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Fasiello E, Gorgoni M, Galbiati A, Sforza M, Berra F, Scarpelli S, Alfonsi V, Annarumma L, Casoni F, Zucconi M, Castronovo V, Ferini-Strambi L, De Gennaro L. Decreased Delta/Beta ratio index as the sleep state-independent electrophysiological signature of sleep state misperception in Insomnia disorder: A focus on the sleep onset and the whole night. Neuroimage 2024; 298:120782. [PMID: 39128660 DOI: 10.1016/j.neuroimage.2024.120782] [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: 04/28/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 08/13/2024] Open
Abstract
PURPOSE Sleep State Misperception (SSM) is described as the tendency of Insomnia Disorder (ID) patients to overestimate Sleep Latency (SL) and underestimate Total Sleep Time (TST). Literature exploring topographical components in ID with SSM is scarce and does not allow us to fully understand the potential mechanisms underlying this phenomenon. This study aims to evaluate the existence of sleep EEG topography alterations in ID patients associated with SSM compared to Healthy Controls (HC), focusing on two distinct periods: the Sleep Onset (SO) and the whole night. METHODS Twenty ID patients (mean age: 43.5 ± 12.7; 7 M/13F) and 18 HCs (mean age: 41.6 ± 11.9; 8 M/10F) underwent a night of Polysomnography (PSG) and completed sleep diaries the following morning upon awakening. Two SSM indices, referring to the misperception of SL (SLm) and TST (TSTm), were calculated by comparing objective and subjective sleep indices extracted by PSG and sleep diary. According to these indices, the entire sample was split into 4 sub-groups: ID +SLm vs HC -SLm; ID +TSTm vs HC -TSTm. RESULTS Considering the SO, the two-way mixed-design ANOVA showed a significant main effect of Groups pointing to a decreased delta/beta ratio in the whole scalp topography. Moreover, we found a significant interaction effect for the sigma and beta bands. Post Hoc tests showed higher sigma and beta power in anterior and temporo-parietal sites during the SO period in IDs +SLm compared to HC -SLm. Considering the whole night, the unpaired t-test revealed in IDs +TSTm significantly lower delta power during NREM, and lower delta/beta ratio index during NREM and REM sleep compared to HCs -TSTm. Finally, we found diffuse significant negative correlations between SSM indices and the delta/beta ratio during SO, NREM, and REM sleep. CONCLUSION The main finding of the present study suggests that higher SL overestimation and TST underestimation are both phenomena related to diffuse cortical hyperarousal interpreted as a sleep state-independent electrophysiological correlate of the SSM, both during the SO and the whole night.
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Affiliation(s)
- Elisabetta Fasiello
- Faculty of Psychology, Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milan, Italy; Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy.
| | - Maurizio Gorgoni
- Department of Psychology, University of Rome Sapienza, Via dei Marsi, 78, 00185, Rome, Italy; Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Andrea Galbiati
- Faculty of Psychology, Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milan, Italy; Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Marco Sforza
- Faculty of Psychology, Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milan, Italy; Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Francesca Berra
- Faculty of Psychology, Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milan, Italy; Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Serena Scarpelli
- Department of Psychology, University of Rome Sapienza, Via dei Marsi, 78, 00185, Rome, Italy
| | - Valentina Alfonsi
- Department of Psychology, University of Rome Sapienza, Via dei Marsi, 78, 00185, Rome, Italy
| | - Ludovica Annarumma
- Department of Psychology, University of Rome Sapienza, Via dei Marsi, 78, 00185, Rome, Italy
| | - Francesca Casoni
- Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Marco Zucconi
- Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Vincenza Castronovo
- Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Luigi Ferini-Strambi
- Faculty of Psychology, Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milan, Italy; Department of Clinical Neurosciences, Neurology-Sleep Disorders Center, IRCCS San Raffaele Scientific Institute, Via Stamira d'Ancona, 20, 20127, Milan, Italy
| | - Luigi De Gennaro
- Department of Psychology, University of Rome Sapienza, Via dei Marsi, 78, 00185, Rome, Italy; Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
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Meyer-Baese L, Anumba N, Bolt T, Daley L, LaGrow TJ, Zhang X, Xu N, Pan WJ, Schumacher EH, Keilholz S. Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states. Front Syst Neurosci 2024; 18:1425491. [PMID: 39157289 PMCID: PMC11327057 DOI: 10.3389/fnsys.2024.1425491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/22/2024] [Indexed: 08/20/2024] Open
Abstract
A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.
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Affiliation(s)
- Lisa Meyer-Baese
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - T. Bolt
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - L. Daley
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - T. J. LaGrow
- Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Xiaodi Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Wen-Ju Pan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - E. H. Schumacher
- Psychology, Georgia Institute of Technology, Atlanta, GA, United States
| | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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3
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Onoda K, Akama H. Exploring complex and integrated information during sleep. Neurosci Conscious 2024; 2024:niae029. [PMID: 38974800 PMCID: PMC11227102 DOI: 10.1093/nc/niae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/11/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
The Integrated Information Theory is a theoretical framework that aims to elucidate the nature of consciousness by postulating that it emerges from the integration of information within a system, and that the degree of consciousness depends on the extent of information integration within the system. When consciousness is lost, the core complex of consciousness proposed by the Integrated Information Theory disintegrates, and Φ measures, which reflect the level of integrated information, are expected to diminish. This study examined the predictions of the Integrated Information Theory using the global brain network acquired via functional magnetic resonance imaging during various tasks and sleep. We discovered that the complex located within the frontoparietal network remained constant regardless of task content, while the regional distribution of the complex collapsed in the initial stages of sleep. Furthermore, Φ measures decreased as sleep progressed under limited analysis conditions. These findings align with predictions made by the Integrated Information Theory and support its postulates.
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Affiliation(s)
- Keiichi Onoda
- Department of Psychology, Otemon Gakuin University, 2-1-15, Nishiai, Ibaraki, Osaka 567-8502, Japan
| | - Hiroyuki Akama
- Department of Life Science and Technology, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8550, Japan
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4
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Salvalaggio A, Pini L, Bertoldo A, Corbetta M. Glioblastoma and brain connectivity: the need for a paradigm shift. Lancet Neurol 2024; 23:740-748. [PMID: 38876751 DOI: 10.1016/s1474-4422(24)00160-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/29/2024] [Accepted: 04/03/2024] [Indexed: 06/16/2024]
Abstract
Despite substantial advances in cancer treatment, for patients with glioblastoma prognosis remains bleak. The emerging field of cancer neuroscience reveals intricate functional interplays between glioblastoma and the cellular architecture of the brain, encompassing neurons, glia, and vessels. New findings underscore the role of structural and functional connections within hierarchical networks, known as the connectome. These connections contribute to the location, spread, and recurrence of a glioblastoma, and a patient's overall survival, revealing a complex interplay between the tumour and the CNS. This mounting evidence prompts a paradigm shift, challenging the perception of glioblastomas as mere foreign bodies within the brain. Instead, these tumours are intricately woven into the structural and functional fabric of the brain. This radical change in thinking holds profound implications for the understanding and treatment of glioblastomas, which could unveil new prognostic factors and surgical strategies and optimise radiotherapy. Additionally, a connectivity approach suggests that non-invasive brain stimulation could disrupt pathological neuron-glioma interactions within specific networks.
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Affiliation(s)
- Alessandro Salvalaggio
- Clinica Neurologica, Azienda Ospedale Università Padova, Padova, Italy; Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Lorenzo Pini
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy; Department of Information Engineering, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Clinica Neurologica, Azienda Ospedale Università Padova, Padova, Italy; Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy; Veneto Institute of Molecular Medicine, Fondazione Biomedica, Padova, Italy.
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5
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Meyer-Baese L, Anumba N, Bolt T, Daley L, LaGrow TJ, Zhang X, Xu N, Pan WJ, Schumacher E, Keilholz S. Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591295. [PMID: 38746246 PMCID: PMC11092498 DOI: 10.1101/2024.04.26.591295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.
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Affiliation(s)
- Lisa Meyer-Baese
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - T Bolt
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - L Daley
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - T J LaGrow
- Electrical and Computer Engineering, Georgia Institute of Technology
| | - Xiaodi Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Wen-Ju Pan
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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6
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Rajeswari J, Jagannath M. Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals. Sci Rep 2024; 14:5561. [PMID: 38448538 PMCID: PMC10917737 DOI: 10.1038/s41598-024-56384-9] [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: 12/21/2023] [Accepted: 03/05/2024] [Indexed: 03/08/2024] Open
Abstract
Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics-University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics-University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.
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Affiliation(s)
- J Rajeswari
- Department of Electronics and Communication Engineering, Agni College of Technology, Chennai, Tamil Nadu, India
| | - M Jagannath
- School of Electronics Engineering, Vellore Institute of Technology (VIT) Chennai, Chennai, Tamil Nadu, India.
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7
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Waters F, Ling I, Azimi S, Blom JD. Sleep-Related Hallucinations. Sleep Med Clin 2024; 19:143-157. [PMID: 38368061 DOI: 10.1016/j.jsmc.2023.10.008] [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] [Indexed: 02/19/2024]
Abstract
The diagnostic category of sleep-related hallucinations (SRH) replaces the previous category of Terrifying Hypnagogic Hallucinations in the 2001 edition of International Classification of Sleep Disorders-R. Hypnagogic and hypnopompic hallucinations (HHH) that occur in the absence of other symptoms or disorder and, within the limits of normal sleep, are most likely non-pathological. By contrast, complex nocturnal visual hallucinations (CNVH) may reflect a dimension of psychopathology reflecting different combinations of etiologic influences. The identification and conceptualization of CNVH is relatively new, and more research is needed to clarify whether CNVH share common mechanisms with HHH.
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Affiliation(s)
- Flavie Waters
- Clinical Research Centre, Graylands Hospital, North Metropolitan Health Service Mental Health, Brockway Road, John XXIII Avenue, Mount Claremont, Perth, Western Australia 6009, Australia; School of Psychological Science, The University of Western Australia, Crawley, Western Australia, Australia.
| | - Ivan Ling
- West Australian Sleep Disorders Research Institute, Perth, Australia; Department of Pulmonary Physiology & Sleep Medicine, Sir Charles Gairdner Hospital, 5th Floor, G-block, Nedlands, Western Australia 6009, Australia
| | - Somayyeh Azimi
- Clinical Research Centre, Graylands Hospital, North Metropolitan Health Service Mental Health, Brockway Road, John XXIII Avenue, Mount Claremont, Perth, Western Australia 6009, Australia; School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jan Dirk Blom
- Parnassia Psychiatric Institute, Kiwistraat 43, The Hague 2552 DH, the Netherlands; Faculty of Social and Behavioural Sciences, Leiden University, Leiden, the Netherlands; Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands
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8
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Diezig S, Denzer S, Achermann P, Mast FW, Koenig T. EEG Microstate Dynamics Associated with Dream-Like Experiences During the Transition to Sleep. Brain Topogr 2024; 37:343-355. [PMID: 36402917 PMCID: PMC10884123 DOI: 10.1007/s10548-022-00923-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/21/2022] [Indexed: 11/21/2022]
Abstract
Consciousness always requires some representational content; that is, one can only be conscious about something. However, the presence of conscious experience (awareness) alone does not determine whether its content is in line with the external and physical world. Dreams, apart from certain forms of hallucinations, typically consist of non-veridical percepts, which are not recognized as false, but rather considered real. This type of experiences have been described as a state of dissociation between phenomenal and reflective awareness. Interestingly, during the transition to sleep, reflective awareness seems to break down before phenomenal awareness as conscious experience does not immediately fade with reduced wakefulness but is rather characterized by the occurrence of uncontrolled thinking and perceptual images, together with a reduced ability to recognize the internal origin of the experience. Relative deactivation of the frontoparietal and preserved activity in parieto-occipital networks has been suggested to account for dream-like experiences during the transition to sleep. We tested this hypothesis by investigating subjective reports of conscious experience and large-scale brain networks using EEG microstates in 45 healthy young subjects during the transition to sleep. We observed an inverse relationship between cognitive effects and physiological activation; dream-like experiences were associated with an increased presence of a microstate with sources in the superior and middle frontal gyrus and precuneus. Additionally, the presence of a microstate associated with higher-order visual areas was decreased. The observed inverse relationship might therefore indicate a disengagement of cognitive control systems that is mediated by specific, inhibitory EEG microstates.
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Affiliation(s)
- Sarah Diezig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Simone Denzer
- Department of Psychology, University of Bern, Bern, Switzerland
| | - Peter Achermann
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Fred W Mast
- Department of Psychology, University of Bern, Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
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Han L, Chan MY, Agres PF, Winter-Nelson E, Zhang Z, Wig GS. Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization. Cereb Cortex 2024; 34:bhad506. [PMID: 38385891 PMCID: PMC10883417 DOI: 10.1093/cercor/bhad506] [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: 02/06/2023] [Revised: 12/05/2023] [Accepted: 12/16/2023] [Indexed: 02/23/2024] Open
Abstract
Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
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10
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Miletínová E, Piorecký M, Koudelka V, Jiříček S, Tomeček D, Brunovský M, Horáček J, Bušková J. Alterations of sleep initiation in NREM parasomnia after sleep deprivation - A multimodal pilot study. Sleep Med X 2023; 6:100086. [PMID: 37745863 PMCID: PMC10511487 DOI: 10.1016/j.sleepx.2023.100086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/24/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023] Open
Abstract
Objectives NREM parasomnias also known as disorders of arousal (DOA) are characterised by abnormal motor and autonomic activation during arousals primarily from slow wave sleep. Dissociative state between sleep and wake is likely responsible for clinical symptoms of DOA. We therefore investigated potential dissociation outside of parasomnic events by using simultaneous 256-channel EEG (hdEEG) and functional magnetic resonance imaging (fMRI). Methods Eight DOA patients (3 women, mean age = 27.8; SD = 4.2) and 8 gender and age matched healthy volunteers (3 women, mean age = 26,5; SD = 4.0) were included into the study. They underwent 30-32 h of sleep deprivation followed by hdEEG and fMRI recording. We determined 2 conditions: falling asleep (FA) and arousal (A), that occurred outside of deep sleep and/or parasomnic event. We used multimodal approach using data obtained from EEG, fMRI and EEG-fMRI integration approach. Results DOA patients showed increase in delta and beta activity over postcentral gyrus and cuneus during awakening period. This group expressed increased connectivity between motor cortex and cingulate during arousals unrelated to parasomnic events in the beta frequency band. They also showed lower connectivity between different portions of cingulum. In contrast, the greater connectivity was found between thalamus and some cortical areas, such as occipital cortex. Conclusion Our findings suggest a complex alteration in falling asleep and arousal mechanisms at both subcortical and cortical levels in response to sleep deprivation. As this alteration is present also outside of slow wave sleep and/or parasomnic episodes we believe this could be a trait factor of DOA.
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Affiliation(s)
- E. Miletínová
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
| | - M. Piorecký
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Department of Biomedical Technology, Faculty of Biomedical Engineering, CTU in Prague, Czech Republic
| | - V. Koudelka
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Department of Biomedical Technology, Faculty of Biomedical Engineering, CTU in Prague, Czech Republic
| | - S. Jiříček
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - D. Tomeček
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - M. Brunovský
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
| | - J. Horáček
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
| | - J. Bušková
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
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11
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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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12
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Fan JM, Kudo K, Verma P, Ranasinghe KG, Morise H, Findlay AM, Vossel K, Kirsch HE, Raj A, Krystal AD, Nagarajan SS. Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep. J Neurosci 2023; 43:8157-8171. [PMID: 37788939 PMCID: PMC10697405 DOI: 10.1523/jneurosci.0197-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 07/07/2023] [Accepted: 08/06/2023] [Indexed: 10/05/2023] Open
Abstract
Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.
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Affiliation(s)
- Joline M Fan
- Department of Neurology, University of California-San Francisco, San Francisco, California 94143
| | - Kiwamu Kudo
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan 243-0460
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
| | - Kamalini G Ranasinghe
- Department of Neurology, University of California-San Francisco, San Francisco, California 94143
| | - Hirofumi Morise
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan 243-0460
| | - Anne M Findlay
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
| | - Keith Vossel
- Department of Neurology, University of California-San Francisco, San Francisco, California 94143
- Mary S. Easton Center for Alzheimer's Disease Research, Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California 90095
| | - Heidi E Kirsch
- Department of Neurology, University of California-San Francisco, San Francisco, California 94143
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
| | - Andrew D Krystal
- Department of Psychiatry, University of California-San Francisco, San Francisco, California 94143
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
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13
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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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14
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Sodré ME, Wießner I, Irfan M, Schenck CH, Mota-Rolim SA. Awake or Sleeping? Maybe Both… A Review of Sleep-Related Dissociative States. J Clin Med 2023; 12:3876. [PMID: 37373570 DOI: 10.3390/jcm12123876] [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: 03/22/2023] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 06/29/2023] Open
Abstract
Recent studies have begun to understand sleep not only as a whole-brain process but also as a complex local phenomenon controlled by specific neurotransmitters that act in different neural networks, which is called "local sleep". Moreover, the basic states of human consciousness-wakefulness, sleep onset (N1), light sleep (N2), deep sleep (N3), and rapid eye movement (REM) sleep-can concurrently appear, which may result in different sleep-related dissociative states. In this article, we classify these sleep-related dissociative states into physiological, pathological, and altered states of consciousness. Physiological states are daydreaming, lucid dreaming, and false awakenings. Pathological states include sleep paralysis, sleepwalking, and REM sleep behavior disorder. Altered states are hypnosis, anesthesia, and psychedelics. We review the neurophysiology and phenomenology of these sleep-related dissociative states of consciousness and update them with recent studies. We conclude that these sleep-related dissociative states have a significant basic and clinical impact since their study contributes to the understanding of consciousness and the proper treatment of neuropsychiatric diseases.
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Affiliation(s)
| | - Isabel Wießner
- Brain Institute, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
| | - Muna Irfan
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Carlos H Schenck
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sergio A Mota-Rolim
- Brain Institute, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
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15
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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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16
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Ma N, Ning Q, Li M, Hao C. The First-Night Effect on the Instability of Stage N2: Evidence from the Activity of the Central and Autonomic Nervous Systems. Brain Sci 2023; 13:brainsci13040667. [PMID: 37190632 DOI: 10.3390/brainsci13040667] [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: 02/24/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
A series of studies have suggested that stage N2 is vulnerable and strongly affected by the first-night effect (FNE). However, the neurophysiological mechanism underlying the vulnerability of stage N2 of the FNE has not been well examined. A total of 17 healthy adults (11 women and 6 men, mean age: 21.59 ± 2.12) underwent two nights of polysomnogram recordings in the sleep laboratory. We analyzed sleep structure and central and autonomic nervous system activity during stage N2 and applied the electroencephalographic (EEG) activation index (beta/delta power ratio) and heart rate variability to reflect changes in central and autonomic nervous system activity caused by the FNE. Correlation analyses were performed between EEG activation and heart rate variability. The results showed that EEG activation and high-frequency heart rate variability increased on the adaptation night (Night 1). Importantly, EEG activation was significantly associated with the percentage of stage N1, and the correlation between EEG activation and high-frequency heart rate variability decreased due to the FNE. These findings indicate that the FNE affects the instability of stage N2 by increasing central nervous system activity and uncoupling the activity between the central and autonomic nervous systems.
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Affiliation(s)
- Ning Ma
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou 510631, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qian Ning
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou 510631, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Mingzhu Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou 510631, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Chao Hao
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou 510631, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health & Cognitive Science, School of Psychology, South China Normal University, Guangzhou 510631, China
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17
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Wang R, Bashyam V, Yang Z, Yu F, Tassopoulou V, Chintapalli SS, Skampardoni I, Sreepada LP, Sahoo D, Nikita K, Abdulkadir A, Wen J, Davatzikos C. Applications of generative adversarial networks in neuroimaging and clinical neuroscience. Neuroimage 2023; 269:119898. [PMID: 36702211 PMCID: PMC9992336 DOI: 10.1016/j.neuroimage.2023.119898] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/16/2022] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.
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Affiliation(s)
- Rongguang Wang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
| | - Vishnu Bashyam
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Fanyang Yu
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Vasiliki Tassopoulou
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Sai Spandana Chintapalli
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Lasya P Sreepada
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Dushyant Sahoo
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Ahmed Abdulkadir
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
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18
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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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19
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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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20
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Setzer B, Fultz NE, Gomez DEP, Williams SD, Bonmassar G, Polimeni JR, Lewis LD. A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state. Nat Commun 2022; 13:5442. [PMID: 36114170 PMCID: PMC9481532 DOI: 10.1038/s41467-022-33010-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/29/2022] [Indexed: 11/30/2022] Open
Abstract
Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.
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Affiliation(s)
- Beverly Setzer
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Nina E Fultz
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Daniel E P Gomez
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Giorgio Bonmassar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
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21
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Trambaiolli LR, Cassani R, Biazoli CE, Cravo AM, Sato JR, Falk TH. Multimodal resting-state connectivity predicts affective neurofeedback performance. Front Hum Neurosci 2022; 16:977776. [PMID: 36158618 PMCID: PMC9493361 DOI: 10.3389/fnhum.2022.977776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques. The fNIRS oxyhemoglobin and deoxyhemoglobin concentrations and the EEG beta and gamma bands modulated by the alpha frequency band (beta-m-alpha and gamma-m-alpha, respectively) recorded over the frontal cortex of healthy subjects were used to estimate functional connectivity from each neuroimaging modality. For each connectivity matrix, relevant edges were selected in a leave-one-subject-out procedure, summed into "connectivity summary scores" (CSS), and submitted as inputs to a support vector regressor (SVR). Then, the performance of the left-out-subject was predicted using the trained SVR model. Linear relationships between the CSS across both modalities were evaluated using Pearson's correlation. The predictive model showed a mean absolute error smaller than 20%, and the fNIRS oxyhemoglobin CSS was significantly correlated with the EEG gamma-m-alpha CSS (r = -0.456, p = 0.030). These results support that pre-task electrophysiological and hemodynamic resting-state connectivity are potential predictors of neurofeedback performance and are meaningfully coupled. This investigation motivates the use of joint EEG-fNIRS connectivity as outcome predictors, as well as a tool for functional connectivity coupling investigation.
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Affiliation(s)
- Lucas R. Trambaiolli
- Basic Neuroscience Division, McLean Hospital–Harvard Medical School, Belmont, MA, United States
| | - Raymundo Cassani
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Claudinei E. Biazoli
- Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom
| | - André M. Cravo
- Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - João R. Sato
- Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
- Big Data, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada
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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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Zou G, Liu J, Zou Q, Gao JH. A-PASS: An automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. J Neural Eng 2022; 19. [PMID: 35878599 DOI: 10.1088/1741-2552/ac83f2] [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: 03/12/2022] [Accepted: 07/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. APPROACH A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods. MAIN RESULTS The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs. SIGNIFICANCE A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.
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Affiliation(s)
- Guangyuan Zou
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Jiayi Liu
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Qihong Zou
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Jia-Hong Gao
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
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25
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Unconscious mind activates central cardiovascular network and promotes adaptation to microgravity possibly anti-aging during 1-year-long spaceflight. Sci Rep 2022; 12:11862. [PMID: 35831420 PMCID: PMC9279338 DOI: 10.1038/s41598-022-14858-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 06/14/2022] [Indexed: 12/12/2022] Open
Abstract
The intrinsic cardiovascular regulatory system (β, 0.00013–0.02 Hz) did not adapt to microgravity after a 6-month spaceflight. The infraslow oscillation (ISO, 0.01–0.10 Hz) coordinating brain dynamics via thalamic astrocytes plays a key role in the adaptation to novel environments. We investigate the adaptive process of a healthy astronaut during a 12-month-long spaceflight by analyzing heart rate variability (HRV) in the LF (0.01–0.05 Hz) and MF1 (0.05–0.10 Hz) bands for two consecutive days on four occasions: before launch, at 1-month (ISS01) and 11-month (ISS02) in space, and after return to Earth. Alteration of β during ISS01 improved during ISS02 (P = 0.0167). During ISS01, LF and MF1 bands, reflecting default mode network (DMN) activity, started to increase at night (by 43.1% and 32.0%, respectively), when suprachiasmatic astrocytes are most active, followed by a 25.9% increase in MF1-band throughout the entire day during ISS02, larger at night (47.4%) than during daytime. Magnetic declination correlated positively with β during ISS01 (r = 0.6706, P < 0.0001) and ISS02 (r = 0.3958, P = 0.0095). Magnetic fluctuations may affect suprachiasmatic astrocytes, and the DMN involving ISOs and thalamic astrocytes may then be activated, first at night, then during the entire day, a mechanism that could perhaps promote an anti-aging effect noted in other investigations.
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26
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Gao W, Huang Z, Ou W, Tang X, Lv W, Nie J. Functional individual variability development of the neonatal brain. Brain Struct Funct 2022; 227:2181-2190. [PMID: 35668328 DOI: 10.1007/s00429-022-02516-8] [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: 10/10/2021] [Accepted: 05/22/2022] [Indexed: 11/28/2022]
Abstract
Individual variability in cognition and behavior results from the differences in brain structure and function that have already emerged before birth. However, little is known about individual variability in brain functional architecture at local level in neonates which is of great significance to explore owing to largely undeveloped long-range functional connectivity and segregated functions in early brain development. To address this, resting-state fMRI data of 163 neonates ranged from 32 to 45 postconceptional weeks (PCW) were used in this study, and various functional features including functional parcellation similarity, local brain activity and local functional connectivity were used to characterize individual functional variability. We observed significantly higher local functional individual variability in superior parietal, sensorimotor, and visual cortex, and lower variability in the frontal, insula and cingulate cortex relative to other regions within each hemisphere. The mean local functional individual variability significantly increased with age, and the age effect was found larger in brain regions such as the occipital, temporal, prefrontal and parietal cortex. Our findings promote the understanding of brain plasticity and regional differential maturation in the early stage.
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Affiliation(s)
- Wenjian Gao
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education (South China Normal University), Guangzhou, China
| | - Ziyi Huang
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education (South China Normal University), Guangzhou, China
| | - Wenfei Ou
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education (South China Normal University), Guangzhou, China
| | - Xiaoqian Tang
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education (South China Normal University), Guangzhou, China
| | - Wanying Lv
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education (South China Normal University), Guangzhou, China
| | - Jingxin Nie
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China. .,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education (South China Normal University), Guangzhou, China.
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27
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Huang C, Zhou Y, Zhong Y, Wang X, Zhang Y. The Bilateral Precuneus as a Potential Neuroimaging Biomarker for Right Temporal Lobe Epilepsy: A Support Vector Machine Analysis. Front Psychiatry 2022; 13:923583. [PMID: 35782449 PMCID: PMC9240203 DOI: 10.3389/fpsyt.2022.923583] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background and Objective While evidence has demonstrated that the default-mode network (DMN) plays a key role in the broad-scale cognitive problems that occur in right temporal lobe epilepsy (rTLE), little is known about alterations in the network homogeneity (NH) of the DMN in TLE. In this study, we used the NH method to investigate the NH of the DMN in TLE at rest, and an support vector machine (SVM) method for the diagnosis of rTLE. Methods A total of 43 rTLE cases and 42 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Imaging data were analyzed with the NH and SVM methods. Results rTLE patients have a decreased NH in the right inferior temporal gyrus (ITG) and left middle temporal gyrus (MTG), but increased NH in the bilateral precuneus (PCu) and right inferior parietal lobe (IPL), compared with HCs. We found that rTLE had a longer performance reaction time (RT). No significant correlation was found between abnormal NH values and clinical variables of the patients. The SVM results showed that increased NH in the bilateral PCu as a diagnostic biomarker distinguished rTLE from HCs with an accuracy of 74.12% (63/85), a sensitivity 72.01% (31/43), and a specificity 72.81% (31/42). Conclusion These findings suggest that abnormal NH of the DMN exists in rTLE, and highlights the significance of the DMN in the pathophysiology of cognitive problems occurring in rTLE, and the bilateral PCu as a neuroimaging diagnostic biomarker for rTLE.
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Affiliation(s)
- Chunyan Huang
- Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Yang Zhou
- Wuhan Mental Health Center, Wuhan, China
- Wuhan Hospital for Psychotherapy, Wuhan, China
| | - Yi Zhong
- NHC Key Laboratory of Mental Health (Peking University), Peking University Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
| | - Xi Wang
- Department of Sleep and Psychosomatic Medicine Center, Taihe Hospital, Affiliated Hospital of Hubei University of Medicine, Shiyan, China
| | - Yunhua Zhang
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Clinical Medical College of Hubei University of Chinese Medicine, Wuhan, China
- Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China
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28
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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: 1] [Impact Index Per Article: 0.5] [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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29
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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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30
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Cao M, Wu Z, Li X. GAT-FD: An integrated MATLAB toolbox for graph theoretical analysis of task-related functional dynamics. PLoS One 2022; 17:e0267456. [PMID: 35446912 PMCID: PMC9022818 DOI: 10.1371/journal.pone.0267456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 04/08/2022] [Indexed: 11/30/2022] Open
Abstract
Functional connectivity has been demonstrated to be varying over time during sensory and cognitive processes. Quantitative examinations of such variations can significantly advance our understanding on large-scale functional organizations and their topological dynamics that support normal brain functional connectome and can be altered in individuals with brain disorders. However, toolboxes that integrate the complete functions for analyzing task-related brain functional connectivity, functional network topological properties, and their dynamics, are still lacking. The current study has developed a MATLAB toolbox, the Graph Theoretical Analysis of Task-Related Functional Dynamics (GAT-FD), which consists of four modules for sliding-window analyses, temporal mask generation, estimations of network properties and dynamics, and result display, respectively. All the involved functions have been tested and validated using functional magnetic resonance imaging data collected from human subjects when performing a block-designed task. The results demonstrated that the GAT-FD allows for effective and quantitative evaluations of the functional network properties and their dynamics during the task period. As an open-source and user-friendly package, the GAT-FD and its detailed user manual are freely available at https://www.nitrc.org/projects/gat_fd and https://centers.njit.edu/cnnl/gat_fd/.
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Affiliation(s)
- Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Ziyan Wu
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America
- * E-mail: ,
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31
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A comprehensive investigation of physiologic noise modeling in resting state fMRI; time shifted cardiac noise in EPI and its removal without external physiologic signal measures. Neuroimage 2022; 254:119136. [PMID: 35346840 DOI: 10.1016/j.neuroimage.2022.119136] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/18/2022] [Accepted: 03/22/2022] [Indexed: 11/23/2022] Open
Abstract
Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR.
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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] [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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Weinhouse GL, Kimchi E, Watson P, Devlin JW. Sleep Assessment in Critically Ill Adults: Established Methods and Emerging Strategies. Crit Care Explor 2022; 4:e0628. [PMID: 35156048 PMCID: PMC8824402 DOI: 10.1097/cce.0000000000000628] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Sleep is a biological mandate with an integral role in optimizing functions that maintain psychological and physical health. During critical illness, however, sleep may be disrupted at best and elusive at worst. Sleep improvement efforts and research endeavors evaluating interventions to improve sleep in critically ill adults are hampered by limited methods available to measure sleep in this setting. This narrative review summarizes available modalities for sleep assessment in the ICU, describes new ICU sleep assessment methods under development, and highlights features of the ideal ICU sleep measurement tool. DATA SOURCES The most relevant literature and author experiences were assessed for inclusion from PubMed and textbooks. STUDY SELECTION The authors selected studies for inclusion by consensus. DATA EXTRACTION The authors reviewed each study and selected appropriate data for inclusion by consensus. DATA SYNTHESIS Currently available tools to measure sleep in critically ill adults have important flaws. Subjective measurements are limited by recall bias, the inability of many patients to communicate, and poorly correlate with objective measures when completed by surrogates. Actigraphy does not consider the effects of sedating medications or myopathy leading to an over estimation of sleep time. Polysomnography, the gold standard for sleep assessment, is limited by interpretation issues and practical application concerns. Single and multiple channel electroencephalogram devices offer real-time physiologic data and are more practical to use than polysomnography but are limited by the scope of sleep-specific information they can measure and poorly characterize the circadian system. CONCLUSIONS A measurement tool that offers real-time sleep and circadian assessment and is practical for broad application in the ICU does not exist. Newer sleep assessment devices have shown promise in measuring physiologic data in real time; when used in combination with other assessment modalities, and analyzed by computational techniques, they may revolutionize sleep monitoring in the ICU.
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Affiliation(s)
- Gerald L Weinhouse
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA
| | - Eyal Kimchi
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Paula Watson
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - John W Devlin
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA
- Department of Pharmacy and Health Systems Sciences, Bouve College of Health Sciences, Northeastern University, Boston, MA
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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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Singh K, Cauzzo S, García-Gomar MG, Stauder M, Vanello N, Passino C, Bianciardi M. Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI. Neuroimage 2022; 249:118865. [PMID: 35031472 DOI: 10.1016/j.neuroimage.2021.118865] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/30/2021] [Accepted: 12/27/2021] [Indexed: 01/21/2023] Open
Abstract
Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor function in living humans in health and disease.
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Affiliation(s)
- 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.
| | - 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; Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
| | - 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
| | - 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
| | - Nicola Vanello
- Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy
| | - Claudio Passino
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy; Fondazione Toscana Gabriele Monasterio, Pisa, 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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Ai Z, He H, Wang T, Chen L, Huang C, Chen C, Xu P, Zhu G, Yang M, Song Y, Su D. Validation of the Thyrotoxicosis-associated Insomnia Model Induced by Thyroxine through Sympathetic Stimulation: Face, Construct and Predictive Perspectives. Exp Neurobiol 2021; 30:387-400. [PMID: 34983880 PMCID: PMC8752319 DOI: 10.5607/en21023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 11/10/2021] [Accepted: 11/17/2021] [Indexed: 11/25/2022] Open
Abstract
Insomnia has become a common central nervous system disease. At present, the pathogenesis of insomnia is not clear. Animal models can help us understand the pathogenesis of the disease and can be used in transformational medicine. Therefore, it is very necessary to establish an appropriate model of insomnia. Clinical data show that insomnia patients with high levels of thyroxine and often accompanied by cardiovascular problems, a common mechanism underlying all of these physiological disruptions is the sympathetic nervous system. Combined with the characteristics of chronic onset of clinical insomnia, an insomnia model induced by long-term intraperitoneal injection of thyroid hormone has been created in our laboratory. In this paper, the insomnia-like state of the model was evaluated based on three validity criteria. Face validity has been demonstrated in metabolism, the Morris water maze, electrocardiogram (ECG) and electroencephalogram (EEG). Structure validity has been proved by the results of targeted metabolomics. After treatment with diazepam, a commonly used clinical anti-insomnia drug, the above physiological and pathological disorders were reversed. The results of comprehensive analysis show that the established thyrotoxicosis-associated insomnia model meets the validity requirement to establish an appropriate animal model of insomnia. The model presented in this article might help to study pathogenetic mechanisms of clinical insomnia, as well as to test promising methods of insomnia treatment.
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Affiliation(s)
- Zhifu Ai
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Hongwei He
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Tingting Wang
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Liling Chen
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Chunhua Huang
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Changlian Chen
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Pengfei Xu
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Genhua Zhu
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Ming Yang
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese.,Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, State Key Lab of Innovation Drug and Efficient Energy-Saving Pharmaceutical Equipment, Jiangxi University o
| | - Yonggui Song
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
| | - Dan Su
- Key Laboratory of depression animal model based on TCM syndrome, Jiangxi Administration of traditional Chinese Medicine, Jiangxi Key Laboratory of TCM for prevention and treatment of brain diseases with cognitive impairment, Jiangxi University of Chinese
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Korom M, Camacho MC, Filippi CA, Licandro R, Moore LA, Dufford A, Zöllei L, Graham AM, Spann M, Howell B, Shultz S, Scheinost D. Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies. Dev Cogn Neurosci 2021; 53:101055. [PMID: 34974250 PMCID: PMC8733260 DOI: 10.1016/j.dcn.2021.101055] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/28/2021] [Accepted: 12/26/2021] [Indexed: 01/07/2023] Open
Abstract
The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT’NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging. The field of infant MRI is young with evolving standards. We address 20 questions that researchers commonly receive reviewers. These come from research ethics boards, grant, and manuscript reviewers. This article reflects the cumulative knowledge of experts in the FIT’NG community.
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Affiliation(s)
- Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA.
| | - M Catalina Camacho
- Division of Biology and Biomedical Sciences (Neurosciences), Washington University School of Medicine, St. Louis, MO, USA.
| | - Courtney A Filippi
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Roxane Licandro
- Institute of Visual Computing and Human-Centered Technology, Computer Vision Lab, TU Wien, Vienna, Austria; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research, Medical University of Vienna, Vienna, Austria
| | - Lucille A Moore
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Alexander Dufford
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Marisa Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Department of Human Development and Family Science, Virginia Polytechnic Institute and State University, Roanoke, VA, USA
| | | | - Sarah Shultz
- Division of Autism & Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, GA, USA.
| | - Dustin Scheinost
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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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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Daniel Arzate-Mena J, Abela E, Olguín-Rodríguez PV, Ríos-Herrera W, Alcauter S, Schindler K, Wiest R, Müller MF, Rummel C. Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales. Neuroimage 2021; 246:118763. [PMID: 34863961 DOI: 10.1016/j.neuroimage.2021.118763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022] Open
Abstract
Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.
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Affiliation(s)
- J Daniel Arzate-Mena
- Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos,Cuernavaca Morelos, Mexico
| | - Eugenio Abela
- Center for Neuropsychiatrics, Psychiatric Services Aargau AG, Windisch, Switzerland
| | | | - Wady Ríos-Herrera
- Facultad de Psicología Universidad Nacional Autónoma de México, Mexico City, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus F Müller
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Morelos, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico; Centro Internacional de Ciencias A. C., Cuernavaca, México
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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Hong CCH, Fallon JH, Friston KJ. fMRI Evidence for Default Mode Network Deactivation Associated with Rapid Eye Movements in Sleep. Brain Sci 2021; 11:brainsci11111528. [PMID: 34827529 PMCID: PMC8615877 DOI: 10.3390/brainsci11111528] [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: 09/30/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 11/25/2022] Open
Abstract
System-specific brain responses—time-locked to rapid eye movements (REMs) in sleep—are characteristically widespread, with robust and clear activation in the primary visual cortex and other structures involved in multisensory integration. This pattern suggests that REMs underwrite hierarchical processing of visual information in a time-locked manner, where REMs index the generation and scanning of virtual-world models, through multisensory integration in dreaming—as in awake states. Default mode network (DMN) activity increases during rest and reduces during various tasks including visual perception. The implicit anticorrelation between the DMN and task-positive network (TPN)—that persists in REM sleep—prompted us to focus on DMN responses to temporally-precise REM events. We timed REMs during sleep from the video recordings and quantified the neural correlates of REMs—using functional MRI (fMRI)—in 24 independent studies of 11 healthy participants. A reanalysis of these data revealed that the cortical areas exempt from widespread REM-locked brain activation were restricted to the DMN. Furthermore, our analysis revealed a modest temporally-precise REM-locked decrease—phasic deactivation—in key DMN nodes, in a subset of independent studies. These results are consistent with hierarchical predictive coding; namely, permissive deactivation of DMN at the top of the hierarchy (leading to the widespread cortical activation at lower levels; especially the primary visual cortex). Additional findings indicate REM-locked cerebral vasodilation and suggest putative mechanisms for dream forgetting.
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Affiliation(s)
- Charles Chong-Hwa Hong
- Patuxent Institution, Correctional Mental Health Center—Jessup, Jessup, MD 20794, USA
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore, MD 21205, USA
- Correspondence: ; Tel.: +1-410-596-1956
| | - James H. Fallon
- Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA;
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92697, USA
| | - Karl J. Friston
- The Well Come Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK;
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Abstract
The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.
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Affiliation(s)
- Zinong Yang
- Graduate Program in Neuroscience, Boston University, Boston MA, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston MA, United States.,Center for Systems Neuroscience, Boston University, Boston MA, United States
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42
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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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43
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Zhang S, Spoletini LJ, Gold BP, Morgan VL, Rogers BP, Chang C. Interindividual Signatures of fMRI Temporal Fluctuations. Cereb Cortex 2021; 31:4450-4463. [PMID: 33903915 PMCID: PMC8408464 DOI: 10.1093/cercor/bhab099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Liam J Spoletini
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Benjamin P Gold
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Baxter P Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
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44
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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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45
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Scrivener CL. When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI. Front Neurosci 2021; 15:636424. [PMID: 34267620 PMCID: PMC8276697 DOI: 10.3389/fnins.2021.636424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/01/2021] [Indexed: 11/19/2022] Open
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.
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Affiliation(s)
- Catriona L. Scrivener
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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46
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Uji M, Cross N, Pomares FB, Perrault AA, Jegou A, Nguyen A, Aydin U, Lina JM, Dang-Vu TT, Grova C. Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings. Hum Brain Mapp 2021; 42:3993-4021. [PMID: 34101939 PMCID: PMC8288107 DOI: 10.1002/hbm.25535] [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: 01/19/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 11/21/2022] Open
Abstract
Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.
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Affiliation(s)
- Makoto Uji
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada
| | - Nathan Cross
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Florence B Pomares
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Aurore A Perrault
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Aude Jegou
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.,Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Alex Nguyen
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Umit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.,Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Jean-Marc Lina
- Departement de Genie Electrique, Ecole de Technologie Superieure, Montreal, Quebec, Canada.,Centre de Recherches Mathematiques, Montréal, Québec, Canada
| | - Thien Thanh Dang-Vu
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.,Centre de Recherches Mathematiques, Montréal, Québec, Canada.,Multimodal Functional Imaging Lab, Biomedical Engineering Department, Neurology and Neurosurgery Department, McGill University, Montréal, Québec, Canada
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47
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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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48
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Goodale SE, Ahmed N, Zhao C, de Zwart JA, Özbay PS, Picchioni D, Duyn J, Englot DJ, Morgan VL, Chang C. fMRI-based detection of alertness predicts behavioral response variability. eLife 2021; 10:62376. [PMID: 33960930 PMCID: PMC8104962 DOI: 10.7554/elife.62376] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 04/09/2021] [Indexed: 12/16/2022] Open
Abstract
Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.
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Affiliation(s)
- Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, United States.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States
| | - Nafis Ahmed
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, United States
| | - Chong Zhao
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, United States
| | - Jacco A de Zwart
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Pinar S Özbay
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Dante Picchioni
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Jeff Duyn
- Advanced MRI Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, United States.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, United States.,Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, United States
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, United States.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States.,Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, United States
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, United States.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, United States
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49
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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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50
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Pujol J, Blanco-Hinojo L, Gallart L, Moltó L, Martínez-Vilavella G, Vilà E, Pacreu S, Adalid I, Deus J, Pérez-Sola V, Fernández-Candil J. Largest scale dissociation of brain activity at propofol-induced loss of consciousness. Sleep 2021; 44:5894260. [PMID: 32813022 DOI: 10.1093/sleep/zsaa152] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/06/2020] [Indexed: 11/14/2022] Open
Abstract
The brain is a functional unit made up of multilevel connected elements showing a pattern of synchronized activity that varies in different states. The wake-sleep cycle is a major variation of brain functional condition that is ultimately regulated by subcortical arousal- and sleep-promoting cell groups. We analyzed the evolution of functional MRI (fMRI) signal in the whole cortex and in a deep region including most sleep- and wake-regulating subcortical nuclei at loss of consciousness induced by the hypnotic agent propofol. Optimal data were obtained in 21 of the 30 healthy participants examined. A dynamic analysis of fMRI time courses on a time-scale of seconds was conducted to characterize consciousness transition, and functional connectivity maps were generated to detail the anatomy of structures showing different dynamics. Inside the magnet, loss of consciousness was marked by the participants ceasing to move their hands. We observed activity synchronization after loss of consciousness within both the cerebral cortex and subcortical structures. However, the evolution of fMRI signal was dissociated, showing a transient reduction of global cortico-subcortical coupling that was restored during the unconscious state. An exception to cortico-subcortical decoupling was a brain network related to self-awareness (i.e. the default mode network) that remained connected to subcortical brain structures. Propofol-induced unconsciousness is thus characterized by an initial, transitory dissociated synchronization at the largest scale of brain activity. Such cortico-subcortical decoupling and subsequent recoupling may allow the brain to detach from waking activity and reorganize into a functionally distinct state.
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Affiliation(s)
- Jesus Pujol
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain.,Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain
| | - Laura Blanco-Hinojo
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain.,Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain
| | - Lluís Gallart
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain.,Department of Surgery, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Luís Moltó
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | | | - Esther Vilà
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Susana Pacreu
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Irina Adalid
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Joan Deus
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain.,Department of Psychobiology and Methodology in Health Sciences, Autonomous University of Barcelona, Barcelona, Spain
| | - Víctor Pérez-Sola
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain.,Institute of Neuropsychiatry and Addictions, Hospital del Mar-IMIM and Department of Psychiatry, Autonomous University of Barcelona, Barcelona, Spain
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