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Argyropoulou MI, Xydis VG, Astrakas LG. Functional connectivity of the pediatric brain. Neuroradiology 2024:10.1007/s00234-024-03453-5. [PMID: 39230715 DOI: 10.1007/s00234-024-03453-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024]
Abstract
PURPOSE This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders. METHODS The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents. RESULTS Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders. CONCLUSION Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.
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Affiliation(s)
- Maria I Argyropoulou
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, Ioannina, 45110, Greece.
| | - Vasileios G Xydis
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, Ioannina, 45110, Greece
| | - Loukas G Astrakas
- Medical Physics Laboratory, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, Ioannina, 45110, Greece
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2
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Dong K, Zhang L, Zhong Y, Xu T, Zhao Y, Chen S, Mahmoud SS, Fang Q. Meso-scale reorganization of local-global brain networks under mild sedation of propofol anesthesia. Neuroimage 2024; 297:120744. [PMID: 39033791 DOI: 10.1016/j.neuroimage.2024.120744] [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: 05/16/2024] [Revised: 06/30/2024] [Accepted: 07/18/2024] [Indexed: 07/23/2024] Open
Abstract
The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local-global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local-global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.
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Affiliation(s)
- Kangli Dong
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou 515063, Guangdong, China.
| | - Lu Zhang
- Department of Rehabilitation, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310027, Zhejiang, China.
| | - Yuming Zhong
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou 515063, Guangdong, China.
| | - Tao Xu
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou 515063, Guangdong, China.
| | - Yue Zhao
- Department of Urology, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen 361102, Fujian, China.
| | - Siya Chen
- Department of Computer Science, City University of Hong Kong, Hong Kong 999077, Hong Kong, China.
| | - Seedahmed S Mahmoud
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou 515063, Guangdong, China.
| | - Qiang Fang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou 515063, Guangdong, China.
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Korponay C, Janes AC, Frederick BB. Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans. Nat Hum Behav 2024; 8:1568-1580. [PMID: 38898230 DOI: 10.1038/s41562-024-01908-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 05/03/2024] [Indexed: 06/21/2024]
Abstract
Functional magnetic resonance imaging (fMRI) is a central tool for investigating human brain function, organization and disease. Here, we show that fMRI-based estimates of functional brain connectivity artifactually inflate at spatially heterogeneous rates during resting-state and task-based scans. This produces false positive connection strength changes and spatial distortion of brain connectivity maps. We demonstrate that this artefact is driven by temporal inflation of the non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning and is not addressed by standard denoising procedures. We provide evidence that sLFO inflation reflects perturbations in cerebral blood flow by respiration and heart rate changes that accompany diminishing arousal during scanning, although the mechanisms of this pathway are uncertain. Finally, we show that adding a specialized sLFO denoising procedure to fMRI processing pipelines mitigates the artifactual inflation of functional connectivity, enhancing the validity and within-scan reliability of fMRI findings.
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Affiliation(s)
- Cole Korponay
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA.
- McLean Hospital Brain Imaging Center, Belmont, MA, USA.
| | - Amy C Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Blaise B Frederick
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA
- McLean Hospital Brain Imaging Center, Belmont, MA, USA
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Mortaheb S, Fort LD, Mason NL, Mallaroni P, Ramaekers JG, Demertzi A. Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:681-692. [PMID: 38588855 DOI: 10.1016/j.bpsc.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Psilocybin is a widely studied psychedelic substance that leads to the psychedelic state, a specific altered state of consciousness. To date, the relationship between the psychedelic state's neurobiological and experiential patterns remains undercharacterized because they are often analyzed separately. We investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment. METHODS Healthy participants were randomized to receive either psilocybin (n = 22) or placebo (n = 27) and scanned for 6 minutes in an eyes-open resting state during the peak subjective drug effect (102 minutes posttreatment) in ultrahigh field 7T magnetic resonance imaging. The 5-Dimensional Altered States of Consciousness Rating Scale was administered 360 minutes after drug intake. RESULTS Under psilocybin, there were alterations across all dimensions of the 5-Dimensional Altered States of Consciousness Rating Scale and widespread increases in averaged brain functional connectivity. Time-varying functional connectivity analysis unveiled a recurrent hyperconnected pattern characterized by low blood oxygen level-dependent signal amplitude, suggesting heightened cortical arousal. In terms of neuroexperiential links, canonical correlation analysis showed higher transition probabilities to the hyperconnected pattern with feelings of oceanic boundlessness and secondly with visionary restructuralization. CONCLUSIONS Psilocybin generates profound alterations at both the brain and the experiential levels. We suggest that the brain's tendency to enter a hyperconnected-hyperarousal pattern under psilocybin represents the potential to entertain variant mental associations. These findings illuminate the intricate interplay between brain dynamics and subjective experience under psilocybin, thereby providing insights into the neurophysiology and neuroexperiential qualities of the psychedelic state.
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Affiliation(s)
- Sepehr Mortaheb
- Physiology of Cognition, GIGA Research, CRC Human Imaging Unit, University of Liège, Liège, Belgium; Fund for Scientific Research FNRS, Brussels, Belgium
| | - Larry D Fort
- Physiology of Cognition, GIGA Research, CRC Human Imaging Unit, University of Liège, Liège, Belgium; Fund for Scientific Research FNRS, Brussels, Belgium
| | - Natasha L Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Athena Demertzi
- Physiology of Cognition, GIGA Research, CRC Human Imaging Unit, University of Liège, Liège, Belgium; Fund for Scientific Research FNRS, Brussels, Belgium; Psychology & Neuroscience of Cognition, University of Liège, Liège, Belgium.
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Joliot M, Cremona S, Tzourio C, Etard O. Modulate the impact of the drowsiness on the resting state functional connectivity. Sci Rep 2024; 14:8652. [PMID: 38622265 PMCID: PMC11018752 DOI: 10.1038/s41598-024-59476-8] [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: 09/01/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.
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Affiliation(s)
- Marc Joliot
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
| | - Sandrine Cremona
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France
| | | | - Olivier Etard
- Normandie Université, UNICAEN, INSERM, COMETE U1075, CYCERON, CHU Caen, 14000, Caen, France
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Xie M, Huang Y, Cai W, Zhang B, Huang H, Li Q, Qin P, Han J. Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review. Brain Sci 2024; 14:50. [PMID: 38248265 PMCID: PMC10813043 DOI: 10.3390/brainsci14010050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/26/2023] [Accepted: 12/30/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.
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Affiliation(s)
- Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
| | - Ying Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
| | - Wendan Cai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Bingqi Zhang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Haonan Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Qingwei Li
- Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China;
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
- Pazhou Laboratory, Guangzhou 510330, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
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7
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Helakari H, Järvelä M, Väyrynen T, Tuunanen J, Piispala J, Kallio M, Ebrahimi SM, Poltojainen V, Kananen J, Elabasy A, Huotari N, Raitamaa L, Tuovinen T, Korhonen V, Nedergaard M, Kiviniemi V. Effect of sleep deprivation and NREM sleep stage on physiological brain pulsations. Front Neurosci 2023; 17:1275184. [PMID: 38105924 PMCID: PMC10722275 DOI: 10.3389/fnins.2023.1275184] [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: 08/09/2023] [Accepted: 11/02/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction Sleep increases brain fluid transport and the power of pulsations driving the fluids. We investigated how sleep deprivation or electrophysiologically different stages of non-rapid-eye-movement (NREM) sleep affect the human brain pulsations. Methods Fast functional magnetic resonance imaging (fMRI) was performed in healthy subjects (n = 23) with synchronous electroencephalography (EEG), that was used to verify arousal states (awake, N1 and N2 sleep). Cardiorespiratory rates were verified with physiological monitoring. Spectral power analysis assessed the strength, and spectral entropy assessed the stability of the pulsations. Results In N1 sleep, the power of vasomotor (VLF < 0.1 Hz), but not cardiorespiratory pulsations, intensified after sleep deprived vs. non-sleep deprived subjects. The power of all three pulsations increased as a function of arousal state (N2 > N1 > awake) encompassing brain tissue in both sleep stages, but extra-axial CSF spaces only in N2 sleep. Spectral entropy of full band and respiratory pulsations decreased most in N2 sleep stage, while cardiac spectral entropy increased in ventricles. Discussion In summary, the sleep deprivation and sleep depth, both increase the power and harmonize the spectral content of human brain pulsations.
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Affiliation(s)
- Heta Helakari
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Tommi Väyrynen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Johanna Tuunanen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Johanna Piispala
- Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Mika Kallio
- Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Seyed Mohsen Ebrahimi
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Valter Poltojainen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Ahmed Elabasy
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Maiken Nedergaard
- Center of Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark
- Center of Translational Neuromedicine, University of Rochester, Rochester, NY, United States
| | - Vesa Kiviniemi
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
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Väyrynen T, Helakari H, Korhonen V, Tuunanen J, Huotari N, Piispala J, Kallio M, Raitamaa L, Kananen J, Järvelä M, Matias Palva J, Kiviniemi V. Infra-slow fluctuations in cortical potentials and respiration drive fast cortical EEG rhythms in sleeping and waking states. Clin Neurophysiol 2023; 156:207-219. [PMID: 37972532 DOI: 10.1016/j.clinph.2023.10.013] [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: 04/18/2023] [Revised: 08/09/2023] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVE Infra-slow fluctuations (ISF, 0.008-0.1 Hz) characterize hemodynamic and electric potential signals of human brain. ISFs correlate with the amplitude dynamics of fast (>1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood-brain barrier (BBB). It is unclear if physiological rhythms like respiration drive or track fast cortical oscillations, and the role of sleep in this coupling is unknown. METHODS We used high-density full-band electroencephalography (EEG) in healthy human volunteers (N = 21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling. RESULTS The phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling being evident during sleep. Phases of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states, with different contributions. CONCLUSIONS ISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations. SIGNIFICANCE We propose that these slow physiological phases play a significant role in coordinating cortical excitability, which is a fundamental aspect of brain function.
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Affiliation(s)
- Tommi Väyrynen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland.
| | - Heta Helakari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Johanna Tuunanen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Johanna Piispala
- MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Mika Kallio
- MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Janne Kananen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, 02150 Espoo, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, University of Glasgow, United Kingdom
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu 90220, Finland
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Han F, Liu X, Mailman RB, Huang X, Liu X. Resting-state global brain activity affects early β-amyloid accumulation in default mode network. Nat Commun 2023; 14:7788. [PMID: 38012153 PMCID: PMC10682457 DOI: 10.1038/s41467-023-43627-y] [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: 09/20/2022] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
It remains unclear why β-amyloid (Aβ) plaque, a hallmark pathology of Alzheimer's disease (AD), first accumulates cortically in the default mode network (DMN), years before AD diagnosis. Resting-state low-frequency ( < 0.1 Hz) global brain activity recently was linked to AD, presumably due to its role in glymphatic clearance. Here we show that the preferential Aβ accumulation in the DMN at the early stage of Aβ pathology was associated with the preferential reduction of global brain activity in the same regions. This can be partly explained by its failure to reach these regions as propagating waves. Together, these findings highlight the important role of resting-state global brain activity in early preferential Aβ deposition in the DMN.
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Affiliation(s)
- Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Xufu Liu
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Richard B Mailman
- Departments of Neurology and Pharmacology, Translational Brain Research Center, Pennsylvania State University College of Medicine and Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Xuemei Huang
- Departments of Neurology and Pharmacology, Translational Brain Research Center, Pennsylvania State University College of Medicine and Milton S. Hershey Medical Center, Hershey, PA, USA
- Departments of Radiology, Neurosurgery, and Kinesiology, Translational Brain Research Center, Pennsylvania State University and Milton S. Hershey Medical Center, Hershey, PA, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, State College, PA, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, State College, PA, USA.
- Institute for Computational and Data Sciences, The Pennsylvania State University, State College, PA, USA.
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10
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Liu X. Decoupling Between Brain Activity and Cerebrospinal Fluid Movement in Neurological Disorders. J Magn Reson Imaging 2023:10.1002/jmri.29148. [PMID: 37991132 PMCID: PMC11109023 DOI: 10.1002/jmri.29148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/23/2023] Open
Abstract
Recent research has identified a link between the global mean signal of resting-state functional MRI (fMRI) and macro-scale cerebrospinal fluid movement, indicating the potential link between this resting-state dynamic and brain waste clearance. Consistent with this notion, the strength of this coupling has been associated with multiple neurodegenerative disease pathologies, especially the build-up of toxic proteins. This article aimed to review the latest advancements in this research area, emphasizing studies on spontaneous global brain activity that is tightly linked to the global mean resting-state fMRI signal, and aimed to discuss potential mechanisms through which this activity and associated physiological modulations might affect brain waste clearance. The available evidence supports the presence of a highly organized global brain activity that is linked to arousal and memory systems. This global brain dynamic, along with its associated physiological modulations, has the potential to influence brain waste clearance through multiple pathways through multiple pathways. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
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11
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Han F, Lee J, Chen X, Ziontz J, Ward T, Landau SM, Baker SL, Harrison TM, Jagust WJ. Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557492. [PMID: 37745434 PMCID: PMC10515801 DOI: 10.1101/2023.09.12.557492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Amyloid-β (Aβ) and tau deposition constitute Alzheimer's disease (AD) neuropathology. Cortical tau deposits first in the entorhinal cortex and hippocampus and then propagates to neocortex in an Aβ-dependent manner. Tau also tends to accumulate earlier in higher-order association cortex than in lower-order primary sensory-motor cortex. While previous research has examined the production and spread of tau, little attention has been paid to its clearance. Low-frequency (<0.1 Hz) global brain activity during the resting state is coupled with cerebrospinal fluid (CSF) flow and potentially reflects glymphatic clearance. Here we report that tau deposition in subjects with evaluated Aβ, accompanied by cortical thinning and cognitive decline, is strongly associated with decreased coupling between CSF flow and global brain activity. Substantial modulation of global brain activity is also manifested as propagating waves of brain activation between higher- and lower-order regions, resembling tau spreading. Together, the findings suggest an important role of resting-state global brain activity in AD tau pathology.
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Affiliation(s)
- Feng Han
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - JiaQie Lee
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Xi Chen
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jacob Ziontz
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Tyler Ward
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | | | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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12
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Li A, Liu H, Lei X, He Y, Wu Q, Yan Y, Zhou X, Tian X, Peng Y, Huang S, Li K, Wang M, Sun Y, Yan H, Zhang C, He S, Han R, Wang X, Liu B. Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness. Nat Commun 2023; 14:3238. [PMID: 37277338 DOI: 10.1038/s41467-023-38972-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 05/23/2023] [Indexed: 06/07/2023] Open
Abstract
Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.
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Affiliation(s)
- Ang Li
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Haiyang Liu
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100101, China
- Department of Anesthesiology, Qinghai Provincial Traffic Hospital, Xining, 810001, China
| | - Xu Lei
- Sleep and Neuroimaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yini He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yan Yan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Xin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Xiaohan Tian
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Yingjie Peng
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shangzheng Huang
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Kaixin Li
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Cheng Zhang
- The Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, 100034, China
| | - Sheng He
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Ruquan Han
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100101, China.
| | - Xiaoqun Wang
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- New Cornerstone Science Laboratory, Beijing Normal University, Beijing, 100875, China.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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13
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Zhang H, Sun H, Li J, Fan Y, Jülich ST, Lei X. Subtypes of insomnia revealed by the heterogeneity of neuroanatomical patterns: a structural MRI study. Biol Psychol 2023; 180:108591. [PMID: 37230291 DOI: 10.1016/j.biopsycho.2023.108591] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023]
Abstract
The current conflicting neuroimaging findings of insomnia disorder (ID) may be attributed to heterogeneity in ID. The present study aims to clarify the high heterogeneity in ID and examine the objective neurobiological subtypes of ID by using a novel machine learning method based on gray matter volumes (GMVs). We recruited 56 patients with ID and 73 healthy controls (HCs). The T1-weighted anatomical images were obtained for each participant. We investigated whether the ID has higher interindividual heterogeneity in GMVs. Then, we used a heterogeneous machine learning algorithm by discriminative analysis (HYDRA) to identify subtypes of ID with features of brain regional GMVs. We found that patients with ID have higher interindividual variability than HCs. HYDRA identified two distinct and reliable neuroanatomical subtypes of ID. Two subtypes showed significantly different aberrance in GMVs compared with HCs. Specifically, subtype 1 exhibited widespread decreased GMVs in some brain regions, including the right inferior temporal gyrus, left superior temporal gyrus, left precuneus, right middle cingulate, and right supplementary motor area. Subtype 2 only demonstrated increased GMVs in the right superior temporal gyrus. Additionally, the GMVs of altered brain regions in subtype 1 were significantly correlated with daytime functioning, but in subtype 2, they were significantly correlated with sleep disturbance. These results explain conflicting neuroimaging findings and propose a potential objective neurobiological classification contributing to ID's precise clinical diagnosis and treatment. DATA AND CODE AVAILABILITY: The source and means of obtaining the data used in the study have been described fully in the Methods and Materials section. The codes and data in this study are available upon a reasonable request to the corresponding author.
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Affiliation(s)
- Haobo Zhang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Haonan Sun
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Jiaqi Li
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yuhan Fan
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Simon Theodor Jülich
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China.
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14
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Onoda K, Akama H. Complex of global functional network as the core of consciousness. Neurosci Res 2023; 190:67-77. [PMID: 36535365 DOI: 10.1016/j.neures.2022.12.007] [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: 05/09/2022] [Revised: 11/20/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Finding the neural basis of consciousness is challenging, and the distribution location of the core of consciousness remains inconclusive. Integrated information theory (IIT) argues that the posterior part of the brain is the hot zone of consciousness, especially phenological consciousness. The IIT has proposed a "main complex", a set of elements determined such that the information loss in a hierarchical partition approach is the largest among those of any other supersets and subsets, as the core of consciousness in a dynamic system. This approach may be applicable not only to phenomenal but also to access-consciousness. This study estimated the main complex of brain dynamics using functional magnetic resonance imaging in Human Connectome Project (HCP) and sleep datasets. The complex analyses revealed the common networks across various tasks and rest-state in HCP, composed of executive control, salience, and dorsal/ventral attention networks. The set of networks of the main complex was maintained during sleep. However, compared with the wakefulness stage, the amount of information of these networks and the default mode network, was reduced for the hypnagogic stage. The global interconnected structure composed of major functional networks can comprise the core of consciousness.
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Affiliation(s)
- Keiichi Onoda
- Department of Psychology, Otemon Gakuin University, Ibaraki, Osaka 567-8502, Japan.
| | - Hiroyuki Akama
- Department of Life Science and Technology, Tokyo Institute of Technology, Meguro, Tokyo 152-8550, Japan
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15
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Han F, Liu X, Yang Y, Liu X. Sex-specific age-related changes in glymphatic function assessed by resting-state functional magnetic resonance imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.02.535258. [PMID: 37034667 PMCID: PMC10081329 DOI: 10.1101/2023.04.02.535258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
The glymphatic system that clears out brain wastes, such as amyloid-β (Aβ) and tau, through cerebrospinal fluid (CSF) flow may play an important role in aging and dementias. However, a lack of non-invasive tools to assess the glymphatic function in humans hindered the understanding of the glymphatic changes in healthy aging. The global infra-slow (<0.1 Hz) brain activity measured by the global mean resting-state fMRI signal (gBOLD) was recently found to be coupled by large CSF movements. This coupling has been used to measure the glymphatic process and found to correlate with various pathologies of Alzheimer's disease (AD), including Aβ pathology. Using resting-state fMRI data from a large group of 719 healthy aging participants, we examined the sex-specific changes of the gBOLD-CSF coupling, as a measure of glymphatic function, over a wide age range between 36-100 years old. We found that this coupling index remains stable before around age 55 and then starts to decline afterward, particularly in females. Menopause may contribute to the accelerated decline in females.
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Affiliation(s)
- Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
| | - Xufu Liu
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
| | - Yifan Yang
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, PA, USA
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16
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Zhang S, Goodale SE, Gold BP, Morgan VL, Englot DJ, Chang C. Vigilance associates with the low-dimensional structure of fMRI data. Neuroimage 2023; 267:119818. [PMID: 36535323 PMCID: PMC10074161 DOI: 10.1016/j.neuroimage.2022.119818] [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: 08/21/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA.
| | - Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin P Gold
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dario J Englot
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Catie Chang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
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17
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Lu Z, Huo T, Deng J, Guo F, Liu K, Liu P, Wang Q, Xiong L. Transcutaneous electrical acupoint stimulation induced sedative effects in healthy volunteers: A resting-state fMRI study. Front Hum Neurosci 2023; 16:843186. [PMID: 36741778 PMCID: PMC9893780 DOI: 10.3389/fnhum.2022.843186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 12/06/2022] [Indexed: 01/21/2023] Open
Abstract
Background Previous studies indicated the sedative effect of acupoint stimulation. However, its mechanism remains unclear. This study aimed to investigate the sedative effect of transcutaneous electrical acupoint stimulation (TEAS) and to explore the brain regions involved in this effect in healthy volunteers using functional magnetic resonance imaging (fMRI) techniques. Methods In this randomized trial, 26 healthy volunteers were randomly assigned to the TEAS group (receiving 30 min of acupoint stimulation at HT7/PC4) and the control group. fMRI was conducted before and after the intervention. The primary outcome was the BIS value during the intervention. Secondary outcomes included the amplitude of low-frequency fluctuation (ALFF) and region of interest (ROI)-based functional connectivity (FC) showed by fMRI. Results In healthy volunteers, compared with the control group, ALFF values in the TEAS-treated volunteers decreased in the left thalamus, right putamen, and midbrain, while they increased in the left orbitofrontal cortex. More FC existed between the thalamus and the insula, middle cingulate cortex, somatosensory cortex, amygdala, and putamen in subjects after TEAS treatment compared with subjects that received non-stimulation. In addition, ALFF values of the thalamus positively correlated with BIS in both groups. Conclusion Transcutaneous electrical acupoint stimulation could induce a sedative effect in healthy volunteers, and inhibition of the thalamus was among its possible mechanisms. Clinical trial registration www.ClinicalTrials.gov; identifier: NCT01896063.
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Affiliation(s)
- Zhihong Lu
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China,*Correspondence: Lize Xiong ✉
| | - Tingting Huo
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jiao Deng
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Kang Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Peng Liu
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Qiang Wang
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Lize Xiong
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China,Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China,Zhihong Lu ✉
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18
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Rajasilta O, Häkkinen S, Björnsdotter M, Scheinin NM, Lehtola SJ, Saunavaara J, Parkkola R, Lähdesmäki T, Karlsson L, Karlsson H, Tuulari JJ. Maternal psychological distress associates with alterations in resting-state low-frequency fluctuations and distal functional connectivity of the neonate medial prefrontal cortex. Eur J Neurosci 2023; 57:242-257. [PMID: 36458867 PMCID: PMC10108202 DOI: 10.1111/ejn.15882] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022]
Abstract
Prenatal stress exposure (PSE) has been observed to exert a programming effect on the developing infant brain, possibly with long-lasting consequences on temperament, cognitive functions and the risk for developing psychiatric disorders. Several prior studies have revealed that PSE associates with alterations in neonate functional connectivity in the prefrontal regions and amygdala. In this study, we explored whether maternal psychological symptoms measured during the 24th gestational week had associations with neonate resting-state network metrics. Twenty-one neonates (nine female) underwent resting-state fMRI scanning (mean gestation-corrected age at scan 26.95 days) to assess fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). The ReHo/fALFF maps were used in multiple regression analysis to investigate whether maternal self-reported anxiety and/or depressive symptoms associate with neonate functional brain features. Maternal psychological distress (composite score of depressive and anxiety symptoms) was positively associated with fALFF in the neonate medial prefrontal cortex (mPFC). Anxiety and depressive symptoms, assessed separately, exhibited similar but weaker associations. Post hoc seed-based connectivity analyses further showed that distal connectivity of mPFC covaried with PSE. No associations were found between neonate ReHo and PSE. These results offer preliminary evidence that PSE may affect functional features of the developing brain during gestation.
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Affiliation(s)
- Olli Rajasilta
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Suvi Häkkinen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Malin Björnsdotter
- The Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Noora M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Satu J Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, University of Turku and Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatric Neurology, University of Turku and Turku University Hospital, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
- Center for Population Health Research, University of Turku and Turku University Hospital, Finland
- Department of Paediatrics and Adolescent Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
- Center for Population Health Research, University of Turku and Turku University Hospital, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
- Department of Psychiatry, University of Oxford (Sigrid Juselius Fellowship), Oxford, UK
- Turku Collegium for Science and Medicine, University of Turku, Turku, Finland
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19
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Gu Y, Han F, Sainburg LE, Schade MM, Buxton OM, Duyn JH, Liu X. An orderly sequence of autonomic and neural events at transient arousal changes. Neuroimage 2022; 264:119720. [PMID: 36332366 PMCID: PMC9772091 DOI: 10.1016/j.neuroimage.2022.119720] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/15/2022] [Accepted: 10/28/2022] [Indexed: 11/09/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) allows the study of functional brain connectivity based on spatially structured variations in neuronal activity. Proper evaluation of connectivity requires removal of non-neural contributions to the fMRI signal, in particular hemodynamic changes associated with autonomic variability. Regression analysis based on autonomic indicator signals has been used for this purpose, but may be inadequate if neuronal and autonomic activities covary. To investigate this potential co-variation, we performed rsfMRI experiments while concurrently acquiring electroencephalography (EEG) and autonomic indicator signals, including heart rate, respiratory depth, and peripheral vascular tone. We identified a recurrent and systematic spatiotemporal pattern of fMRI (named as fMRI cascade), which features brief signal reductions in salience and default-mode networks and the thalamus, followed by a biphasic global change with a sensory-motor dominance. This fMRI cascade, which was mostly observed during eyes-closed condition, was accompanied by large EEG and autonomic changes indicative of arousal modulations. Importantly, the removal of the fMRI cascade dynamics from rsfMRI diminished its correlations with various signals. These results suggest that the rsfMRI correlations with various physiological and neural signals are not independent but arise, at least partly, from the fMRI cascades and associated neural and physiological changes at arousal modulations.
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Affiliation(s)
- Yameng Gu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lucas E Sainburg
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Margeaux M Schade
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA 16802, USA
| | - Orfeu M Buxton
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA 16802, USA
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA; Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
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20
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Mind blanking is a distinct mental state linked to a recurrent brain profile of globally positive connectivity during ongoing mentation. Proc Natl Acad Sci U S A 2022; 119:e2200511119. [PMID: 36194631 PMCID: PMC9564098 DOI: 10.1073/pnas.2200511119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Mind blanking (MB) is a waking state during which we do not report any mental content. The phenomenology of MB challenges the view of a constantly thinking mind. Here, we comprehensively characterize the MB's neurobehavioral profile with the aim to delineate its role during ongoing mentation. Using functional MRI experience sampling, we show that the reportability of MB is less frequent, faster, and with lower transitional dynamics than other mental states, pointing to its role as a transient mental relay. Regarding its neural underpinnings, we observed higher global signal amplitude during MB reports, indicating a distinct physiological state. Using the time-varying functional connectome, we show that MB reports can be classified with high accuracy, suggesting that MB has a unique neural composition. Indeed, a pattern of global positive-phase coherence shows the highest similarity to the connectivity patterns associated with MB reports. We interpret this pattern's rigid signal architecture as hindering content reportability due to the brain's inability to differentiate signals in an informative way. Collectively, we show that MB has a unique neurobehavioral profile, indicating that nonreportable mental events can happen during wakefulness. Our results add to the characterization of spontaneous mentation and pave the way for more mechanistic investigations of MB's phenomenology.
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21
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Gonzalez-Castillo J, Fernandez IS, Handwerker DA, Bandettini PA. Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness. Neuroimage 2022; 259:119424. [PMID: 35781079 PMCID: PMC9377091 DOI: 10.1016/j.neuroimage.2022.119424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/16/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022] Open
Abstract
Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD.
| | - Isabel S Fernandez
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD; Functional MRI Core, National Institutes of Health, Bethesda, MD
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22
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Wang Y, van Gelderen P, de Zwart JA, Özbay PS, Mandelkow H, Picchioni D, Duyn JH. Cerebrovascular activity is a major factor in the cerebrospinal fluid flow dynamics. Neuroimage 2022; 258:119362. [PMID: 35688316 PMCID: PMC9271599 DOI: 10.1016/j.neuroimage.2022.119362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022] Open
Abstract
Cerebrospinal fluid (CSF) provides physical protection to the central nervous system as well as an essential homeostatic environment for the normal functioning of neurons. Additionally, it has been proposed that the pulsatile movement of CSF may assist in glymphatic clearance of brain metabolic waste products implicated in neurodegeneration. In awake humans, CSF flow dynamics are thought to be driven primarily by cerebral blood volume fluctuations resulting from a number of mechanisms, including a passive vascular response to blood pressure variations associated with cardiac and respiratory cycles. Recent research has shown that mechanisms that rely on the action of vascular smooth muscle cells ("cerebrovascular activity") such as neuronal activity, changes in intravascular CO2, and autonomic activation from the brainstem, may lead to CSF pulsations as well. Nevertheless, the relative contribution of these mechanisms to CSF flow remains unclear. To investigate this further, we developed an MRI approach capable of disentangling and quantifying CSF flow components of different time scales associated with these mechanisms. This approach was evaluated on human control subjects (n = 12) performing intermittent voluntary deep inspirations, by determining peak flow velocities and displaced volumes between these mechanisms in the fourth ventricle. We found that peak flow velocities were similar between the different mechanisms, while displaced volumes per cycle were about a magnitude larger for deep inspirations. CSF flow velocity peaked at around 10.4 s (range 7.1-14.8 s, n = 12) following deep inspiration, consistent with known cerebrovascular activation delays for this autonomic challenge. These findings point to an important role of cerebrovascular activity in the genesis of CSF pulsations. Other regulatory triggers for cerebral blood flow such as autonomic arousal and orthostatic challenges may create major CSF pulsatile movement as well. Future quantitative comparison of these and possibly additional types of CSF pulsations with the proposed approach may help clarify the conditions that affect CSF flow dynamics.
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Affiliation(s)
- Yicun Wang
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
| | - Peter van Gelderen
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Jacco A de Zwart
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Pinar S Özbay
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Hendrik Mandelkow
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Dante Picchioni
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
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23
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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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24
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Newell DW, Nedergaard M, Aaslid R. Physiological Mechanisms and Significance of Intracranial B Waves. Front Neurol 2022; 13:872701. [PMID: 35651339 PMCID: PMC9149212 DOI: 10.3389/fneur.2022.872701] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/20/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Recently published studies have described slow spontaneous cerebral blood flow (CBF) and cerebrospinal fluid (CSF) oscillations measured by magnetic resonance imaging (MRI) as potential drivers of brain glymphatic flow, with a similar frequency as intracranial B-waves. Aiming to establish the relationship between these waveforms, we performed additional analysis of frequency and waveform parameters, of our previously published transcranial Doppler (TCD) and intracranial pressure (ICP) recordings of intracranial B waves, to compare to published MRI frequency measurements of CBF and CSF slow oscillations. Patients and Methods We analyzed digital recordings of B waves in 29 patients with head injury, including middle cerebral artery (MCA) flow velocity (FV), ICP, end tidal CO2, and arterial blood pressure (ABP). A subset of these recordings demonstrated high B wave activity and was further analyzed for parameters including frequency, interaction, and waveform distribution curve features. These measures were compared to published similar measurements of spontaneous CBF and CSF fluctuations evaluated using MRI. Results In patients with at least 10% amplitude B wave activity, the MCA blood flow velocity oscillations comprising the B waves, had a maximum amplitude at 0.0245 Hz, and time derivative a maximum amplitude at 0.035 Hz. The frequency range of the B waves was between 0.6–2.3 cycles per min (0.011-0.038 Hz), which is in the same range as MRI measured CBF slow oscillations, reported in human volunteers. Waveform asymmetry in MCA velocity and ICP cycles during B waves, was also similar to published MRI measured CBF slow oscillations. Cross-correlation analysis showed equivalent time derivatives of FV vs. ICP in B waves, compared to MRI measured CBF slow oscillations vs. CSF flow fluctuations. Conclusions The TCD and ICP recordings of intracranial B waves show a similar frequency range as CBF and CSF flow oscillations measured using MRI, and share other unique morphological wave features. These findings strongly suggest a common physiological mechanism underlying the two classes of phenomena. The slow blood flow and volume oscillations causing intracranial B waves appear to be part of a cascade that may provide a significant driving force for compartmentalized CSF movement and facilitate glymphatic flow.
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Affiliation(s)
- David W Newell
- Department of Neurosurgery, Seattle Neuroscience Institute, Seattle, WA, United States
| | - Maiken Nedergaard
- Department of Basic and Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Translational Neuromedicine, University of Rochester Medical School, Rochester, NY, United States
| | - Rune Aaslid
- Department of Neurosurgery, University of Bern, Bern, Switzerland
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25
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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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26
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Santarnecchi E, Sprugnoli G, Sicilia I, Dukart J, Neri F, Romanella SM, Cerase A, Vatti G, Rocchi R, Rossi A. Thalamic altered spontaneous activity and connectivity in obstructive sleep apnea syndrome. J Neuroimaging 2022; 32:314-327. [PMID: 34964182 PMCID: PMC9094633 DOI: 10.1111/jon.12952] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/30/2021] [Accepted: 11/15/2021] [Indexed: 10/19/2022] Open
Abstract
BACKGROUND AND PURPOSE Obstructive sleep apnea (OSA) syndrome is a sleep disorder characterized by excessive snoring, repetitive apneas, and nocturnal arousals, that leads to fragmented sleep and intermittent nocturnal hypoxemia. Morphometric and functional brain alterations in cortical and subcortical structures have been documented in these patients via magnetic resonance imaging (MRI), even if correlational data between the alterations in the brain and cognitive and clinical indexes are still not reported. METHODS We examined the impact of OSA on brain spontaneous activity by measuring the fractional amplitude of low-frequency fluctuations (fALFF) in resting-state functional MRI data of 20 drug-naïve patients with OSA syndrome and 20 healthy controls matched for age, gender, and body mass index. RESULTS Patients showed a pattern of significantly abnormal subcortical functional activity as compared to controls, with increased activity selectively involving the thalami, specifically their intrinsic nuclei connected to somatosensory and motor-premotor cortical regions. Using these nuclei as seed regions, the subsequent functional connectivity analysis highlighted an increase in patients' thalamocortical connectivity at rest. Additionally, the correlation between fALFF and polysomnographic data revealed a possible link between OSA severity and fALFF of regions belonging to the central autonomic network. CONCLUSIONS Our results suggest a hyperactivation in thalamic diurnal activity in patients with OSA syndrome, which we interpret as a possible consequence of increased thalamocortical circuitry activation during nighttime due to repeated arousals.
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Affiliation(s)
- Emiliano Santarnecchi
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Giulia Sprugnoli
- Siena Brain Investigation & Neuromodulation Laboratory, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
| | - Isabella Sicilia
- Center for Sleep Study, University of Siena School of Medicine, Siena, Italy
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Francesco Neri
- Siena Brain Investigation & Neuromodulation Laboratory, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
| | - Sara M. Romanella
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Siena Brain Investigation & Neuromodulation Laboratory, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
| | - Alfonso Cerase
- Department of Medicine, Surgery and Neuroscience, Section of Neuroradiology, University of Siena, Siena, Italy
| | - Giampaolo Vatti
- Center for Sleep Study, University of Siena School of Medicine, Siena, Italy
| | - Raffaele Rocchi
- Center for Sleep Study, University of Siena School of Medicine, Siena, Italy
| | - Alessandro Rossi
- Siena Brain Investigation & Neuromodulation Laboratory, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
- Center for Sleep Study, University of Siena School of Medicine, Siena, Italy
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27
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Cauzzo S, Singh K, Stauder M, García-Gomar MG, Vanello N, Passino C, Staab J, Indovina I, Bianciardi M. Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI. Neuroimage 2022; 250:118925. [PMID: 35074504 DOI: 10.1016/j.neuroimage.2022.118925] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 11/24/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus - alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.
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Affiliation(s)
- Simone Cauzzo
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Life Sciences Institute, Sant'Anna School of Advanced Studies, Pisa, Italy.
| | - Kavita Singh
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Matthew Stauder
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - María Guadalupe García-Gomar
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Nicola Vanello
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy
| | - Claudio Passino
- Life Sciences Institute, Sant'Anna School of Advanced Studies, Pisa, Italy; Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Jeffrey Staab
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States; Department of Otorhinolaryngology - Head and Neck Surgery, Mayo Clinic, Rochester, MN, United States
| | - Iole Indovina
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Italy; Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Marta Bianciardi
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Division of Sleep Medicine, Harvard University, Boston, MA.
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28
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Resting state network connectivity is attenuated by fMRI acoustic noise. Neuroimage 2021; 247:118791. [PMID: 34920084 DOI: 10.1016/j.neuroimage.2021.118791] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/21/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal. METHODS Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity. RESULTS fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity. DISCUSSION rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.
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Preoperative Assessment of Language Dominance through Combined Resting-State and Task-Based Functional Magnetic Resonance Imaging. J Pers Med 2021; 11:jpm11121342. [PMID: 34945814 PMCID: PMC8706548 DOI: 10.3390/jpm11121342] [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: 10/24/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 11/17/2022] Open
Abstract
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years, the resting-state fMRI (RS-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Twenty patients suffering from brain lesions close to supposed language-relevant cortical areas were included. RS-fMRI and task-based (TB-fMRI) were performed for the purpose of preoperative language assessment. TB-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language-critical and language-supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the TB-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice index. Thereby, the RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. In general, the results suggest that determining language dominance in the human brain is feasible both with TB-fMRI and RS-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
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30
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Martin CG, He BJ, Chang C. State-related neural influences on fMRI connectivity estimation. Neuroimage 2021; 244:118590. [PMID: 34560268 PMCID: PMC8815005 DOI: 10.1016/j.neuroimage.2021.118590] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/11/2021] [Accepted: 09/16/2021] [Indexed: 12/01/2022] Open
Abstract
The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.
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Affiliation(s)
- Caroline G Martin
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Departments of Neurology, Neuroscience & Physiology, and Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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31
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Intraoperative Resting-State Functional Connectivity Based on RGB Imaging. Diagnostics (Basel) 2021; 11:diagnostics11112067. [PMID: 34829414 PMCID: PMC8625493 DOI: 10.3390/diagnostics11112067] [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] [Received: 09/16/2021] [Revised: 10/27/2021] [Accepted: 11/04/2021] [Indexed: 11/26/2022] Open
Abstract
RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. Magnetic resonance imaging (fMRI) and functional near infra-red spectroscopy (fNIRS) resting-state procedures cannot be used intraoperatively but RGB imaging provides an ideal solution to identify resting-state networks during a neurosurgical operation. We applied resting-state methodologies to intraoperative RGB imaging and evaluated their ability to identify resting-state networks. We adapted two resting-state methodologies from fMRI for the identification of resting-state networks using intraoperative RGB imaging. Measurements were performed in 3 patients who underwent resection of lesions adjacent to motor sites. The resting-state networks were compared to the identifications provided by RGB task-based imaging and electrical brain stimulation. Intraoperative RGB resting-state networks corresponded to RGB task-based imaging (DICE:0.55±0.29). Resting state procedures showed a strong correspondence between them (DICE:0.66±0.11) and with electrical brain stimulation. RGB imaging is a relevant technique for intraoperative resting-state networks identification. Intraoperative resting-state imaging has several advantages compared to functional task-based analyses: data acquisition is shorter, less complex, and less demanding for the patients, especially for those unable to perform the tasks.
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Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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Han F, Brown GL, Zhu Y, Belkin-Rosen AE, Lewis MM, Du G, Gu Y, Eslinger PJ, Mailman RB, Huang X, Liu X. Decoupling of Global Brain Activity and Cerebrospinal Fluid Flow in Parkinson's Disease Cognitive Decline. Mov Disord 2021; 36:2066-2076. [PMID: 33998068 PMCID: PMC8453044 DOI: 10.1002/mds.28643] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 04/19/2021] [Accepted: 04/26/2021] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Deposition and spreading of misfolded proteins (α-synuclein and tau) have been linked to Parkinson's disease cognitive dysfunction. The glymphatic system may play an important role in the clearance of these toxic proteins via cerebrospinal fluid (CSF) flow through perivascular and interstitial spaces. Recent studies discovered that sleep-dependent global brain activity is coupled to CSF flow, which may reflect glymphatic function. OBJECTIVE The objective of this current study was to determine if the decoupling of brain activity-CSF flow is linked to Parkinson's disease cognitive dysfunction. METHODS Functional and structural MRI data, clinical motor (Unified Parkinson's Disease Rating Scale), and cognitive (Montreal Cognitive Assessment [MoCA]) scores were collected from 60 Parkinson's disease and 58 control subjects. Parkinson's disease patients were subgrouped into those with mild cognitive impairment (MoCA < 26), n = 31, and those without mild cognitive impairment (MoCA ≥ 26), n = 29. The coupling strength between the resting-state global blood-oxygen-level-dependent signal and associated CSF flow was quantified, compared among groups, and associated with clinical and structural measurements. RESULTS Global blood-oxygen-level-dependent signal-CSF coupling decreased significantly (P < 0.006) in Parkinson's disease patients showing mild cognitive impairment, compared with those without mild cognitive impairment and controls. Reduced global blood-oxygen-level-dependent signal-CSF coupling was associated with decreased MoCA scores present in Parkinson's disease patients (P = 0.005) but not in controls (P = 0.65). Weaker global blood-oxygen-level-dependent signal-CSF coupling in Parkinson's disease patients also was associated with a thinner right entorhinal cortex (Spearman's correlation, -0.36; P = 0.012), an early structural change often seen in Alzheimer's disease. CONCLUSIONS The decoupling between global brain activity and associated CSF flow is related to Parkinson's disease cognitive impairment. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
| | - Gregory L. Brown
- Department of Engineering Science and Mechanics, The Pennsylvania State University, PA, USA
- Department of Neurology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Yalin Zhu
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
| | | | - Mechelle M. Lewis
- Department of Neurology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Pharmacology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Guangwei Du
- Department of Neurology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Yameng Gu
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
| | - Paul J. Eslinger
- Department of Neurology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Radiology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Richard B. Mailman
- Department of Neurology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Pharmacology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Xuemei Huang
- Department of Neurology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Pharmacology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Radiology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Neurosurgery, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Kinesiology, Pennsylvania State University Milton S. Hershey Medical Center, Hershey, PA, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, PA, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, PA, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, PA, USA
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Zhang J, Kucyi A, Raya J, Nielsen AN, Nomi JS, Damoiseaux JS, Greene DJ, Horovitz SG, Uddin LQ, Whitfield-Gabrieli S. What have we really learned from functional connectivity in clinical populations? Neuroimage 2021; 242:118466. [PMID: 34389443 DOI: 10.1016/j.neuroimage.2021.118466] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 02/09/2023] Open
Abstract
Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
| | - Aaron Kucyi
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jovicarole Raya
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Lucina Q Uddin
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
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35
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Gu Y, Sainburg LE, Kuang S, Han F, Williams JW, Liu Y, Zhang N, Zhang X, Leopold DA, Liu X. Brain Activity Fluctuations Propagate as Waves Traversing the Cortical Hierarchy. Cereb Cortex 2021; 31:3986-4005. [PMID: 33822908 PMCID: PMC8485153 DOI: 10.1093/cercor/bhab064] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain's functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.
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Affiliation(s)
- Yameng Gu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Lucas E Sainburg
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Sizhe Kuang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Feng Han
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Jack W Williams
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Yikang Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
- The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Xiang Zhang
- College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, 16802, USA
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Xiao Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
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36
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Bansal K, Garcia JO, Lauharatanahirun N, Muldoon SF, Sajda P, Vettel JM. Scale-specific dynamics of high-amplitude bursts in EEG capture behaviorally meaningful variability. Neuroimage 2021; 241:118425. [PMID: 34303795 DOI: 10.1016/j.neuroimage.2021.118425] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/25/2021] [Accepted: 07/21/2021] [Indexed: 10/20/2022] Open
Abstract
Cascading high-amplitude bursts in neural activity, termed avalanches, are thought to provide insight into the complex spatially distributed interactions in neural systems. In human neuroimaging, for example, avalanches occurring during resting-state show scale-invariant dynamics, supporting the hypothesis that the brain operates near a critical point that enables long range spatial communication. In fact, it has been suggested that such scale-invariant dynamics, characterized by a power-law distribution in these avalanches, are universal in neural systems and emerge through a common mechanism. While the analysis of avalanches and subsequent criticality is increasingly seen as a framework for using complex systems theory to understand brain function, it is unclear how the framework would account for the omnipresent cognitive variability, whether across individuals or tasks. To address this, we analyzed avalanches in the EEG activity of healthy humans during rest as well as two distinct task conditions that varied in cognitive demands and produced behavioral measures unique to each individual. In both rest and task conditions we observed that avalanche dynamics demonstrate scale-invariant characteristics, but differ in their specific features, demonstrating individual variability. Using a new metric we call normalized engagement, which estimates the likelihood for a brain region to produce high-amplitude bursts, we also investigated regional features of avalanche dynamics. Normalized engagement showed not only the expected individual and task dependent variability, but also scale-specificity that correlated with individual behavior. Our results suggest that the study of avalanches in human brain activity provides a tool to assess cognitive variability. Our findings expand our understanding of avalanche features and are supportive of the emerging theoretical idea that the dynamics of an active human brain operate close to a critical-like region and not a singular critical-state.
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Affiliation(s)
- Kanika Bansal
- Human Research and Engineering Directorate, US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
| | - Javier O Garcia
- Human Research and Engineering Directorate, US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Nina Lauharatanahirun
- Department of Biomedical Engineering and Department of Biobehavioral Health, Pennsylvania State University, State College, PA 16802, USA
| | - Sarah F Muldoon
- Mathematics Department, CDSE Program, and Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY 14260, USA
| | - Paul Sajda
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA; Data Science Institute, Columbia University, New York, NY 10027, USA
| | - Jean M Vettel
- Human Research and Engineering Directorate, US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
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37
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Cardis R, Lecci S, Fernandez LM, Osorio-Forero A, Chu Sin Chung P, Fulda S, Decosterd I, Lüthi A. Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain. eLife 2021; 10:65835. [PMID: 34227936 PMCID: PMC8291975 DOI: 10.7554/elife.65835] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 07/02/2021] [Indexed: 12/22/2022] Open
Abstract
Frequent nightly arousals typical for sleep disorders cause daytime fatigue and present health risks. As such arousals are often short, partial, or occur locally within the brain, reliable characterization in rodent models of sleep disorders and in human patients is challenging. We found that the EEG spectral composition of non-rapid eye movement sleep (NREMS) in healthy mice shows an infraslow (~50 s) interval over which microarousals appear preferentially. NREMS could hence be vulnerable to abnormal arousals on this time scale. Chronic pain is well-known to disrupt sleep. In the spared nerve injury (SNI) mouse model of chronic neuropathic pain, we found more numerous local cortical arousals accompanied by heart rate increases in hindlimb primary somatosensory, but not in prelimbic, cortices, although sleep macroarchitecture appeared unaltered. Closed-loop mechanovibrational stimulation further revealed higher sensory arousability. Chronic pain thus preserved conventional sleep measures but resulted in elevated spontaneous and evoked arousability. We develop a novel moment-to-moment probing of NREMS vulnerability and propose that chronic pain-induced sleep complaints arise from perturbed arousability.
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Affiliation(s)
- Romain Cardis
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Pain Center, Department of Anesthesiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Sandro Lecci
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Laura Mj Fernandez
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Alejandro Osorio-Forero
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Paul Chu Sin Chung
- Pain Center, Department of Anesthesiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Stephany Fulda
- Sleep Medicine Unit, Neurocenter of Southern Switzerland, Civic Hospital (EOC) of Lugano, Lugano, Switzerland
| | - Isabelle Decosterd
- Pain Center, Department of Anesthesiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Anita Lüthi
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
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38
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Reduced coupling between cerebrospinal fluid flow and global brain activity is linked to Alzheimer disease-related pathology. PLoS Biol 2021; 19:e3001233. [PMID: 34061820 PMCID: PMC8168893 DOI: 10.1371/journal.pbio.3001233] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 04/14/2021] [Indexed: 11/19/2022] Open
Abstract
The glymphatic system plays an important role in clearing the amyloid-β (Aβ) and tau proteins that are closely linked to Alzheimer disease (AD) pathology. Glymphatic clearance, as well as Aβ accumulation, is highly dependent on sleep, but the sleep-dependent driving forces behind cerebrospinal fluid (CSF) movements essential to the glymphatic flux remain largely unclear. Recent studies have reported that widespread, high-amplitude spontaneous brain activations in the drowsy state and during sleep, which are shown as large global signal peaks in resting-state functional magnetic resonance imaging (rsfMRI), are coupled with CSF movements, suggesting their potential link to glymphatic flux and metabolite clearance. By analyzing multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project, here we showed that the coupling between the global fMRI signal and CSF influx is correlated with AD-related pathology, including various risk factors for AD, the severity of AD-related diseases, the cortical Aβ level, and cognitive decline over a 2-year follow-up. These results provide critical initial evidence for involvement of sleep-dependent global brain activity, as well as the associated physiological modulations, in the clearance of AD-related brain waste. This study reveals strong coupling between the global fMRI signal and cerebrospinal fluid influx, finding that this is correlated with Alzheimer’s disease-related pathology, disease severity, and cognitive decline. This supports a link between spontaneous low-frequency brain dynamics and Alzheimer’s disease pathology, presumably due to their role in glymphatic clearance.
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Song X, Chen X, Yuksel C, Yuan J, Pizzagalli DA, Forester B, Öngür D, Du F. Bioenergetics and abnormal functional connectivity in psychotic disorders. Mol Psychiatry 2021; 26:2483-2492. [PMID: 33398087 PMCID: PMC8254819 DOI: 10.1038/s41380-020-00993-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 12/22/2022]
Abstract
Psychotic Disorders such as schizophrenia (SZ) and bipolar disorder (BD) are characterized by abnormal functional connectivity (FC) within neural networks such as the default mode network (DMN), as well as attenuated anticorrelation between DMN and task-positive networks (TPN). Bioenergetic processes are critical for synaptic connectivity and are also abnormal in psychotic disorders. We therefore examined the association between brain energy metabolism and FC in psychotic disorders. 31P magnetization transfer spectroscopy from medial prefrontal cortex (MPFC) and whole-brain fMRI data were collected from demographically matched groups of SZ, BD, and healthy control (HC) subjects. The creatine kinase (CK) reaction flux calculated from spectroscopy was used as an index of regional energy production rate. FC maps were generated with MPFC as the seed region. Compared to HC, SZ showed significantly lower CK flux, while both BD and SZ patients showed decreased anticorrelation between MPFC and TPN. CK flux was significantly correlated with FC between MPFC and other DMN nodes in HC. This positive correlation was reduced modestly in BD and strongly in SZ. CK flux was negatively correlated with the anticorrelation between MPFC and TPN in HC, but this relationship was not observed in BD or SZ. These results indicate that MPFC energy metabolism rates are associated with stronger FC within networks and stronger anticorrelation between networks in HC. However, this association is decreased in SZ and BD, where bioenergetic and FC abnormalities are evident. This pattern may suggest that impairment in energy production in psychotic disorders underlies the impaired neural connectivity.
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Affiliation(s)
- Xiaopeng Song
- Psychotic Disorders Division, McLean Hospital, 02478, USA,McLean Imaging Center, McLean Hospital, 02478, USA,Harvard Medical School, Boston, Massachusetts, 02115, USA,These authors contributed equally to this work
| | - Xi Chen
- Psychotic Disorders Division, McLean Hospital, 02478, USA,McLean Imaging Center, McLean Hospital, 02478, USA,Harvard Medical School, Boston, Massachusetts, 02115, USA,These authors contributed equally to this work
| | - Cagri Yuksel
- Psychotic Disorders Division, McLean Hospital, 02478, USA,Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Junliang Yuan
- McLean Imaging Center, McLean Hospital, 02478, USA,National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Diego A. Pizzagalli
- McLean Imaging Center, McLean Hospital, 02478, USA,Harvard Medical School, Boston, Massachusetts, 02115, USA,Center for Depression, Anxiety and Stress Research, McLean Hospital, 02478, USA
| | - Brent Forester
- Harvard Medical School, Boston, Massachusetts, 02115, USA,Division of Geriatric Psychiatry, McLean Hospital, 02478, USA
| | - Dost Öngür
- Psychotic Disorders Division, McLean Hospital, 02478, USA,McLean Imaging Center, McLean Hospital, 02478, USA,Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Fei Du
- Psychotic Disorders Division, McLean Hospital, Belmont, MA, 02478, USA. .,McLean Imaging Center, McLean Hospital, Belmont, MA, 02478, USA. .,Harvard Medical School, Boston, MA, 02115, USA.
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40
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Soon CS, Vinogradova K, Ong JL, Calhoun VD, Liu T, Zhou JH, Ng KK, Chee MWL. Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes. Neuroimage 2021; 237:118129. [PMID: 33951513 DOI: 10.1016/j.neuroimage.2021.118129] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 04/04/2021] [Accepted: 04/28/2021] [Indexed: 01/16/2023] Open
Abstract
Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.
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Affiliation(s)
- Chun Siong Soon
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore.
| | - Ksenia Vinogradova
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, USA
| | - Thomas Liu
- UCSD Center for Functional MRI and Department of Radiology, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Juan Helen Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael W L Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore.
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Escrichs A, Biarnes C, Garre-Olmo J, Fernández-Real JM, Ramos R, Pamplona R, Brugada R, Serena J, Ramió-Torrentà L, Coll-De-Tuero G, Gallart L, Barretina J, Vilanova JC, Mayneris-Perxachs J, Essig M, Figley CR, Pedraza S, Puig J, Deco G. Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club. Cereb Cortex 2021; 31:2466-2481. [PMID: 33350451 DOI: 10.1093/cercor/bhaa367] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/19/2022] Open
Abstract
Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.
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Affiliation(s)
- Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Carles Biarnes
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Josep Garre-Olmo
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Institut d'Assistència Sanitària, Salt (Girona), Spain
| | - José Manuel Fernández-Real
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Rafel Ramos
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,Primary Care Services, Catalan Institute of Health (ICS), Girona, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, Faculty of Medicine, University of Lleida-IRBLleida, Lleida, Spain
| | - Ramon Brugada
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Cardiovascular Genetics Center, IDIBGI, CIBER-CV, Girona, Spain
| | - Joaquin Serena
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Lluís Ramió-Torrentà
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Gabriel Coll-De-Tuero
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Luís Gallart
- Biobanc, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Jordi Barretina
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Joan C Vilanova
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Jordi Mayneris-Perxachs
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Marco Essig
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Chase R Figley
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Salvador Pedraza
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Josep Puig
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,Institucio Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Catalonia, Spain.,Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences, Leipzig, Germany.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
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42
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Thomas J, Sharma D, Mohanta S, Jain N. Resting-State functional networks of different topographic representations in the somatosensory cortex of macaque monkeys and humans. Neuroimage 2020; 228:117694. [PMID: 33385552 DOI: 10.1016/j.neuroimage.2020.117694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/15/2020] [Accepted: 12/21/2020] [Indexed: 11/16/2022] Open
Abstract
Information processing in the brain is mediated through a complex functional network architecture whose comprising nodes integrate and segregate themselves on different timescales. To gain an understanding of the network function it is imperative to identify and understand the network structure with respect to the underlying anatomical connectivity and the topographic organization. Here we show that the previously described resting-state network for the somatosensory area 3b comprises of distinct networks that are characteristic for different topographic representations. Seed-based resting-state functional connectivity analysis in macaque monkeys and humans using BOLD-fMRI signals from the face, the hand and rest of the medial somatosensory representations of area 3b revealed different correlation patterns. Both monkeys and humans have many similarities in the connectivity networks, although the networks are more complex in humans with many more nodes. In both the species face area network has the highest ipsilateral and contralateral connectivity, which included areas 3b and 4, and ventral premotor area. The area 3b hand network included ipsilateral hand representation in area 4. The emergent functional network structures largely reflect the known anatomical connectivity. Our results show that different body part representations in area 3b have independent functional networks perhaps reflecting differences in the behavioral use of different body parts. The results also show that large cortical areas if considered together, do not give a complete and accurate picture of the network architecture.
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Affiliation(s)
- John Thomas
- National Brain Research Centre, NH 8, Manesar 122052, Haryana, India
| | - Dixit Sharma
- National Brain Research Centre, NH 8, Manesar 122052, Haryana, India
| | - Sounak Mohanta
- National Brain Research Centre, NH 8, Manesar 122052, Haryana, India
| | - Neeraj Jain
- National Brain Research Centre, NH 8, Manesar 122052, Haryana, India.
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43
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Higgins C, Liu Y, Vidaurre D, Kurth-Nelson Z, Dolan R, Behrens T, Woolrich M. Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron 2020; 109:882-893.e7. [PMID: 33357412 PMCID: PMC7927915 DOI: 10.1016/j.neuron.2020.12.007] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 10/15/2020] [Accepted: 12/04/2020] [Indexed: 11/04/2022]
Abstract
Our brains at rest spontaneously replay recently acquired information, but how this process is orchestrated to avoid interference with ongoing cognition is an open question. Here we investigated whether replay coincided with spontaneous patterns of whole-brain activity. We found, in two separate datasets, that replay sequences were packaged into transient bursts occurring selectively during activation of the default mode network (DMN) and parietal alpha networks. These networks are believed to support inwardly oriented attention and inhibit bottom-up sensory processing and were characterized by widespread synchronized oscillations coupled to increases in high frequency power, mechanisms thought to coordinate information flow between disparate cortical areas. Our data reveal a tight correspondence between two widely studied phenomena in neural physiology and suggest that the DMN may coordinate replay bursts in a manner that minimizes interference with ongoing cognition. Replay in humans coincides with activity in specific resting brain networks Clusters of heightened default mode and alpha activity are linked to replay bursts These networks are characterized by highly synchronized brain-wide oscillations High-frequency power bursts are uniquely linked to default mode network activation
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Affiliation(s)
- Cameron Higgins
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - Yunzhe Liu
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Diego Vidaurre
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Zeb Kurth-Nelson
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK; Deepmind, London, UK
| | - Ray Dolan
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Timothy Behrens
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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44
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Signorelli CM, Uhrig L, Kringelbach M, Jarraya B, Deco G. Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss. Neuroimage 2020; 227:117618. [PMID: 33307225 DOI: 10.1016/j.neuroimage.2020.117618] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/14/2020] [Accepted: 12/01/2020] [Indexed: 11/30/2022] Open
Abstract
Anesthesia induces a reconfiguration of the repertoire of functional brain states leading to a high function-structure similarity. However, it is unclear how these functional changes lead to loss of consciousness. Here we suggest that the mechanism of conscious access is related to a general dynamical rearrangement of the intrinsic hierarchical organization of the cortex. To measure cortical hierarchy, we applied the Intrinsic Ignition analysis to resting-state fMRI data acquired in awake and anesthetized macaques. Our results reveal the existence of spatial and temporal hierarchical differences of neural activity within the macaque cortex, with a strong modulation by the depth of anesthesia and the employed anesthetic agent. Higher values of Intrinsic Ignition correspond to rich and flexible brain dynamics whereas lower values correspond to poor and rigid, structurally driven brain dynamics. Moreover, spatial and temporal hierarchical dimensions are disrupted in a different manner, involving different hierarchical brain networks. All together suggest that disruption of brain hierarchy is a new signature of consciousness loss.
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Affiliation(s)
- Camilo Miguel Signorelli
- Department of Computer Science, University of Oxford, UK; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, France; Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Spain.
| | - Lynn Uhrig
- Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, France; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Direction de la Recherche Fondamentale, NeuroSpin Center, France; Department of Anesthesiology and Critical Care, Necker Hospital, University Paris Descartes, France; Department of Anesthesiology and Critical Care, Sainte-Anne Hospital, University Paris Descartes, France
| | - Morten Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, UK; Department of Psychiatry, University of Oxford, UK
| | - Bechir Jarraya
- Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, France; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Direction de la Recherche Fondamentale, NeuroSpin Center, France; Neurosurgery Department, Foch Hospital, Suresnes, France; University of Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, France.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Spain; Institució Catalana de la Recerca i Estudis Avançats, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
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45
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Turner KL, Gheres KW, Proctor EA, Drew PJ. Neurovascular coupling and bilateral connectivity during NREM and REM sleep. eLife 2020; 9:62071. [PMID: 33118932 PMCID: PMC7758068 DOI: 10.7554/elife.62071] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/28/2020] [Indexed: 12/11/2022] Open
Abstract
To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.
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Affiliation(s)
- Kevin L Turner
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States.,Center for Neural Engineering, The Pennsylvania State University, University Park, United States
| | - Kyle W Gheres
- Center for Neural Engineering, The Pennsylvania State University, University Park, United States.,Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, United States
| | - Elizabeth A Proctor
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States.,Center for Neural Engineering, The Pennsylvania State University, University Park, United States.,Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, United States.,Department of Neurosurgery, Penn State College of Medicine, Hershey, United States.,Department of Pharmacology, Penn State College of Medicine, Hershey, United States
| | - Patrick J Drew
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States.,Center for Neural Engineering, The Pennsylvania State University, University Park, United States.,Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, United States.,Department of Neurosurgery, Penn State College of Medicine, Hershey, United States
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46
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Sobczak F, He Y, Sejnowski TJ, Yu X. Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics. Cereb Cortex 2020; 31:826-844. [PMID: 32940658 PMCID: PMC7906791 DOI: 10.1093/cercor/bhaa260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/19/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascular origin of the fMRI signal, it remains challenging to separate the neural correlates of global rs-fMRI signal fluctuations from other confounding sources. However, the slow-oscillation detected from individual vessels by single-vessel fMRI presents strong correlation to neural oscillations. Here, we use recurrent neural networks (RNNs) to predict the future temporal evolution of the rs-fMRI slow oscillation from both rodent and human brains. The RNNs trained with vessel-specific rs-fMRI signals encode the unique brain oscillatory dynamic feature, presenting more effective prediction than the conventional autoregressive model. This RNN-based predictive modeling of rs-fMRI datasets from the Human Connectome Project (HCP) reveals brain state-specific characteristics, demonstrating an inverse relationship between the global rs-fMRI signal fluctuation with the internal default-mode network (DMN) correlation. The RNN prediction method presents a unique data-driven encoding scheme to specify potential brain state differences based on the global fMRI signal fluctuation, but not solely dependent on the global variance.
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Affiliation(s)
- Filip Sobczak
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany
| | - Yi He
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Danish Research Centre for Magnetic Resonance, 2650, Hvidovre, Denmark
| | - Terrence J Sejnowski
- Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.,Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
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47
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Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proc Natl Acad Sci U S A 2020; 117:20890-20897. [PMID: 32817467 DOI: 10.1073/pnas.2003383117] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Multimodal evidence suggests that brain regions accumulate information over timescales that vary according to anatomical hierarchy. Thus, these experimentally defined "temporal receptive windows" are longest in cortical regions that are distant from sensory input. Interestingly, spontaneous activity in these regions also plays out over relatively slow timescales (i.e., exhibits slower temporal autocorrelation decay). These findings raise the possibility that hierarchical timescales represent an intrinsic organizing principle of brain function. Here, using resting-state functional MRI, we show that the timescale of ongoing dynamics follows hierarchical spatial gradients throughout human cerebral cortex. These intrinsic timescale gradients give rise to systematic frequency differences among large-scale cortical networks and predict individual-specific features of functional connectivity. Whole-brain coverage permitted us to further investigate the large-scale organization of subcortical dynamics. We show that cortical timescale gradients are topographically mirrored in striatum, thalamus, and cerebellum. Finally, timescales in the hippocampus followed a posterior-to-anterior gradient, corresponding to the longitudinal axis of increasing representational scale. Thus, hierarchical dynamics emerge as a global organizing principle of mammalian brains.
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48
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Gorgoni M, D’Atri A, Scarpelli S, Ferrara M, De Gennaro L. The electroencephalographic features of the sleep onset process and their experimental manipulation with sleep deprivation and transcranial electrical stimulation protocols. Neurosci Biobehav Rev 2020; 114:25-37. [PMID: 32343983 DOI: 10.1016/j.neubiorev.2020.04.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/28/2020] [Accepted: 04/05/2020] [Indexed: 02/08/2023]
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49
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Song C, Tagliazucchi E. Linking the nature and functions of sleep: insights from multimodal imaging of the sleeping brain. CURRENT OPINION IN PHYSIOLOGY 2020; 15:29-36. [PMID: 32715184 PMCID: PMC7374576 DOI: 10.1016/j.cophys.2019.11.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Sleep and wakefulness are traditionally considered as two mutually exclusive states with contrasting behavioural manifestations and complementary neurobiological functions. However, the discoveries of local sleep in global wakefulness and local wakefulness in global sleep have challenged this classical view and raised questions about the nature and functions of sleep. Here, we review the contributions from recent multimodal imaging studies of human sleep towards understanding the relationship between the nature and functions of sleep. Through simultaneous tracking of brain state and mapping of brain activity, these studies revealed that the sleeping brain can carry out covert cognitive processing that was thought to be wake-specific (wake-like function in the sleeping brain). Conversely, the awake brain can perform housekeeping functions through local sleep of neural populations (sleep-like function in the awake brain). We discuss how the blurred boundary between sleep and wakefulness highlights the need to radically rethink the definition of brain states, and how the recently discovered fMRI signatures of global and local sleep can help to address these outstanding questions.
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Affiliation(s)
- Chen Song
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Enzo Tagliazucchi
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
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50
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Chen JE, Lewis LD, Chang C, Tian Q, Fultz NE, Ohringer NA, Rosen BR, Polimeni JR. Resting-state "physiological networks". Neuroimage 2020; 213:116707. [PMID: 32145437 PMCID: PMC7165049 DOI: 10.1016/j.neuroimage.2020.116707] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 02/26/2020] [Accepted: 03/03/2020] [Indexed: 12/21/2022] Open
Abstract
Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such "physiological networks"-sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology-resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the "physiological networks" seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent "physiological connectivity" cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such "physiological" dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.
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Affiliation(s)
- Jingyuan E Chen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Nina E Fultz
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, USA
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