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy. .,Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B, 35131, Padua, Italy.
| | - Elisa Dal Bò
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy.,Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B, 35131, Padua, Italy
| | - Tania Moretta
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy
| | - Daniela Palomba
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy.,Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B, 35131, Padua, Italy
| | - Simone Messerotti Benvenuti
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy.,Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B, 35131, Padua, Italy
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35
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Osorio-Forero A, Cherrad N, Banterle L, Fernandez LMJ, Lüthi A. When the Locus Coeruleus Speaks Up in Sleep: Recent Insights, Emerging Perspectives. Int J Mol Sci 2022; 23:ijms23095028. [PMID: 35563419 PMCID: PMC9099715 DOI: 10.3390/ijms23095028] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 12/03/2022] Open
Abstract
For decades, numerous seminal studies have built our understanding of the locus coeruleus (LC), the vertebrate brain’s principal noradrenergic system. Containing a numerically small but broadly efferent cell population, the LC provides brain-wide noradrenergic modulation that optimizes network function in the context of attentive and flexible interaction with the sensory environment. This review turns attention to the LC’s roles during sleep. We show that these roles go beyond down-scaled versions of the ones in wakefulness. Novel dynamic assessments of noradrenaline signaling and LC activity uncover a rich diversity of activity patterns that establish the LC as an integral portion of sleep regulation and function. The LC could be involved in beneficial functions for the sleeping brain, and even minute alterations in its functionality may prove quintessential in sleep disorders.
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36
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Zhang J, Whitehurst LN, Mednick SC. The Role of Sleep for Episodic Memory Consolidation: Stabilizing or Rescuing? Neurobiol Learn Mem 2022; 191:107621. [DOI: 10.1016/j.nlm.2022.107621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 03/01/2022] [Accepted: 04/04/2022] [Indexed: 12/25/2022]
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37
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Toor B, van den Berg NH, Fang Z, Pozzobon A, Ray LB, Fogel SM. Age-related differences in problem-solving skills: Reduced benefit of sleep for memory trace consolidation. Neurobiol Aging 2022; 116:55-66. [DOI: 10.1016/j.neurobiolaging.2022.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 04/05/2022] [Accepted: 04/17/2022] [Indexed: 10/18/2022]
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38
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ASMR amplifies low frequency and reduces high frequency oscillations. Cortex 2022; 149:85-100. [DOI: 10.1016/j.cortex.2022.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/26/2021] [Accepted: 01/10/2022] [Indexed: 11/20/2022]
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39
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Cakan C, Dimulescu C, Khakimova L, Obst D, Flöel A, Obermayer K. Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep. Front Comput Neurosci 2022; 15:800101. [PMID: 35095451 PMCID: PMC8790481 DOI: 10.3389/fncom.2021.800101] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.
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Affiliation(s)
- Caglar Cakan
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Cristiana Dimulescu
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Liliia Khakimova
- Department of Neurology, University Medicine, Greifswald, Germany
| | - Daniela Obst
- Department of Neurology, University Medicine, Greifswald, Germany
| | - Agnes Flöel
- Department of Neurology, University Medicine, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Klaus Obermayer
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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40
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OUP accepted manuscript. Cereb Cortex 2022; 32:4782-4796. [DOI: 10.1093/cercor/bhab516] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/14/2022] Open
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41
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Competitive dynamics underlie cognitive improvements during sleep. Proc Natl Acad Sci U S A 2021; 118:2109339118. [PMID: 34903651 PMCID: PMC8713802 DOI: 10.1073/pnas.2109339118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2021] [Indexed: 12/02/2022] Open
Abstract
Sleep facilitates both long-term episodic memory consolidation and short-term working memory functioning. However, the mechanism by which the sleeping brain performs both complex feats and which sleep features are associated with these processes remain unclear. Using a pharmacological approach, we demonstrate that long-term and working memory are served by distinct offline neural mechanisms and that these mechanisms are mutually antagonistic. We propose a sleep switch model in which the brain toggles between the two memory processes via a complex interaction at the synaptic, systems, and mechanistic level with implications for research on cognitive disturbances observed in neurodegenerative disorders such as Alzheimer’s and Parkinson's disease, both of which involve the decline of sleep. We provide evidence that human sleep is a competitive arena in which cognitive domains vie for limited resources. Using pharmacology and effective connectivity analysis, we demonstrate that long-term memory and working memory are served by distinct offline neural mechanisms that are mutually antagonistic. Specifically, we administered zolpidem to increase central sigma activity and demonstrated targeted suppression of autonomic vagal activity. With effective connectivity, we determined the central activity has greater causal influence over autonomic activity, and the magnitude of this influence during sleep produced a behavioral trade-off between offline long-term and working memory processing. These findings suggest a sleep switch mechanism that toggles between central sigma-dependent long-term memory and autonomic vagal-dependent working memory processing.
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42
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Osorio-Forero A, Cardis R, Vantomme G, Guillaume-Gentil A, Katsioudi G, Devenoges C, Fernandez LMJ, Lüthi A. Noradrenergic circuit control of non-REM sleep substates. Curr Biol 2021; 31:5009-5023.e7. [PMID: 34648731 DOI: 10.1016/j.cub.2021.09.041] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/09/2021] [Accepted: 09/15/2021] [Indexed: 12/13/2022]
Abstract
To understand what makes sleep vulnerable in disease, it is useful to look at how wake-promoting mechanisms affect healthy sleep. Wake-promoting neuronal activity is inhibited during non-rapid-eye-movement sleep (NREMS). However, sensory vigilance persists in NREMS in animals and humans, suggesting that wake promotion could remain functional. Here, we demonstrate that consolidated mouse NREMS is a brain state with recurrent fluctuations of the wake-promoting neurotransmitter noradrenaline on the ∼50-s timescale in the thalamus. These fluctuations occurred around mean noradrenaline levels greater than the ones of quiet wakefulness, while noradrenaline (NA) levels declined steeply in REMS. They coincided with a clustering of sleep spindle rhythms in the forebrain and with heart-rate variations, both of which are correlates of sensory arousability. We addressed the origins of these fluctuations by using closed-loop optogenetic locus coeruleus (LC) activation or inhibition timed to moments of low and high spindle activity during NREMS. We could suppress, lock, or entrain sleep-spindle clustering and heart-rate variations, suggesting that both fore- and hindbrain-projecting LC neurons show coordinated infraslow activity variations in natural NREMS. Noradrenergic modulation of thalamic, but not cortical, circuits was required for sleep-spindle clustering and involved NA release into primary sensory and reticular thalamic nuclei that activated both α1- and β-adrenergic receptors to cause slowly decaying membrane depolarizations. Noradrenergic signaling by LC constitutes a vigilance-promoting mechanism that renders mammalian NREMS vulnerable to disruption on the close-to-minute timescale through sustaining thalamocortical and autonomic sensory arousability. VIDEO ABSTRACT.
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Affiliation(s)
- Alejandro Osorio-Forero
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Romain Cardis
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Gil Vantomme
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Aurélie Guillaume-Gentil
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Georgia Katsioudi
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Christiane Devenoges
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Laura M J Fernandez
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland
| | - Anita Lüthi
- Department of Fundamental Neurosciences, University of Lausanne, Rue du Bugnon 9, 1005 Lausanne, Switzerland.
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43
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Chylinski DO, Van Egroo M, Narbutas J, Grignard M, Koshmanova E, Berthomier C, Berthomier P, Brandewinder M, Salmon E, Bahri MA, Bastin C, Collette F, Phillips C, Maquet P, Muto V, Vandewalle G. Heterogeneity in the links between sleep arousals, amyloid-beta and cognition. JCI Insight 2021; 6:152858. [PMID: 34784296 PMCID: PMC8783672 DOI: 10.1172/jci.insight.152858] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/10/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Tight relationships between sleep quality, cognition, and amyloid-β (Aβ) accumulation, a hallmark of Alzheimer’s disease (AD) neuropathology, have been shown. Sleep arousals become more prevalent with aging and are considered to reflect poorer sleep quality. However, heterogeneity in arousals has been suggested while their associations with Aβ and cognition are not established. METHODS We recorded undisturbed night-time sleep with EEG in 101 healthy individuals aged 50–70 years, devoid of cognitive and sleep disorders. We classified spontaneous arousals according to their association with muscular tone increase (M+/M–) and sleep stage transition (T+/T–). We assessed cortical Aβ burden over earliest affected regions via PET imaging and assessed cognition via neuropsychological testing. RESULTS Arousal types differed in their oscillatory composition in θ (4–8 Hz) and β (16–30 Hz) EEG bands. Furthermore, T+M– arousals, interrupting sleep continuity, were positively linked to Aβ burden (P = 0.0053, R²β* = 0.08). By contrast, more prevalent T–M+ arousals, upholding sleep continuity, were associated with lower Aβ burden (P = 0.0003, R²β* = 0.13), and better cognition, particularly over the attentional domain (P < 0.05, R²β* ≥ 0.04). CONCLUSION Contrasting with what is commonly accepted, we provide empirical evidence that arousals are diverse and differently associated with early AD-related neuropathology and cognition. This suggests that sleep arousals, and their coalescence with other brain oscillations during sleep, may actively contribute to the beneficial functions of sleep and constitute markers of favorable brain and cognitive health trajectories. TRIAL REGISTRATION EudraCT 2016-001436-35. FUNDING FRS-FNRS Belgium (FRSM 3.4516.11), Actions de Recherche Concertées Fédération Wallonie-Bruxelles (SLEEPDEM 17/27-09), ULiège, and European Regional Development Fund (Radiomed Project).
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Affiliation(s)
- Daphne O Chylinski
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Maxime Van Egroo
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Justinas Narbutas
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Martin Grignard
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Ekaterina Koshmanova
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | | | | | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christophe Phillips
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Pierre Maquet
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Vincenzo Muto
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Gilles Vandewalle
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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44
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Abstract
The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.
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Affiliation(s)
- Zinong Yang
- Graduate Program in Neuroscience, Boston University, Boston MA, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston MA, United States.,Center for Systems Neuroscience, Boston University, Boston MA, United States
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45
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Gagnon K, Labrosse M, Gingras MA, Godbout R. Sleep Instability Correlates with Attentional Impairment in Boys with Attention Deficit Hyperactivity Disorder. Brain Sci 2021; 11:1425. [PMID: 34827422 PMCID: PMC8615536 DOI: 10.3390/brainsci11111425] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 10/19/2021] [Accepted: 10/22/2021] [Indexed: 11/16/2022] Open
Abstract
Theoretical models of sleep and attention deficit hyperactivity disorder (ADHD) suggest that symptoms of ADHD are associated with daytime sleepiness, but it has received little support. The present study aimed at testing an alternative model involving the association of attentional instability with sleep instability, i.e., sleep stage transitions and arousals. Twelve ADHD and 15 healthy control (HC) boys aged between 8 and 12 years old underwent polysomnography recording and attentional testing. The microarousal index, the number of awakenings, and the number of stage shifts between stages 1, 2, 3, 4 and REM sleep throughout the night were computed as sleep stability parameters. Attentional functioning was assessed using the Continuous Performance Test-II. We found significantly higher sleep instability in ADHD compared to HC. Sleep arousals and stage transitions (micro arousal index, stage 4/3 and 2/4 transitions) in ADHD significantly correlated with lower attentional scores. No association whatsoever was found between sleep instability and attentional functioning in HC. The results show that sleep instability is associated with lower attentional performance in boys with ADHD, but not in HC. This could be compatible with a model according to which attention and sleep stability share a common neural substrate in ADHD.
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Affiliation(s)
- Katia Gagnon
- Sleep Laboratory and Clinic, Hôpital en Santé mentale Rivière-des-Prairies, Montréal, QC H1E 1A4, Canada; (K.G.); (M.L.); (M.-A.G.)
- Department of Psychiatry, Université de Montréal, Montréal, QC H3T 1J4, Canada
| | - Mélanie Labrosse
- Sleep Laboratory and Clinic, Hôpital en Santé mentale Rivière-des-Prairies, Montréal, QC H1E 1A4, Canada; (K.G.); (M.L.); (M.-A.G.)
| | - Marc-André Gingras
- Sleep Laboratory and Clinic, Hôpital en Santé mentale Rivière-des-Prairies, Montréal, QC H1E 1A4, Canada; (K.G.); (M.L.); (M.-A.G.)
| | - Roger Godbout
- Sleep Laboratory and Clinic, Hôpital en Santé mentale Rivière-des-Prairies, Montréal, QC H1E 1A4, Canada; (K.G.); (M.L.); (M.-A.G.)
- Department of Psychiatry, Université de Montréal, Montréal, QC H3T 1J4, Canada
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46
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Ona G, Sampedro F, Romero S, Valle M, Camacho V, Migliorelli C, Mañanas MÁ, Antonijoan RM, Puntes M, Coimbra J, Ballester MR, Garrido M, Riba J. The Kappa Opioid Receptor and the Sleep of Reason: Cortico-Subcortical Imbalance Following Salvinorin-A. Int J Neuropsychopharmacol 2021; 25:54-63. [PMID: 34537829 PMCID: PMC8756086 DOI: 10.1093/ijnp/pyab063] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 08/25/2021] [Accepted: 09/15/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The mechanisms through which kappa opioid receptor (KOR) agonists induce psychotomimetic effects are largely unknown, although the modulation of this receptor has attracted attention for its clinical use. In this work, we characterize the neuropharmacological effects of salvinorin-A, a highly selective KOR agonist. METHODS Changes in multimodal electroencephalography, single-photon emission computed tomography, and subjective effects following the acute administration of salvinorin-A are reported. The study included 2 sub-studies that employed a double-blind, crossover, randomized, placebo-controlled design. RESULTS The electroencephalography measures showed a marked increase in delta and gamma waves and a decrease in alpha waves while subjects were under the effect of salvinorin-A. Regarding single-photon emission computed tomography measures, significant decreases in regional cerebral blood flow were detected in multiple regions of the frontal, temporal, parietal, and occipital cortices. Significant regional cerebral blood flow increases were observed in some regions of the medial temporal lobe, including the amygdala, the hippocampal gyrus, and the cerebellum. The pattern of subjective effects induced by salvinorin-A was similar to those observed in relation to other psychotomimetic drugs but with an evidently dissociative nature. No dysphoric effects were reported. CONCLUSION The salvinorin-A-mediated KOR agonism induced dramatic psychotomimetic effects along with a generalized decrease in cerebral blood flow and electric activity within the cerebral cortex.
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Affiliation(s)
- Genís Ona
- Human Neuropsychopharmacology Group, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Frederic Sampedro
- Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain,Centro de Investigación en Red-Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain,Correspondence: Frederic Sampedro, PhD, Hospital de Sant Pau Research Institute, Sant Quintí Street number 77, 08041 Barcelona, Spain ()
| | - Sergio Romero
- Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Marta Valle
- Departament de Farmacologia i Terapèutica, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Valle Camacho
- Nuclear Medicine Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Carolina Migliorelli
- Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Miguel Ángel Mañanas
- Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Rosa Maria Antonijoan
- Departament de Farmacologia i Terapèutica, Universitat Autònoma de Barcelona, Barcelona, Spain,Centre d’Investigació de Medicaments, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain,Servei de Farmacologia Clínica, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain,Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain
| | - Montserrat Puntes
- Centre d’Investigació de Medicaments, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Jimena Coimbra
- Centre d’Investigació de Medicaments, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Maria Rosa Ballester
- Centre d’Investigació de Medicaments, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain,Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain,Blanquerna School of Health Science, Universitat Ramon Llull, Barcelona, Spain
| | - Maite Garrido
- Centre d’Investigació de Medicaments, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Jordi Riba
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht,the Netherlands
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Dolfen N, Veldman MP, Gann MA, von Leupoldt A, Puts NAJ, Edden RAE, Mikkelsen M, Swinnen S, Schwabe L, Albouy G, King BR. A role for GABA in the modulation of striatal and hippocampal systems under stress. Commun Biol 2021; 4:1033. [PMID: 34475515 PMCID: PMC8413374 DOI: 10.1038/s42003-021-02535-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/05/2021] [Indexed: 11/10/2022] Open
Abstract
Previous research has demonstrated that stress modulates the competitive interaction between the hippocampus and striatum, two structures known to be critically involved in motor sequence learning. These earlier investigations, however, have largely focused on blood oxygen-level dependent (BOLD) responses. No study to date has examined the link between stress, motor learning and levels of striatal and hippocampal gamma-aminobutyric acid (GABA). This knowledge gap is surprising given the known role of GABA in neuroplasticity subserving learning and memory. The current study thus examined: a) the effects of motor learning and stress on striatal and hippocampal GABA levels; and b) how learning- and stress-induced changes in GABA relate to the neural correlates of learning. To do so, fifty-three healthy young adults were exposed to a stressful or non-stressful control intervention before motor sequence learning. Striatal and hippocampal GABA levels were assessed at baseline and post-intervention/learning using magnetic resonance spectroscopy. Regression analyses indicated that stress modulated the link between striatal GABA levels and functional plasticity in both the hippocampus and striatum during learning as measured with fMRI. This study provides evidence for a role of GABA in the stress-induced modulation of striatal and hippocampal systems.
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Affiliation(s)
- Nina Dolfen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
| | - Menno P Veldman
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
| | - Mareike A Gann
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
| | | | - Nicolaas A J Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mark Mikkelsen
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
| | - Lars Schwabe
- Department of Cognitive Psychology, Institute of Psychology, University of Hamburg, Hamburg, Germany
| | - Geneviève Albouy
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.
- Leuven Brain Institute, Leuven, Belgium.
- Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, UT, USA.
| | - Bradley R King
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, Leuven, Belgium
- Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, UT, USA
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48
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Bouchard M, Lina JM, Gaudreault PO, Lafrenière A, Dubé J, Gosselin N, Carrier J. Sleeping at the switch. eLife 2021; 10:64337. [PMID: 34448453 PMCID: PMC8452310 DOI: 10.7554/elife.64337] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
Sleep slow waves are studied for their role in brain plasticity, homeostatic regulation, and their changes during aging. Here, we address the possibility that two types of slow waves co-exist in humans. Thirty young and 29 older adults underwent a night of polysomnographic recordings. Using the transition frequency, slow waves with a slow transition (slow switchers) and those with a fast transition (fast switchers) were discovered. Slow switchers had a high electroencephalography (EEG) connectivity along their depolarization transition while fast switchers had a lower connectivity dynamics and dissipated faster during the night. Aging was associated with lower temporal dissipation of sleep pressure in slow and fast switchers and lower EEG connectivity at the microscale of the oscillations, suggesting a decreased flexibility in the connectivity network of older individuals. Our findings show that two different types of slow waves with possible distinct underlying functions coexist in the slow wave spectrum.
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Affiliation(s)
- Maude Bouchard
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada.,Department of Psychology, Université de Montréal, Montreal, Canada
| | - Jean-Marc Lina
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada.,Department of Electrical Engineering, École de Technologie Supérieure, Montreal, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montreal, Canada
| | - Pierre-Olivier Gaudreault
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada
| | - Alexandre Lafrenière
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada
| | - Jonathan Dubé
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada.,Department of Psychology, Université de Montréal, Montreal, Canada
| | - Nadia Gosselin
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada.,Department of Psychology, Université de Montréal, Montreal, Canada
| | - Julie Carrier
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada.,Department of Psychology, Université de Montréal, Montreal, Canada
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49
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Park HR, Cha J, Joo EY, Kim H. Altered cerebrocerebellar functional connectivity in patients with obstructive sleep apnea and its association with cognitive function. Sleep 2021; 45:6357664. [PMID: 34432059 DOI: 10.1093/sleep/zsab209] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/19/2021] [Indexed: 11/14/2022] Open
Abstract
STUDY OBJECTIVES Previous functional MRI studies have reported altered brain networks in patients with obstructive sleep apnea (OSA). However, the extent and pattern of abnormal connectivity were inconsistent across studies, and cerebrocerebellar connections have been rarely assessed. We investigated functional network changes in cerebral and cerebellar cortices of OSA patients. METHODS Resting-state functional MRI, polysomnography and neuropsychological (NP) test data were acquired from 74 OSA patients (age: 45.8±10.7 years) and 33 healthy subjects (39.6±9.3 years). Connectivity matrices were extracted by computing correlation coefficients from various ROIs, and Fisher r-to-z transformations. In the functional connections that showed significant group differences, linear regression was conducted to examine the association between connectivity and clinical characteristics. RESULTS Patients with OSA showed reduced functional connectivity (FC) in cerebrocerebellar connections linking different functional networks, and greater FC in cortical between-network connections in prefrontal regions involving the default mode network and the control network. For OSA group, we found no correlation between FC and sleep parameters including lowest SaO2 and arousal index in the connections where significant associations were observed in healthy subjects. FC changes in default mode network (DMN) areas were related to reduced verbal fluency in OSA. Lower local efficiency and lower clustering coefficient of the salience network in the left cerebellum were also observed in OSA. CONCLUSIONS OSA affects mainly the cerebrocerebellar pathway. The disruption of function in these connections are related to sleep fragmentation and hypoxia during sleep. These abnormal network functions, especially DMN, are suggested to participate in cognitive decline of OSA.
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Affiliation(s)
- Hea Ree Park
- Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, Republic of Korea
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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50
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A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis. Brain Sci 2021; 11:brainsci11081072. [PMID: 34439690 PMCID: PMC8393958 DOI: 10.3390/brainsci11081072] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/02/2021] [Accepted: 08/13/2021] [Indexed: 10/26/2022] Open
Abstract
With the development of intensive care technology, the number of patients who survive acute severe brain injury has increased significantly. At present, it is difficult to diagnose the patients with disorders of consciousness (DOCs) because motor responses in these patients may be very limited and inconsistent. Electrophysiological criteria, such as event-related potentials or motor imagery, have also been studied to establish a diagnosis and prognosis based on command-following or active paradigms. However, the use of such task-based techniques in DOC patients is methodologically complex and requires careful analysis and interpretation. The present paper focuses on the analysis of sleep patterns for the evaluation of DOC and its relationships with diagnosis and prognosis outcomes. We discuss the concepts of sleep patterns in patients suffering from DOC, identification of this challenging population, and the prognostic value of sleep. The available literature on individuals in an unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) following traumatic or nontraumatic severe brain injury is reviewed. We can distinguish patients with different levels of consciousness by studying sleep patients with DOC. Most MCS patients have sleep and wake alternations, sleep spindles and rapid eye movement (REM) sleep, while UWS patients have few EEG changes. A large number of sleep spindles and organized sleep-wake patterns predict better clinical outcomes. It is expected that this review will promote our understanding of sleep EEG in DOC.
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