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Picchioni D, Yang FN, de Zwart JA, Wang Y, Mandelkow H, Özbay PS, Chen G, Taylor PA, Lam N, Chappel-Farley MG, Chang C, Liu J, van Gelderen P, Duyn JH. Arousal threshold reveals novel neural markers of sleep depth independently from the conventional sleep stages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.08.09.607376. [PMID: 39149368 PMCID: PMC11326234 DOI: 10.1101/2024.08.09.607376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Reports of sleep-specific brain activity patterns have been constrained by assessing brain function as it related to the conventional polysomnographic sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. The current study used all-night functional MRI sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to characterize sleep depth better by searching for novel neural markers of sleep depth that are neuroanatomically localized and temporally unrelated to the conventional stages. Functional correlation values calculated in a four-min time window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis, and compared to models with sleep stage to determine the unique relationships with AAT. These unique relationships were for thalamocerebellar correlations, the relationship between the right language network and the right "default-mode network dorsal medial prefrontal cortex subsystem," and the relationship between thalamus and ventral attention network. These novel neural markers of sleep depth would have remained undiscovered if the data were merely analyzed with the conventional sleep stages.
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Affiliation(s)
- Dante Picchioni
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Fan Nils Yang
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Jacco A de Zwart
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Yicun Wang
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- Department of Radiology, Stony Brook University, USA
| | - Hendrik Mandelkow
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- Artificial Intelligence for Image-Guided Therapy, Koninklijke Philips, Netherlands
| | - Pinar S Özbay
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- Institute of Biomedical Engineering, Boğaziçi University, Turkey
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Niki Lam
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- School of Medicine and Dentistry, University of Rochester, USA
| | - Miranda G Chappel-Farley
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- Center for Sleep and Circadian Science, University of Pittsburgh, USA
| | - Catie Chang
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- Departments of Electrical Engineering and Computer Science, Vanderbilt University, USA
| | - Jiaen Liu
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, USA
| | - Peter van Gelderen
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Jeff H Duyn
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
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Pereira M, Chen X, Paltarzhytskaya A, Pacheсo Y, Muller N, Bovy L, Lei X, Chen W, Ren H, Song C, Lewis LD, Dang-Vu TT, Czisch M, Picchioni D, Duyn J, Peigneux P, Tagliazucchi E, Dresler M. Sleep neuroimaging: Review and future directions. J Sleep Res 2025:e14462. [PMID: 39940102 DOI: 10.1111/jsr.14462] [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: 07/08/2024] [Revised: 11/29/2024] [Accepted: 12/29/2024] [Indexed: 02/14/2025]
Abstract
Sleep research has evolved considerably since the first sleep electroencephalography recordings in the 1930s and the discovery of well-distinguishable sleep stages in the 1950s. While electrophysiological recordings have been used to describe the sleeping brain in much detail, since the 1990s neuroimaging techniques have been applied to uncover the brain organization and functional connectivity of human sleep with greater spatial resolution. The combination of electroencephalography with different neuroimaging modalities such as positron emission tomography, structural magnetic resonance imaging and functional magnetic resonance imaging imposes several challenges for sleep studies, for instance, the need to combine polysomnographic recordings to assess sleep stages accurately, difficulties maintaining and consolidating sleep in an unfamiliar and restricted environment, scanner-induced distortions with physiological artefacts that may contaminate polysomnography recordings, and the necessity to account for all physiological changes throughout the sleep cycles to ensure better data interpretability. Here, we review the field of sleep neuroimaging in healthy non-sleep-deprived populations, from early findings to more recent developments. Additionally, we discuss the challenges of applying concurrent electroencephalography and imaging techniques to sleep, which consequently have impacted the sample size and generalizability of studies, and possible future directions for the field.
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Affiliation(s)
- Mariana Pereira
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Xinyuan Chen
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | | | - Yibran Pacheсo
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Nils Muller
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Leonore Bovy
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Wei Chen
- School of Information Science and Technology & Human Phenome Institute, Fudan University, Shanghai, China
| | - Haoran Ren
- School of Health and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Chen Song
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Thien Thanh Dang-Vu
- Department of Health, Kinesiology and Applied Physiology, Concordia University & Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montreal, Quebec, Canada
| | | | - Dante Picchioni
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Jeff Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, and UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Enzo Tagliazucchi
- Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health Institute, Universidad Adolfo Ibanez, Santiago, Chile
| | - Martin Dresler
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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3
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Theis N, Bahuguna J, Rubin JE, Cape J, Iyengar S, Prasad KM. Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576937. [PMID: 38328170 PMCID: PMC10849576 DOI: 10.1101/2024.01.23.576937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.
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Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jyotika Bahuguna
- Department of Neuroscience, Laboratoire de Neurosciences Cognitive et Adaptive, University of Strasbourg, France
| | | | - Joshua Cape
- Department of Statistics, University of Wisconsin-Madison, WI, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, PA, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
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4
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Titone S, Samogin J, Peigneux P, Swinnen SP, Mantini D, Albouy G. Frequency-dependent connectivity in large-scale resting-state brain networks during sleep. Eur J Neurosci 2024; 59:686-702. [PMID: 37381891 DOI: 10.1111/ejn.16080] [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: 02/18/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023]
Abstract
Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.
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Affiliation(s)
- Simon Titone
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- LBI-KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Jessica Samogin
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Philippe Peigneux
- Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at the Centre for Research in Cognition and Neurosciences (CRCN), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Stephan P Swinnen
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- LBI-KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Genevieve Albouy
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- LBI-KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, Utah, USA
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5
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Spina MA, Andrillon T, Quin N, Wiley JF, Rajaratnam SMW, Bei B. Does providing feedback and guidance on sleep perceptions using sleep wearables improve insomnia? Findings from "Novel Insomnia Treatment Experiment": a randomized controlled trial. Sleep 2023; 46:zsad167. [PMID: 37294865 PMCID: PMC10485571 DOI: 10.1093/sleep/zsad167] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 05/08/2023] [Indexed: 06/11/2023] Open
Abstract
STUDY OBJECTIVES Insomnia is a disorder diagnosed based on self-reported sleep complaints. Differences between self-reported and sensor-based sleep parameters (sleep-wake state discrepancy) are common but not well-understood in individuals with insomnia. This two-arm, parallel-group, single-blind, superiority randomized-controlled trial examined whether monitoring sleep using wearable devices and providing support for interpretation of sensor-based sleep data improved insomnia symptoms or impacted sleep-wake state discrepancy. METHODS A total of 113 (age M = 47.53; SD = 14.37, 64.9% female) individuals with significant insomnia symptoms (Insomnia Severity Index(ISI) ≥10) from the community were randomized 1:1 (permuted block randomization) to receive 5 weeks (1) Intervention (n = 57): feedback about sensor-based sleep (Fitbit and EEG headband) with guidance for data interpretation and ongoing monitoring, and (2) Control (n = 56): sleep education and hygiene. Both groups received one individual session and two check-in calls. The ISI (primary outcome), sleep disturbance (SDis), sleep-related impairment (SRI), depression, and anxiety were assessed at baseline and post-intervention. RESULTS In total, 103 (91.2%) participants completed the study. Intention-to-treat multiple regression with multiple imputations showed that after controlling for baseline values, compared to the Control group (n = 51), the Intervention group (n = 52) had lower ISI (p = .011, d = 0.51) and SDis (p = .036, d = 0.42) post-intervention, but differences in SRI, depression, anxiety, and sleep-wake state discrepancy parameters (total sleep time, sleep onset latency, and wake after sleep onset) were not meaningful (P-values >.40). CONCLUSIONS Providing feedback and guidance about sensor-based sleep parameters reduced insomnia severity and sleep disturbance but did not alter sleep-wake state discrepancy in individuals with insomnia more than sleep hygiene and education. The role of sleep wearable devices among individuals with insomnia requires further research. CLINICAL TRIAL REGISTRATION The Novel Insomnia Treatment Experiment (NITE): the effectiveness of incorporating appropriate guidance for sleep wearables in users with insomnia. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378452, Australia New Zealand Clinical Trials Registry: ACTRN12619001636145.
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Affiliation(s)
- Marie-Antoinette Spina
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Thomas Andrillon
- School of Philosophical, Historical, and International Studies, Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, VIC, Australia
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Nina Quin
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Joshua F Wiley
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Shantha M W Rajaratnam
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Bei Bei
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
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6
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Zhang R, Tomasi D, Shokri-Kojori E, Manza P, Feldman DE, Kroll DS, Biesecker CL, McPherson KL, Schwandt M, Wang GJ, Wiers CE, Volkow ND. Effect of detoxification on N3 sleep correlates with brain functional but not structural changes in alcohol use disorder. Drug Alcohol Depend 2022; 238:109545. [PMID: 35779511 PMCID: PMC9444901 DOI: 10.1016/j.drugalcdep.2022.109545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Sleep disturbances are very common in alcohol use disorder (AUD) and contribute to relapse. Detoxification appears to have limited effects on sleep problems. However, inter-individual differences and related brain mechanisms have not been closely examined. METHODS We examined N3 sleep and the associated brain functional and structural changes in 30 AUD patients (9 Females, mean age: 42 years) undergoing a 3-week inpatient detoxification. Patients' N3 sleep, resting state functional connectivity (RSFC), grey matter volume (GMV) and negative mood were measured on week 1 and week 3. RESULTS AUD patients did not show significant N3 sleep recovery after 3-weeks of detoxification. However, we observed large variability among AUD patients. Inter-individual variations in N3 increases were associated with increases in midline default mode network (DMN) RSFC but not with GMV using a whole-brain approach. Exploratory analyses revealed significant sex by detoxification effects on N3 sleep such that AUD females showed greater N3 increases than AUD males. Further, N3 increases fully mediated the effect of mood improvement on DMN RSFC increases. CONCLUSIONS We show a significant relationship between N3 and DMN functional changes in AUD over time/abstinence. The current findings may have clinical implications for monitoring brain recovery in AUD using daily sleep measures, which might help guide individualized treatments. Future investigations on sex differences with a larger sample and with longitudinal data for a longer period of abstinence are needed.
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Affiliation(s)
- Rui Zhang
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA.
| | - Dardo Tomasi
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Ehsan Shokri-Kojori
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Peter Manza
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Dana E Feldman
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Danielle S Kroll
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Catherine L Biesecker
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Katherine L McPherson
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Melanie Schwandt
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Gene-Jack Wang
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Corinde E Wiers
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Nora D Volkow
- National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, MD 20892-1013, USA; National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD 20892-1013, USA.
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7
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Bensen-Boakes DB, Lovato N, Meaklim H, Bei B, Scott H. “Sleep-wake state discrepancy”: toward a common understanding and standardized nomenclature. Sleep 2022; 45:6668259. [DOI: 10.1093/sleep/zsac187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Darah-Bree Bensen-Boakes
- Flinders Health and Medical Research Institute: Sleep Health, Flinders University , Adelaide , Australia
| | - Nicole Lovato
- Flinders Health and Medical Research Institute: Sleep Health, Flinders University , Adelaide , Australia
| | - Hailey Meaklim
- Turner Institute for Brain and Mental Health, Monash University , Clayton , Australia
| | - Bei Bei
- Turner Institute for Brain and Mental Health, Monash University , Clayton , Australia
| | - Hannah Scott
- Flinders Health and Medical Research Institute: Sleep Health, Flinders University , Adelaide , Australia
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8
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Baird B, Aparicio MK, Alauddin T, Riedner B, Boly M, Tononi G. Episodic thought distinguishes spontaneous cognition in waking from REM and NREM sleep. Conscious Cogn 2022; 97:103247. [PMID: 34864360 PMCID: PMC8752510 DOI: 10.1016/j.concog.2021.103247] [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: 03/25/2021] [Revised: 10/15/2021] [Accepted: 11/22/2021] [Indexed: 01/03/2023]
Abstract
Evidence suggests continuity between cognition in waking and sleeping states. However, one type of cognition that may differ is episodic thoughts of the past and future. The current study investigated this across waking, NREM sleep and REM sleep. We analyzed thought reports obtained from a large sample of individuals (N = 138) who underwent experience-sampling during wakefulness as well as serial awakenings in sleep. Our data suggest that while episodic thoughts are common during waking spontaneous thought, episodic thoughts of both the past and the future rarely occur in either N2 or REM sleep. Moreover, replicating previous findings, episodic thoughts during wakefulness exhibit a strong prospective bias and frequently involve autobiographical planning. Together, these results suggest that the occurrence of spontaneous episodic thoughts differs substantially across waking and dreaming sleep states. We suggest that this points to a difference in the way that human consciousness is typically experienced across the sleep-wake cycle.
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Affiliation(s)
- Benjamin Baird
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, USA.
| | - Mariel Kalkach Aparicio
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, USA
| | - Tariq Alauddin
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, USA
| | - Brady Riedner
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, USA
| | - Melanie Boly
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, USA; Department of Neurology, University of Wisconsin - Madison, Madison, WI, USA
| | - Giulio Tononi
- Center for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin - Madison, Madison, WI, USA
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9
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Jeong SO, Kang J, Pae C, Eo J, Park SM, Son J, Park HJ. Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics. Neuroimage 2021; 244:118618. [PMID: 34571159 DOI: 10.1016/j.neuroimage.2021.118618] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.
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Affiliation(s)
- Seok-Oh Jeong
- Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinseok Eo
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Sung Min Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Junho Son
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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10
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Vespa S, Heyse J, Stumpp L, Liberati G, Ferrao Santos S, Rooijakkers H, Nonclercq A, Mouraux A, van Mierlo P, El Tahry R. Vagus Nerve Stimulation Elicits Sleep EEG Desynchronization and Network Changes in Responder Patients in Epilepsy. Neurotherapeutics 2021; 18:2623-2638. [PMID: 34668148 PMCID: PMC8804116 DOI: 10.1007/s13311-021-01124-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/23/2022] Open
Abstract
Neural desynchronization was shown as a key mechanism of vagus nerve stimulation (VNS) action in epilepsy, and EEG synchronization measures are explored as possible response biomarkers. Since brain functional organization in sleep shows different synchrony and network properties compared to wakefulness, we aimed to explore the effects of acute VNS on EEG-derived measures in the two different states of vigilance. EEG epochs were retrospectively analyzed from twenty-four VNS-treated epileptic patients (11 responders, 13 non-responders) in calm wakefulness and stage N2 sleep. Weighted Phase Lag Index (wPLI) was computed as connectivity measure of synchronization, for VNS OFF and VNS ON conditions. Global efficiency (GE) was computed as a network measure of integration. Ratios OFF/ON were obtained as desynchronization/de-integration index. Values were compared between responders and non-responders, and between EEG states. ROC curve and area-under-the-curve (AUC) analysis was performed for response classification. In responders, stronger VNS-induced theta desynchronization (p < 0.05) and decreased GE (p < 0.05) were found in sleep, but not in wakefulness. Theta sleep wPLI Ratio OFF/ON yielded an AUC of 0.825, and 79% accuracy as a response biomarker if a cut-off value is set at 1.05. Considering all patients, the VNS-induced GE decrease was significantly more important in sleep compared to awake EEG state (p < 0.01). In conclusion, stronger sleep EEG desynchronization in theta band distinguishes responders to VNS therapy from non-responders. VNS-induced reduction of network integration occurs significantly more in sleep than in wakefulness.
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Affiliation(s)
- Simone Vespa
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium.
| | - Jolan Heyse
- Medical Image and Signal Processing Group (MEDISIP), Ghent University, Ghent, Belgium
| | - Lars Stumpp
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
| | - Giulia Liberati
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
| | - Susana Ferrao Santos
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
- Centre for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Herbert Rooijakkers
- Centre for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Antoine Nonclercq
- Bio, Electro and Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium
| | - André Mouraux
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group (MEDISIP), Ghent University, Ghent, Belgium
| | - Riëm El Tahry
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
- Centre for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
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11
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Kang J, Jeong S, Pae C, Park H. Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics. Hum Brain Mapp 2021; 42:3411-3428. [PMID: 33934421 PMCID: PMC8249903 DOI: 10.1002/hbm.25442] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/24/2022] Open
Abstract
The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Seok‐Oh Jeong
- Department of StatisticsHankuk University of Foreign StudiesYong‐In, SeoulSouth Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Hae‐Jeong Park
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
- Graduate School of Medical Science, Brain Korea 21 ProjectYonsei University College of MedicineSeoulSouth Korea
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12
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Park HJ, Kang J. A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles. Front Comput Neurosci 2021; 15:590019. [PMID: 33935674 PMCID: PMC8079648 DOI: 10.3389/fncom.2021.590019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
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Affiliation(s)
- Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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13
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Blanco B, Molnar M, Carreiras M, Collins-Jones LH, Vidal E, Cooper RJ, Caballero-Gaudes C. Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants. NEUROPHOTONICS 2021; 8:025011. [PMID: 34136588 PMCID: PMC8200331 DOI: 10.1117/1.nph.8.2.025011] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/25/2021] [Indexed: 05/27/2023]
Abstract
Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain's organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort ( N = 99 ) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.
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Affiliation(s)
- Borja Blanco
- Basque Center on Cognition, Brain, and Language, Donostia/San Sebastián, Spain
- University College London, Biomedical Optics Research Laboratory, DOT-HUB, London, United Kingdom
| | - Monika Molnar
- University of Toronto, Faculty of Medicine, Department of Speech-Language Pathology, Toronto, Ontario, Canada
| | - Manuel Carreiras
- Basque Center on Cognition, Brain, and Language, Donostia/San Sebastián, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Liam H. Collins-Jones
- University College London, Biomedical Optics Research Laboratory, DOT-HUB, London, United Kingdom
| | - Ernesto Vidal
- University College London, Biomedical Optics Research Laboratory, DOT-HUB, London, United Kingdom
| | - Robert J. Cooper
- University College London, Biomedical Optics Research Laboratory, DOT-HUB, London, United Kingdom
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14
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Altered Coactive Micropattern Connectivity in the Default-Mode Network during the Sleep-Wake Cycle. Neural Plast 2020. [DOI: 10.1155/2020/8876131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The default-mode network (DMN) is believed to be associated with levels of consciousness, but how the functional connectivity (FC) of the DMN changes across different states of consciousness is still unclear. In the current work, we addressed this issue by exploring the coactive micropattern (CAMP) networks of the DMN according to the CAMPs of rat DMN activity during the sleep-wake cycle and tracking their topological alterations among different states of consciousness. Three CAMP networks were observed in DMN activity, and they displayed greater FC and higher efficiency than the original DMN structure in all states of consciousness, implying more efficient information processing in the CAMP networks. Furthermore, no significant differences in FC or network properties were found among the three CAMP networks in the waking state. However, the three networks were distinct in their characteristics in two sleep states, indicating that different CAMP networks played specific roles in distinct sleep states. In addition, we found that the changes in the FC and network properties of the CAMP networks were similar to those in the original DMN structure, suggesting intrinsic effects of various states of consciousness on DMN dynamics. Our findings revealed three underlying CAMP networks within the DMN dynamics and deepened the current knowledge concerning FC alterations in the DMN during conscious changes in the sleep-wake cycle.
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15
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Dynamic Properties of Human Default Mode Network in Eyes-Closed and Eyes-Open. Brain Topogr 2020; 33:720-732. [PMID: 32803623 DOI: 10.1007/s10548-020-00792-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/08/2020] [Indexed: 10/23/2022]
Abstract
The default mode network (DMN) reflects spontaneous activity in the resting human brain. Previous studies examined the difference in static functional connectivity (sFC) of the DMN between eyes-closed (EC) and eyes-open (EO) using the resting-state functional magnetic resonance imaging (rs-fMRI) data. However, it remains unclear about the difference in dynamic FC (dFC) of the DMN between EC and EO. To this end, we acquired rs-fMRI data from 19 subjects in two different statues (EC and EO) and selected a seed region-of-interest (ROI) at the posterior cingulate cortex (PCC) to generate the sFC map. We identified the DMN consisting of ten clusters that were significantly correlated with the PCC. By using a sliding-window approach, we analyzed the dFC of the DMN. Then, the Newman's modularity algorithm was applied to identify dFC states based on nodal total connectivity strength in each sliding-window. In addition, graph-theory based network analysis was applied to detect dynamic topological properties of the DMN. We identified three group-level dFC states (State1, 2 and 3) that reflects the strength of dFC within the DMN between EC and EO in different time. The following results were reached: (1) no significant difference in sFC between EC and EO, (2) dFC was lower in State2 but higher in State3 in EC than in EO, (3) lower clustering coefficient, local efficiency, and global efficiency, but higher characteristic path length in State2 in EC than in EO, and (4) lower nodal strength in the precuneus (PCUN), PCC, angular gyrus (ANG), middle temporal gyrus (MTG) and medial prefrontal cortex (MPFC) in State3 in EC. These results suggested different resting statuses, EC and EO, may induce different time-varying neural activity in the DMN.
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16
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Lee CW, Blanco B, Dempsey L, Chalia M, Hebden JC, Caballero-Gaudes C, Austin T, Cooper RJ. Sleep State Modulates Resting-State Functional Connectivity in Neonates. Front Neurosci 2020; 14:347. [PMID: 32362811 PMCID: PMC7180180 DOI: 10.3389/fnins.2020.00347] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 03/23/2020] [Indexed: 01/26/2023] Open
Abstract
The spontaneous cerebral activity that gives rise to resting-state networks (RSNs) has been extensively studied in infants in recent years. However, the influence of sleep state on the presence of observable RSNs has yet to be formally investigated in the infant population, despite evidence that sleep modulates resting-state functional connectivity in adults. This effect could be extremely important, as most infant neuroimaging studies rely on the neonate to remain asleep throughout data acquisition. In this study, we combine functional near-infrared spectroscopy with electroencephalography to simultaneously monitor sleep state and investigate RSNs in a cohort of healthy term born neonates. During active sleep (AS) and quiet sleep (QS) our newborn neonates show functional connectivity patterns spatially consistent with previously reported RSN structures. Our three independent functional connectivity analyses revealed stronger interhemispheric connectivity during AS than during QS. In turn, within hemisphere short-range functional connectivity seems to be enhanced during QS. These findings underline the importance of sleep state monitoring in the investigation of RSNs.
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Affiliation(s)
- Chuen Wai Lee
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Borja Blanco
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom.,Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain
| | - Laura Dempsey
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | - Maria Chalia
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy C Hebden
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | | | - Topun Austin
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | - Robert J Cooper
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
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17
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Goldstein-Piekarski AN, Holt-Gosselin B, O'Hora K, Williams LM. Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology 2020; 45:192-204. [PMID: 31426055 PMCID: PMC6879628 DOI: 10.1038/s41386-019-0483-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/21/2019] [Accepted: 07/22/2019] [Indexed: 12/13/2022]
Abstract
In advancing precision psychiatry, we focus on what imaging technology and computational approaches offer for the future of diagnostic subtyping and personalized tailoring of interventions for sleep impairment in mood and anxiety disorders. Current diagnostic criteria for mood and anxiety tend to lump different forms of sleep disturbance together. Parsing the biological features of sleep impairment and brain circuit dysfunction is one approach to identifying subtypes within these disorders that are mechanistically coherent and offer targets for intervention. We focus on two large-scale neural circuits implicated in sleep impairment and in mood and anxiety disorders: the default mode network and negative affective network. Through a synthesis of existing knowledge about these networks, we pose a testable framework for understanding how hyper- versus hypo-engagement of these networks may underlie distinct features of mood and sleep impairment. Within this framework we consider whether poor sleep quality may have an explanatory role in previously observed associations between network dysfunction and mood symptoms. We expand this framework to future directions including the potential for connecting circuit-defined subtypes to more distal features derived from digital phenotyping and wearable technologies, and how new discovery may be advanced through machine learning approaches.
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Affiliation(s)
- Andrea N Goldstein-Piekarski
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
| | - Bailey Holt-Gosselin
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94305, USA
| | - Kathleen O'Hora
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94305, USA.
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA.
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18
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Kang J, Pae C, Park HJ. Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex. PLoS One 2019; 14:e0222161. [PMID: 31498822 PMCID: PMC6733463 DOI: 10.1371/journal.pone.0222161] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/22/2019] [Indexed: 11/19/2022] Open
Abstract
The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretical analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In the state transition, the default mode network acts as a transition hub, while coactivation of the prefrontal cortex and default mode network is captured as the global minimum. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by a graph-theoretical analysis of the nonlinear state transition network of the brain.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
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19
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Stevner ABA, Vidaurre D, Cabral J, Rapuano K, Nielsen SFV, Tagliazucchi E, Laufs H, Vuust P, Deco G, Woolrich MW, Van Someren E, Kringelbach ML. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nat Commun 2019; 10:1035. [PMID: 30833560 PMCID: PMC6399232 DOI: 10.1038/s41467-019-08934-3] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 02/11/2019] [Indexed: 12/02/2022] Open
Abstract
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep. Sleep is composed of a number of different stages, each associated with a different pattern of brain activity. Here, using a data-driven Hidden Markov Model (HMM) of fMRI data, the authors discover a more complex set of neural activity states underlying the conventional stages of non-REM sleep.
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Affiliation(s)
- A B A Stevner
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK. .,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000, Aarhus, Denmark. .,Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.
| | - D Vidaurre
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK
| | - J Cabral
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057, Braga, Portugal
| | - K Rapuano
- Department of Psychological and Brain Sciences, Dartmouth College, 03755, Hanover, NH, USA
| | - S F V Nielsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark
| | - E Tagliazucchi
- Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, The Netherlands.,Department of Neurology, University Hospital Schleswig Holstein, Christian-Alrbrechts-Universität, 24105, Kiel, Germany.,Department of Neurology and Brain Imaging Center, Goethe University, 60528, Frankfurt am Main, Germany
| | - H Laufs
- Department of Neurology, University Hospital Schleswig Holstein, Christian-Alrbrechts-Universität, 24105, Kiel, Germany.,Department of Neurology and Brain Imaging Center, Goethe University, 60528, Frankfurt am Main, Germany
| | - P Vuust
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC, 3800, Australia
| | - M W Woolrich
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK
| | - E Van Someren
- Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, The Netherlands.,Departments of Integrative Neurophysiology and Psychiatry GGZ-InGeest, Amsterdam Neuroscience, VU University and Medical Center, 1081 HV, Amsterdam, The Netherlands
| | - M L Kringelbach
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK.,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000, Aarhus, Denmark.,Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057, Braga, Portugal
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20
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Tozzi A, Peters JF. The common features of different brain activities. Neurosci Lett 2019; 692:41-46. [PMID: 30385139 DOI: 10.1016/j.neulet.2018.10.054] [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: 05/08/2018] [Revised: 07/24/2018] [Accepted: 10/29/2018] [Indexed: 11/15/2022]
Abstract
The term "brain activity" refers to a wide range of mental faculties that can be assessed either on anatomical/functional micro-, meso- and macro- spatiotemporal scales of observation, or on intertwined cortical levels with mutual interactions. Our aim is to show that every brain activity encompassed in a given anatomical or functional level necessarily displays a counterpart in others, i.e., they are "dual". "Duality" refers to the case where two seemingly different physical systems turn out to be equivalent. We describe a method, based on novel topological findings, that makes different manifolds (standing for different brain activities) able to scatter, collide and combine, in order that they merge, condense and stitch together in a quantifiable way. We develop a computational tool which explains how, despite their local cortical functional differences, all mental processes, from perception to emotions, from cognition to mind wandering, may be reduced to a single, general brain activity that takes place in dimensions higher than the classical three-dimensional plus time. This framework permits a topological duality among different brain activities and neuro-techniques, because it holds for all the types of spatio-temporal nervous functions, independent of their cortical location, inter- and intra-level relationships, strength, magnitude and boundaries.
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Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, University of North Texas 1155 Union Circle, #311427 Denton, TX 76203-5017, USA.
| | - James F Peters
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle Winnipeg, MB R3T 5V6, Canada; Department of Mathematics, Adıyaman University, 02040, Adıyaman, Turkey.
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Del Rio-Bermudez C, Blumberg MS. Active Sleep Promotes Functional Connectivity in Developing Sensorimotor Networks. Bioessays 2018; 40:e1700234. [PMID: 29508913 PMCID: PMC6247910 DOI: 10.1002/bies.201700234] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 02/01/2018] [Indexed: 12/15/2022]
Abstract
A ubiquitous feature of active (REM) sleep in mammals and birds is its relative abundance in early development. In rat pups across the first two postnatal weeks, active sleep promotes the expression of synchronized oscillatory activity within and between cortical and subcortical sensorimotor structures. Sensory feedback from self-generated myoclonic twitches - which are produced exclusively during active sleep - also triggers neural oscillations in those structures. We have proposed that one of the functions of active sleep in early infancy is to provide a context for synchronizing developing structures. Specifically, neural oscillations contribute to a variety of neurodevelopmental processes, including synapse formation, neuronal differentiation and migration, apoptosis, and the refinement of topographic maps. In addition, synchronized oscillations promote functional connectivity between distant brain areas. Consequently, any condition or manipulation that restricts active sleep can, in turn, deprive the infant animal of substantial sensory experience, resulting in atypical developmental trajectories.
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Affiliation(s)
- Carlos Del Rio-Bermudez
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, 52242, Iowa, USA
- Delta Center, University of Iowa, Iowa City, 52242, Iowa, USA
| | - Mark S Blumberg
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, 52242, Iowa, USA
- Delta Center, University of Iowa, Iowa City, 52242, Iowa, USA
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, 52245, Iowa, USA
- Department of Biology, University of Iowa, Iowa City, 52242, Iowa, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, 52242, Iowa, USA
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22
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McKillop LE, Vyazovskiy VV. Sleep- and Wake-Like States in Small Networks In Vivo and In Vitro. Handb Exp Pharmacol 2018; 253:97-121. [PMID: 30443784 DOI: 10.1007/164_2018_174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Wakefulness and sleep are highly complex and heterogeneous processes, involving multiple neurotransmitter systems and a sophisticated interplay between global and local networks of neurons and non-neuronal cells. Macroscopic approaches applied at the level of the whole organism, view sleep as a global behaviour and allow for investigation into aspects such as the effects of insufficient or disrupted sleep on cognitive function, metabolism, thermoregulation and sensory processing. While significant progress has been achieved using such large-scale approaches, the inherent complexity of sleep-wake regulation has necessitated the development of methods which tackle specific aspects of sleep in isolation. One way this may be achieved is by investigating specific cellular or molecular phenomena in the whole organism in situ, either during spontaneous or induced sleep-wake states. This approach has greatly advanced our knowledge about the electrophysiology and pharmacology of ion channels, specific receptors, intracellular pathways and the small networks implicated in the control and regulation of the sleep-wake cycle. Importantly though, there are a variety of external and internal factors that influence global behavioural states which are difficult to control for using these approaches. For this reason, over the last few decades, ex vivo experimental models have become increasingly popular and have greatly advanced our understanding of many fundamental aspects of sleep, including the neuroanatomy and neurochemistry of sleep states, sleep regulation, the origin and dynamics of specific sleep oscillations, network homeostasis as well as the functional roles of sleep. This chapter will focus on the use of small neuronal networks as experimental models and will highlight the most significant and novel insights these approaches have provided.
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Affiliation(s)
- Laura E McKillop
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
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Brain network dynamics in high-functioning individuals with autism. Nat Commun 2017; 8:16048. [PMID: 28677689 PMCID: PMC5504272 DOI: 10.1038/ncomms16048] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 05/24/2017] [Indexed: 01/28/2023] Open
Abstract
Theoretically, autism should be underpinned by aberrant brain dynamics. However, how brain activity changes over time in individuals with autism spectrum disorder (ASD) remains unknown. Here we characterize brain dynamics in autism using an energy-landscape analysis applied to resting-state fMRI data. Whereas neurotypical brain activity frequently transits between two major brain states via an intermediate state, high-functioning adults with ASD show fewer neural transitions due to an unstable intermediate state, and these infrequent transitions predict the severity of autism. Moreover, in contrast to the controls whose IQ is correlated with the neural transition frequency, IQ scores of individuals with ASD are instead predicted by the stability of their brain dynamics. Finally, such brain–behaviour associations are related to functional segregation between brain networks. These findings suggest that atypical functional coordination in the brains of adults with ASD underpins overly stable neural dynamics, which supports both their ASD symptoms and cognitive abilities. Though individuals with autism spectrum disorder (ASD) show a number of neural abnormalities, the relationship between global dynamic neural patterns and ASD symptoms remains unclear. Here, authors describe such global brain dynamics, relate these to cognitive abilities, ASD symptoms, and predict ASD diagnosis.
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Ezaki T, Watanabe T, Ohzeki M, Masuda N. Energy landscape analysis of neuroimaging data. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2017; 375:rsta.2016.0287. [PMID: 28507232 PMCID: PMC5434078 DOI: 10.1098/rsta.2016.0287] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/27/2017] [Indexed: 05/09/2023]
Abstract
Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.
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Affiliation(s)
- Takahiro Ezaki
- National Institute of Informatics, Hitotsubashi, Chiyoda-ku, Tokyo, Japan
- Kawarabayashi Large Graph Project, ERATO, JST, c/o Global Research Center for Big Data Mathematics, NII, Chiyoda-ku, Tokyo, Japan
| | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AZ, UK
| | - Masayuki Ohzeki
- Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK
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Ashourvan A, Gu S, Mattar MG, Vettel JM, Bassett DS. The energy landscape underpinning module dynamics in the human brain connectome. Neuroimage 2017; 157:364-380. [PMID: 28602945 PMCID: PMC5600845 DOI: 10.1016/j.neuroimage.2017.05.067] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 05/26/2017] [Accepted: 05/31/2017] [Indexed: 11/03/2022] Open
Abstract
Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain's dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations.
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Affiliation(s)
- Arian Ashourvan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Shi Gu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Applied Mathematics and Computational Science Graduate Program, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo G Mattar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jean M Vettel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA; Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Tozzi A, Peters JF. From abstract topology to real thermodynamic brain activity. Cogn Neurodyn 2017; 11:283-292. [PMID: 28559956 PMCID: PMC5430247 DOI: 10.1007/s11571-017-9431-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 02/14/2017] [Accepted: 03/08/2017] [Indexed: 12/25/2022] Open
Abstract
Recent approaches to brain phase spaces reinforce the foremost role of symmetries and energy requirements in the assessment of nervous activity. Changes in thermodynamic parameters and dimensions occur in the brain during symmetry breakings and transitions from one functional state to another. Based on topological results and string-like trajectories into nervous energy landscapes, we provide a novel method for the evaluation of energetic features and constraints in different brain functional activities. We show how abstract approaches, namely the Borsuk-Ulam theorem and its variants, may display real, energetic physical counterparts. When topology meets the physics of the brain, we arrive at a general model of neuronal activity, in terms of multidimensional manifolds and computational geometry, that has the potential to be operationalized.
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Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, Department of Physics, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA
| | - James F. Peters
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor’s Circle, Winnipeg, MB R3T 5V6 Canada
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Al-Rawas SF, Abdelbasit KM, Al-Lawati HH, Poothrikovil R, Al-Rawahi AK, Khan AA, Delamont RS. The Usefulness of Nap Sleep Recording During Routine Electroencephalography: An Audit Study. Oman Med J 2017; 32:256-258. [PMID: 28584610 PMCID: PMC5447796 DOI: 10.5001/omj.2017.48] [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: 10/02/2016] [Accepted: 02/27/2017] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES A measure to increase the electroencephalogram (EEG) outcome includes a short period of nap sleep during a routine standard EEG with the aim of increasing its sensitivity to interictal abnormalities or provoking seizures. As part of an ongoing auditing of our EEG data, we aimed to investigate the contribution of nap sleep during routine outpatient department based EEGs requested for a variety of reasons. METHODS EEG data at the Department of Clinical Physiology at Sultan Qaboos University Hospital, Oman, from July 2006 to December 2007 and from January 2009 to December 2010 (total 42 months) were reviewed. The EEGs were for patients older than 13-years referred for possible epilepsy, blackouts, headache, head trauma, and other non-specified attacks. The recording period was between 20 to 40 minutes. Abnormalities were identified during waking and nap sleep periods. RESULTS A total of 2 547 EEGs were reviewed and 744 were abnormal (29.2%). Of those abnormal EEGs, nap sleep was obtained in 258 (34.7%) EEGs, and 39 (15.1%) showed abnormalities during nap sleep. Nineteen out of the 39 (48.7%) EEGs were abnormal during awake and nap sleep; and 20 (51.3%) were abnormal during nap sleep, which represented only 2.7% of the total abnormal EEGs (n = 744). CONCLUSIONS The contribution of the short nap sleep to the pickup rate of interictal abnormalities in EEG was minimal. We recommend the EEG service to include one cycle of spontaneous sleep EEG directed at patients with a history suggestive of epilepsy if their awake EEGs are normal.
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Affiliation(s)
- Sami Farah Al-Rawas
- Department of Clinical Physiology/Clinical Neurophysiology, Sultan Qaboos University Hospital, Muscat, Oman
| | - Khidir M Abdelbasit
- Department of Mathematics and Statistics, College of Science, Sultan Qaboos University, Muscat, Oman
| | | | - Rajesh Poothrikovil
- Department of Clinical Physiology/Clinical Neurophysiology, Sultan Qaboos University Hospital, Muscat, Oman
| | - Amal Khalfan Al-Rawahi
- Department of Clinical Physiology/Clinical Neurophysiology, Sultan Qaboos University Hospital, Muscat, Oman
| | - Abdul Aleem Khan
- Department of Physiology, College of Medicine, Sultan Qaboos University, Muscat, Oman
| | - Robert Shane Delamont
- Department of Neurology, King's Neuroscience Centre, King's College Hospital, London, UK
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Lin P, Yang Y, Gao J, De Pisapia N, Ge S, Wang X, Zuo CS, Jonathan Levitt J, Niu C. Dynamic Default Mode Network across Different Brain States. Sci Rep 2017; 7:46088. [PMID: 28382944 PMCID: PMC5382672 DOI: 10.1038/srep46088] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 03/08/2017] [Indexed: 01/06/2023] Open
Abstract
The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states.
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Affiliation(s)
- Pan Lin
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, China
- Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- Center for Mind/Brain Sciences, University of Trento, Mattarello, 38100, Italy
- Key Laboratory of Child Development and Leaning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Yong Yang
- School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330013, China
| | - Junfeng Gao
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, China
| | - Nicola De Pisapia
- Center for Mind/Brain Sciences, University of Trento, Mattarello, 38100, Italy
| | - Sheng Ge
- Key Laboratory of Child Development and Leaning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Xiang Wang
- Medical Psychological Institute of Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Chun S. Zuo
- Brain Imaging Center, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, 02478, USA
| | - James Jonathan Levitt
- Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, VA, Boston Healthcare System, Brockton Division, and Harvard Medical School, Boston, MA 02301, USA
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Chen Niu
- Department of Medical Imaging, First Affiliated Hospital of Xi’an Jiaotong University College of Medicine, Shaanxi Xi’an 710061, China
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Kang J, Pae C, Park HJ. Energy landscape analysis of the subcortical brain network unravels system properties beneath resting state dynamics. Neuroimage 2017; 149:153-164. [DOI: 10.1016/j.neuroimage.2017.01.075] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 01/23/2017] [Accepted: 01/31/2017] [Indexed: 12/30/2022] Open
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Watanabe T, Masuda N, Megumi F, Kanai R, Rees G. Energy landscape and dynamics of brain activity during human bistable perception. Nat Commun 2014; 5:4765. [PMID: 25163855 PMCID: PMC4174295 DOI: 10.1038/ncomms5765] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 07/21/2014] [Indexed: 11/08/2022] Open
Abstract
Individual differences in the structure of parietal and prefrontal cortex predict the stability of bistable visual perception. However, the mechanisms linking such individual differences in brain structures to behaviour remain elusive. Here we demonstrate a systematic relationship between the dynamics of brain activity, cortical structure and behaviour underpinning bistable perception. Using fMRI in humans, we find that the activity dynamics during bistable perception are well described as fluctuating between three spatially distributed energy minimums: visual-area-dominant, frontal-area-dominant and intermediate states. Transitions between these energy minimums predicted behaviour, with participants whose brain activity tend to reflect the visual-area-dominant state exhibiting more stable perception and those whose activity transits to frontal-area-dominant states reporting more frequent perceptual switches. Critically, these brain activity dynamics are correlated with individual differences in grey matter volume of the corresponding brain areas. Thus, individual differences in the large-scale dynamics of brain activity link focal brain structure with bistable perception.
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Affiliation(s)
- Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AR, UK
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Woodland Road, Clifton, Bristol BS8 1UB, UK
| | - Fukuda Megumi
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AR, UK
| | - Ryota Kanai
- Centre for Consciousness Science, School of Psychology, University of Sussex, Pevensey 1, Brighton BN1 9QH, UK
| | - Geraint Rees
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AR, UK
- Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK
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