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Wang H, Chen J, Yuan Z, Huang Y, Lin F. NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis. Med Image Anal 2024; 97:103290. [PMID: 39094462 DOI: 10.1016/j.media.2024.103290] [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: 05/07/2023] [Revised: 02/08/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.
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Affiliation(s)
- Houxiang Wang
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China
| | - Jiaqing Chen
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China.
| | - Zihao Yuan
- School of Science, Wuhan University of Technology, Wuhan Hubei, 430071, China
| | - Yangxin Huang
- School of Public Health, University of South Florida, Tampa, FL 33612, USA
| | - Fuchun Lin
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
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2
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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3
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Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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4
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Wu Y, Yao J, Xu XM, Zhou LL, Salvi R, Ding S, Gao X. Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study. Front Neurosci 2024; 18:1402039. [PMID: 38933814 PMCID: PMC11201293 DOI: 10.3389/fnins.2024.1402039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Purpose Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs). Methods We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model. Results SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity. Conclusion Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
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Affiliation(s)
- Yuanqing Wu
- Department of Otorhinolaryngology Head and Neck Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Department of Otorhinolaryngology Head and Neck Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jun Yao
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao-Min Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Lei-Lei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Richard Salvi
- Center for Hearing and Deafness, University at Buffalo, The State University of New York, Buffalo, NY, United States
| | - Shaohua Ding
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, China
| | - Xia Gao
- Department of Otorhinolaryngology Head and Neck Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
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5
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Kharitonova EV, Sun Q, Ockerman F, Chen B, Zhou LY, Cao H, Mathias RA, Auer PL, Ober C, Raffield LM, Reiner AP, Cox NJ, Kelada S, Tao R, Li Y. EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.23.24307839. [PMID: 38826253 PMCID: PMC11142285 DOI: 10.1101/2024.05.23.24307839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.
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Affiliation(s)
- Elena V. Kharitonova
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Frank Ockerman
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brian Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura Y. Zhou
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Hongyuan Cao
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Rasika A. Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Paul L. Auer
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98105, USA
| | - Nancy J. Cox
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Samir Kelada
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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6
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [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: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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7
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Tononi G, Boly M, Cirelli C. Consciousness and sleep. Neuron 2024; 112:1568-1594. [PMID: 38697113 PMCID: PMC11105109 DOI: 10.1016/j.neuron.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 05/04/2024]
Abstract
Sleep is a universal, essential biological process. It is also an invaluable window on consciousness. It tells us that consciousness can be lost but also that it can be regained, in all its richness, when we are disconnected from the environment and unable to reflect. By considering the neurophysiological differences between dreaming and dreamless sleep, we can learn about the substrate of consciousness and understand why it vanishes. We also learn that the ongoing state of the substrate of consciousness determines the way each experience feels regardless of how it is triggered-endogenously or exogenously. Dreaming consciousness is also a window on sleep and its functions. Dreams tell us that the sleeping brain is remarkably lively, recombining intrinsic activation patterns from a vast repertoire, freed from the requirements of ongoing behavior and cognitive control.
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Affiliation(s)
- Giulio Tononi
- Department of Psychiatry, University of Wisconsin, Madison, WI 53719, USA.
| | - Melanie Boly
- Department of Neurology, University of Wisconsin, Madison, WI 53719, USA
| | - Chiara Cirelli
- Department of Psychiatry, University of Wisconsin, Madison, WI 53719, USA
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8
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Yang FN, Picchioni D, de Zwart JA, Wang Y, van Gelderen P, Duyn JH. Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.24.24306208. [PMID: 38903093 PMCID: PMC11188122 DOI: 10.1101/2024.04.24.24306208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16-hour fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.
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Affiliation(s)
- Fan Nils Yang
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Dante Picchioni
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Jacco A. de Zwart
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Yicun Wang
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Peter van Gelderen
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Jeff H. Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
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9
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Joliot M, Cremona S, Tzourio C, Etard O. Modulate the impact of the drowsiness on the resting state functional connectivity. Sci Rep 2024; 14:8652. [PMID: 38622265 PMCID: PMC11018752 DOI: 10.1038/s41598-024-59476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.
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Affiliation(s)
- Marc Joliot
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
| | - Sandrine Cremona
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France
| | | | - Olivier Etard
- Normandie Université, UNICAEN, INSERM, COMETE U1075, CYCERON, CHU Caen, 14000, Caen, France
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10
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Lu L, Li F, Li H, Zhou L, Wu X, Yuan F. Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models. CNS Neurosci Ther 2024; 30:e14660. [PMID: 38439697 PMCID: PMC10912843 DOI: 10.1111/cns.14660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 03/06/2024] Open
Abstract
OBJECTIVES This study aimed to investigate the temporal dynamics of brain activity and characterize the spatiotemporal specificity of transitions and large-scale networks on short timescales in acute mild traumatic brain injury (mTBI) patients and those with cognitive impairment in detail. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 71 acute mTBI patients and 57 age-, sex-, and education-matched healthy controls (HCs). A hidden Markov model (HMM) analysis of rs-fMRI data was conducted to identify brain states that recurred over time and to assess the dynamic patterns of activation states that characterized acute mTBI patients and those with cognitive impairment. The dynamic parameters (fractional occupancy, lifetime, interval time, switching rate, and probability) between groups and their correlation with cognitive performance were analyzed. RESULTS Twelve HMM states were identified in this study. Compared with HCs, acute mTBI patients and those with cognitive impairment exhibited distinct changes in dynamics, including fractional occupancy, lifetime, and interval time. Furthermore, the switching rate and probability across HMM states were significantly different between acute mTBI patients and patients with cognitive impairment (all p < 0.05). The temporal reconfiguration of states in acute mTBI patients and those with cognitive impairment was associated with several brain networks (including the high-order cognition network [DMN], subcortical network [SUB], and sensory and motor network [SMN]). CONCLUSIONS Hidden Markov models provide additional information on the dynamic activity of brain networks in patients with acute mTBI and those with cognitive impairment. Our results suggest that brain network dynamics determined by the HMM could reinforce the understanding of the neuropathological mechanisms of acute mTBI patients and those with cognitive impairment.
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Affiliation(s)
- Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Fengfang Li
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hui Li
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Leilei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xinying Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Fang Yuan
- Department of Neurosurgery, Shanghai Jiao Tong University Affiliated Sixth Peoples' Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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11
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Gohil C, Huang R, Roberts E, van Es MWJ, Quinn AJ, Vidaurre D, Woolrich MW. osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife 2024; 12:RP91949. [PMID: 38285016 PMCID: PMC10945565 DOI: 10.7554/elife.91949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024] Open
Abstract
Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Rukuang Huang
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Mats WJ van Es
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
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12
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Han J, Xie Q, Wu X, Huang Z, Tanabe S, Fogel S, Hudetz AG, Wu H, Northoff G, Mao Y, He S, Qin P. The neural correlates of arousal: Ventral posterolateral nucleus-global transient co-activation. Cell Rep 2024; 43:113633. [PMID: 38159279 DOI: 10.1016/j.celrep.2023.113633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 11/21/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
Arousal and awareness are two components of consciousness whose neural mechanisms remain unclear. Spontaneous peaks of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal have been found to be sensitive to changes in arousal. By contrasting BOLD signals at different arousal levels, we find decreased activation of the ventral posterolateral nucleus (VPL) during transient peaks in the global signal in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) compared to wakefulness and in eyes-closed compared to eyes-open conditions in healthy awake individuals. Intriguingly, VPL-global co-activation remains high in patients with unresponsive wakefulness syndrome (UWS), who exhibit high arousal without awareness, while it reduces in rapid eye movement sleep, a state characterized by low arousal but high awareness. Furthermore, lower co-activation is found in individuals during N3 sleep compared to patients with UWS. These results demonstrate that co-activation of VPL and global activity is critical to arousal but not to awareness.
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Affiliation(s)
- Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Qiuyou Xie
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong, China; Joint Research Centre for Disorders of Consciousness, Guangzhou, Guangdong, China
| | - Xuehai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zirui Huang
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - Sean Tanabe
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Anthony G Hudetz
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - Hang Wu
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Sheng He
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Pengmin Qin
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, Guangdong, China; Pazhou Lab, Guangzhou 510335, China.
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13
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Torabi M, Mitsis GD, Poline JB. On the variability of dynamic functional connectivity assessment methods. Gigascience 2024; 13:giae009. [PMID: 38587470 PMCID: PMC11000510 DOI: 10.1093/gigascience/giae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/05/2023] [Accepted: 02/15/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods. METHODS We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity. RESULTS Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages. CONCLUSIONS Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.
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Affiliation(s)
- Mohammad Torabi
- Graduate Program in Biological and Biomedical Engineering, McGill University, Duff Medical Building, 3775 rue University, Montreal H3A 2B4, Canada
- Department of Bioengineering, McGill University, 3480 University Street, Montreal H3A 0E9, Canada
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal H3A 2B4, Canada
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, 3480 University Street, Montreal H3A 0E9, Canada
| | - Jean-Baptiste Poline
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, 3801 University Street, Montreal H3A 2B4, Canada
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14
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Idesis S, Geli S, Faskowitz J, Vohryzek J, Sanz Perl Y, Pieper F, Galindo-Leon E, Engel AK, Deco G. Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex. PLoS Comput Biol 2024; 20:e1011818. [PMID: 38241383 PMCID: PMC10836715 DOI: 10.1371/journal.pcbi.1011818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/02/2024] [Accepted: 01/09/2024] [Indexed: 01/21/2024] Open
Abstract
Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.
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Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
| | - Sebastián Geli
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Florian Pieper
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Edgar Galindo-Leon
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
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15
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Xu Z, Tang S, Liu C, Zhang Q, Gu H, Li X, Di Z, Li Z. Temporal segmentation of EEG based on functional connectivity network structure. Sci Rep 2023; 13:22566. [PMID: 38114604 PMCID: PMC10730570 DOI: 10.1038/s41598-023-49891-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.
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Affiliation(s)
- Zhongming Xu
- The International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China
- The School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Shaohua Tang
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China
- The School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Chuancai Liu
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qiankun Zhang
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Heng Gu
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Xiaoli Li
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Zengru Di
- The International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
| | - Zheng Li
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China.
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16
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Deco G, Lynn CW, Sanz Perl Y, Kringelbach ML. Violations of the fluctuation-dissipation theorem reveal distinct nonequilibrium dynamics of brain states. Phys Rev E 2023; 108:064410. [PMID: 38243472 DOI: 10.1103/physreve.108.064410] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/16/2023] [Indexed: 01/21/2024]
Abstract
The brain is a nonequilibrium system whose dynamics change in different brain states, such as wakefulness and deep sleep. Thermodynamics provides the tools for revealing these nonequilibrium dynamics. We used violations of the fluctuation-dissipation theorem to describe the hierarchy of nonequilibrium dynamics associated with different brain states. Together with a whole-brain model fitted to empirical human neuroimaging data, and deriving the appropriate analytical expressions, we were able to capture the deviation from equilibrium in different brain states that arises from asymmetric interactions and hierarchical organization.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, 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
| | - Christopher W Lynn
- Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, New York 10016, USA and Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Department of Physics, University of Buenos Aires, Buenos Aires 1428, Argentina and Paris Brain Institute (ICM), Paris 75013, France
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, United Kingdom; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom; and Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark
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17
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Yates TS, Ellis CT, Turk-Browne NB. Functional networks in the infant brain during sleep and wake states. Cereb Cortex 2023; 33:10820-10835. [PMID: 37718160 DOI: 10.1093/cercor/bhad327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/19/2023] Open
Abstract
Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.
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Affiliation(s)
- Tristan S Yates
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Cameron T Ellis
- Department of Psychology, Stanford University, Stanford, CA, United States
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
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18
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Pan H, Mao Y, Liu P, Li Y, Wei G, Qiao X, Ren Y, Zhao F. Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis. Med Phys 2023; 50:6269-6282. [PMID: 36995984 DOI: 10.1002/mp.16406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases. PURPOSE To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients. METHODS We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis. RESULTS (1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum. CONCLUSIONS Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.
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Affiliation(s)
- Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yuan Li
- School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China
| | - Guanglan Wei
- Information Network Center, Shandong Second Provincial General Hospital, Jinan, China
| | - Xiaoyan Qiao
- School of Mathematics and Information Science, Shandong Technology and Business University, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
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19
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Khazaei M, Raeisi K, Vanhatalo S, Zappasodi F, Comani S, Tokariev A. Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury. Neuroimage 2023; 279:120342. [PMID: 37619792 DOI: 10.1016/j.neuroimage.2023.120342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 08/11/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.
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Affiliation(s)
- Mohammad Khazaei
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy.
| | - Khadijeh Raeisi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy
| | - Sampsa Vanhatalo
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Filippo Zappasodi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Institute for Advanced Biomedical Technologies, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Anton Tokariev
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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20
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Tozzi A. Laws of taxation for multicellular organisms: The economics of sleep. Biosystems 2023; 232:105018. [PMID: 37666410 DOI: 10.1016/j.biosystems.2023.105018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
In macro-public finance, the Ramsey rule (RR) concerns variable taxation to maximize social welfare and economic efficiency in a purely competitive monopolistic system. To extract tax revenue with the least loss of utility to the representative individual, RR dictates that optimal, proportionate taxes should be such as to diminish in the same proportion the production of each commodity taxed. The sources of supply that are inelastic, i.e., necessities/utilities, must be taxed more. We hypothesize that the Ramsey's economical approach might provide a general mechanism to investigate far-flung biological issues, such as preys/predators dynamics, food restriction in ecological niches, local changes in blood flow in rival or complementary organs of multicellular organisms. In particular, RR suggests a quantifiable relationship between the physiological decrease in cortical spike frequency occurring during sleep and energy consumption. Since small decreases in spike frequency during sleep are correlated with large decreases in the amount of consumed ATP, the brain could be considered an inelastic commodity which can be "taxed" more than other organs, allowing the whole organism to spare energy. Shedding light on the energy budget of the central nervous system, RR improves our knowledge of cerebral perfusion during sensory-evoked responses and tissue hypoxia caused by decreased blood flow, suggesting that energy from outside can be provided to counteract brain ischemia. In sum, the economical approach provided by Ramsey stands for a useful methodological tool that could be used in biological contexts to investigate the dynamical correlations among different organs in multicellular organisms.
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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.
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21
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Hussain S, Menchaca I, Shalchy MA, Yaghoubi K, Langley J, Seitz AR, Hu XP, Peters MAK. Locus coeruleus integrity predicts ease of attaining and maintaining neural states of high attentiveness. Brain Res Bull 2023; 202:110733. [PMID: 37586427 DOI: 10.1016/j.brainresbull.2023.110733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 07/31/2023] [Accepted: 08/11/2023] [Indexed: 08/18/2023]
Abstract
The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brain's principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinson's, Alzheimer's). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood - partly because LC's small size makes it difficult to study noninvasively in humans. Here, we characterized LC's influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm and measured individual differences in LC magnetization transfer contrast. The model revealed five hidden states, including a stable state dominated by salience-network activity that occurred when subjects actively engaged with the task. LC magnetization transfer contrast correlated with this state's stability across experimental manipulations and with subjects' propensity to enter into and remain in this state. These results provide new insight into LC's role in driving spatiotemporal neural patterns associated with attention, and demonstrate that variation in LC integrity can explain individual differences in these patterns even in healthy young adults.
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Affiliation(s)
- Sana Hussain
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Isaac Menchaca
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | | | - Kimia Yaghoubi
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Jason Langley
- Center for Advanced Neuroimaging, University of California, Riverside, CA, USA
| | - Aaron R Seitz
- Department of Psychology, University of California Riverside, Riverside, CA, USA; Department of Psychology, Northeastern University, Boston, MA, USA
| | - Xiaoping P Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA; Center for Advanced Neuroimaging, University of California, Riverside, CA, USA.
| | - Megan A K Peters
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA; Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA; Program in Brain, Mind, & Consciousness, Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
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22
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Abstract
Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this technology at scale in the routine workflow of sleep centers. The field, however, is complex and presents unsolved challenges in a number of areas. Recent developments in computer science and artificial intelligence are nevertheless closing the gap. Technological advances are also opening new pathways for expanding our current understanding of the domain and its analysis.
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Affiliation(s)
- Diego Alvarez-Estevez
- Center for Information and Communications Technology Research (CITIC), Universidade da Coruña, 15071 A Coruña, Spain.
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23
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Wu Y, Li P, Bhat N, Fan H, Liu M. Effects of repeated sleep deprivation on brain pericytes in mice. Sci Rep 2023; 13:12760. [PMID: 37550395 PMCID: PMC10406921 DOI: 10.1038/s41598-023-40138-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/05/2023] [Indexed: 08/09/2023] Open
Abstract
The damaging effects of sleep deprivation (SD) on brain parenchyma have been extensively studied. However, the specific influence of SD on brain pericytes, a primary component of the blood-brain barrier (BBB) and the neurovascular unit (NVU), is still unclear. The present study examined how acute or repeated SD impairs brain pericytes by measuring the cerebrospinal fluid (CSF) levels of soluble platelet-derived growth factor receptor beta (sPDGFRβ) and quantifying pericyte density in the cortex, hippocampus, and subcortical area of the PDGFRβ-P2A-CreERT2/tdTomato mice, which predominantly express the reporter tdTomato in vascular pericytes. Our results showed that a one-time 4 h SD did not significantly change the CSF sPDGFRβ level. In contrast, repeated SD (4 h/day for 10 consecutive days) significantly elevated the CSF sPDGFRβ level, implying explicit pericyte damages due to repeated SD. Furthermore, repeated SD significantly decreased the pericyte densities in the cortex and hippocampus, though the pericyte apoptosis status remained unchanged as measured with Annexin V-affinity assay and active Caspase-3 staining. These results suggest that repeated SD causes brain pericyte damage and loss via non-apoptosis pathways. These changes to pericytes may contribute to SD-induced BBB and NVU dysfunctions. The reversibility of this process implies that sleep improvement may have a protective effect on brain pericytes.
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Affiliation(s)
- Yan Wu
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Pengfei Li
- Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Narayan Bhat
- Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Hongkuan Fan
- Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Meng Liu
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA.
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24
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Masaracchia L, Fredes F, Woolrich MW, Vidaurre D. Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data. J Neurophysiol 2023; 130:364-379. [PMID: 37403598 PMCID: PMC10625837 DOI: 10.1152/jn.00054.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/06/2023] Open
Abstract
Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. These patterns differ from one another depending on the assumptions of the models. How these assumptions affect specific data decompositions in practice, however, is often unclear, which hinders model applicability and interpretability. For instance, the hidden Markov model (HMM) automatically detects characteristic, recurring activity patterns (so-called states) from time series data. States are defined by a certain probability distribution, whose state-specific parameters are estimated from the data. But what specific features, from all of those that the data contain, do the states capture? That depends on the choice of probability distribution and on other model hyperparameters. Using both synthetic and real data, we aim to better characterize the behavior of two HMM types that can be applied to electrophysiological data. Specifically, we study which differences in data features (such as frequency, amplitude, or signal-to-noise ratio) are more salient to the models and therefore more likely to drive the state decomposition. Overall, we aim at providing guidance for the appropriate use of this type of analysis on one- or two-channel neural electrophysiological data and an informed interpretation of its results given the characteristics of the data and the purpose of the analysis.NEW & NOTEWORTHY Compared with classical supervised methods, unsupervised methods of analysis have the advantage to be freer of subjective biases. However, it is not always clear what aspects of the data these methods are most sensitive to, which complicates interpretation. Focusing on the hidden Markov model, commonly used to describe electrophysiological data, we explore in detail the nature of its estimates through simulations and real data examples, providing important insights about what to expect from these models.
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Affiliation(s)
- Laura Masaracchia
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Felipe Fredes
- Center for Proteins in Memory, Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Mark W Woolrich
- Psychiatry Department, Oxford Centre for Human Brain Activity, Oxford University, Oxford, United Kingdom
| | - Diego Vidaurre
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Psychiatry Department, Oxford Centre for Human Brain Activity, Oxford University, Oxford, United Kingdom
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25
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Javaheripour N, Colic L, Opel N, Li M, Maleki Balajoo S, Chand T, Van der Meer J, Krylova M, Izyurov I, Meller T, Goltermann J, Winter NR, Meinert S, Grotegerd D, Jansen A, Alexander N, Usemann P, Thomas-Odenthal F, Evermann U, Wroblewski A, Brosch K, Stein F, Hahn T, Straube B, Krug A, Nenadić I, Kircher T, Croy I, Dannlowski U, Wagner G, Walter M. Altered brain dynamic in major depressive disorder: state and trait features. Transl Psychiatry 2023; 13:261. [PMID: 37460460 DOI: 10.1038/s41398-023-02540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/20/2023] Open
Abstract
Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
| | - Lejla Colic
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
| | - Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, 52425, Jülich, Germany
| | - Tara Chand
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Department of Clinical Psychology, Friedrich Schiller University Jena, Am Steiger 3-1, 07743, Jena, Germany
| | - Johan Van der Meer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marina Krylova
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Igor Izyurov
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Ilona Croy
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
- Department of Clinical Psychology, Friedrich Schiller University Jena, Am Steiger 3-1, 07743, Jena, Germany
- Department of Psychotherapie and Psychosomatic Medicine, Carl Gustav Carus University Hospital Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany.
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany.
- German Center for Mental Health (DZPG), Jena, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany.
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
- Leibniz Institute for Neurobiology, Magdeburg, Germany.
- Center for Behavioral Brain Sciences, Magdeburg, Germany.
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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26
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Luppi AI, Cabral J, Cofre R, Mediano PAM, Rosas FE, Qureshi AY, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine JM, Kringelbach ML, Orio P, Ching S, Sanz Perl Y, Diringer MN, Stevens RD, Sitt JD. Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness. Neuroimage 2023; 275:120162. [PMID: 37196986 PMCID: PMC10262065 DOI: 10.1016/j.neuroimage.2023.120162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/16/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023] Open
Abstract
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Portugal
| | - Rodrigo Cofre
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile; Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Abid Y Qureshi
- University of Kansas Medical Center, Kansas City, MO, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | - Enzo Tagliazucchi
- Departamento de Física (UBA) e Instituto de Fisica de Buenos Aires (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso and Instituto de Neurociencia, Universidad de Valparaíso, Valparaíso, Chile
| | - ShiNung Ching
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; National Scientific and Technical Research Council (CONICET), Godoy Cruz, CABA 2290, Argentina
| | - Michael N Diringer
- Department of Neurology and Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Robert D Stevens
- Departments of Anesthesiology and Critical Care Medicine, Neurology, and Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jacobo Diego Sitt
- Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France.
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27
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Jiang W, Ding S, Xu C, Ke H, Bo H, Zhao T, Ma L, Li H. Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model. Front Hum Neurosci 2023; 17:1197613. [PMID: 37457501 PMCID: PMC10340116 DOI: 10.3389/fnhum.2023.1197613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD. Methods In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC. Results The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD. Discussion In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.
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Affiliation(s)
- Wenhao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Shihang Ding
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Cong Xu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Huihuang Ke
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Hongjian Bo
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Tiejun Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Lin Ma
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Haifeng Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
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28
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Perl YS, Pallavicini C, Piccinini J, Demertzi A, Bonhomme V, Martial C, Panda R, Alnagger N, Annen J, Gosseries O, Ibañez A, Laufs H, Sitt JD, Jirsa VK, Kringelbach ML, Laureys S, Deco G, Tagliazucchi E. Low-dimensional organization of global brain states of reduced consciousness. Cell Rep 2023; 42:112491. [PMID: 37171963 PMCID: PMC11220841 DOI: 10.1016/j.celrep.2023.112491] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/19/2023] [Accepted: 04/24/2023] [Indexed: 05/14/2023] Open
Abstract
Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unclear. We combine whole-brain modeling, data augmentation, and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to patients with brain injury is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, increasing alongside loss of consciousness. Finally, we investigate the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.
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Affiliation(s)
- Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Paris Brain Institute (ICM), Paris, France.
| | - Carla Pallavicini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI), Buenos Aires, Argentina
| | - Juan Piccinini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
| | - Athena Demertzi
- Physiology of Cognition Research Lab, GIGA CRC-In Vivo Imaging Center, GIGA Institute, University of Liège, Liège, Belgium
| | - Vincent Bonhomme
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liège, Liège, Belgium; University Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Régional de la Citadelle (CHR Citadelle), Liège, Belgium; Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Naji Alnagger
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Agustin Ibañez
- National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California-San Francisco (UCSF), San Francisco, CA, USA; Trinity College, Dublin, Ireland
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany; Department of Neurology, Christian Albrechts University, Kiel, Germany
| | - Jacobo D Sitt
- Paris Brain Institute (ICM), Paris, France; INSERM U 1127, Paris, France; CNRS UMR 7225, Paris, France
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Århus, Denmark; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 (Ciudad Universitaria), Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina; Centre du Cerveau(2), Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium.
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Huang Y, Xie M, Liu Y, Zhang X, Jiang L, Bao H, Qin P, Han J. Brain State Relays Self-Processing and Heartbeat-Evoked Cortical Responses. Brain Sci 2023; 13:brainsci13050832. [PMID: 37239303 DOI: 10.3390/brainsci13050832] [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: 04/20/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
The self has been proposed to be grounded in interoceptive processing, with heartbeat-evoked cortical activity as a neurophysiological marker of this processing. However, inconsistent findings have been reported on the relationship between heartbeat-evoked cortical responses and self-processing (including exteroceptive- and mental-self-processing). In this review, we examine previous research on the association between self-processing and heartbeat-evoked cortical responses and highlight the divergent temporal-spatial characteristics and brain regions involved. We propose that the brain state relays the interaction between self-processing and heartbeat-evoked cortical responses and thus accounts for the inconsistency. The brain state, spontaneous brain activity which highly and continuously changes in a nonrandom way, serves as the foundation upon which the brain functions and was proposed as a point in an extremely high-dimensional space. To elucidate our assumption, we provide reviews on the interactions between dimensions of brain state with both self-processing and heartbeat-evoked cortical responses. These interactions suggest the relay of self-processing and heartbeat-evoked cortical responses by brain state. Finally, we discuss possible approaches to investigate whether and how the brain state impacts the self-heart interaction.
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Affiliation(s)
- Ying Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Yunhe Liu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Xinyu Zhang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Liubei Jiang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Han Bao
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
- Pazhou Lab, Guangzhou 510330, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education China, Institute for Brain Research and Rehabilitation and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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Vidaurre D. Dynamic functional connectivity: why the controversy? Neuroimage 2023:120169. [PMID: 37196985 DOI: 10.1016/j.neuroimage.2023.120169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/19/2023] Open
Abstract
In principle, dynamic functional connectivity in fMRI is just a statistical measure. A passer-by might think it to be a specialist topic, but it continues to attract widespread attention and spark controversy. Why?
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Affiliation(s)
- Diego Vidaurre
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, 8000 (Denmark); Department of Psychiatry, Oxford University, OX3 7JX (UK).
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31
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Hussain S, Langley J, Seitz AR, Hu XP, Peters MA. A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging. Brain Connect 2023; 13:154-163. [PMID: 36367193 PMCID: PMC10079241 DOI: 10.1089/brain.2022.0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.
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Affiliation(s)
- Sana Hussain
- Department of Bioengineering, University of California, Riverside, Riverside, California, USA
| | - Jason Langley
- Center for Advanced Neuroimaging, University of California, Riverside, Riverside, California, USA
| | - Aaron R. Seitz
- Department of Psychology, University of California, Riverside, Riverside, California, USA
| | - Xiaoping P. Hu
- Department of Bioengineering, University of California, Riverside, Riverside, California, USA
- Center for Advanced Neuroimaging, University of California, Riverside, Riverside, California, USA
| | - Megan A.K. Peters
- Department of Bioengineering, University of California, Riverside, Riverside, California, USA
- Department of Cognitive Sciences, University of California, Irvine, Irvine, California, USA
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32
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Chen X, Jin X, Zhang J, Ho KW, Wei Y, Cheng H. Validation of a wearable forehead sleep recorder against polysomnography in sleep staging and desaturation events in a clinical sample. J Clin Sleep Med 2023; 19:711-718. [PMID: 36689310 PMCID: PMC10071378 DOI: 10.5664/jcsm.10416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 01/24/2023]
Abstract
STUDY OBJECTIVES Wearable sleep recording devices may be a helpful alternative method for polysomnography (PSG) due to their higher accessibility and comfort as well as lower cost, but their validities need to be examined. The aim of this study was to evaluate the accuracy of a novel single-channel, electroencephalography-based wearable forehead sleep recorder (UMindSleep) to assess sleep staging and oxygen desaturation. METHODS Two hundred and three Chinese adults recruited from a sleep medicine center underwent an overnight study wearing UMindSleep and PSG simultaneously. Sleep parameters including sleep staging and oxygen desaturation index were compared between UMindSleep and PSG. RESULTS A total of 195,349 valid epochs from 197 participants (171 with obstructive sleep apnea, 86.8%) were included in analyses of sleep staging. Sensitivities of UMindSleep compared to PSG were 79.7% for wake, 85.8% for light sleep, 79.4% for deep sleep, and 82.7% for rapid eye movement sleep. Specificities were 95.3% for wake, 83.4% for light sleep, 97.0% for deep sleep, and 96.8% for rapid eye movement sleep. Furthermore, the kappa agreements of 0.69-0.79 were indicative of a substantial agreement for sleep staging between UMindSleep and PSG. Sensitivity and specificity regarding oxygen desaturation index were 93.4% and 88.9%, yielding a kappa coefficient of 0.82. CONCLUSIONS Our findings suggest that UMindSleep may serve as a feasible, accurate, and dependable device for screening of sleep disorders (eg, obstructive sleep apnea) and assessing sleep structure. CITATION Chen X, Jin X, Zhang J, Ho KW, Wei Y, Cheng H. Validation of a wearable forehead sleep recorder against polysomnography in sleep staging and desaturation events in a clinical sample. J Clin Sleep Med. 2023;19(4):711-718.
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Affiliation(s)
- Xinru Chen
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xinyi Jin
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jihui Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Kwok Wah Ho
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yongli Wei
- Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Hanrong Cheng
- Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China
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Yu Y, Qiu Y, Li G, Zhang K, Bo B, Pei M, Ye J, Thompson GJ, Cang J, Fang F, Feng Y, Duan X, Tong C, Liang Z. Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice. Nat Commun 2023; 14:1651. [PMID: 36964161 PMCID: PMC10039056 DOI: 10.1038/s41467-023-37352-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
Sleep is ubiquitous and essential, but its mechanisms remain unclear. Studies in animals and humans have provided insights of sleep at vastly different spatiotemporal scales. However, challenges remain to integrate local and global information of sleep. Therefore, we developed sleep fMRI based on simultaneous electrophysiology at 9.4 T in male mice. Optimized un-anesthetized mouse fMRI setup allowed manifestation of NREM and REM sleep, and a large sleep fMRI dataset was collected and openly accessible. State dependent global patterns were revealed, and state transitions were found to be global, asymmetrical and sequential, which can be predicted up to 17.8 s using LSTM models. Importantly, sleep fMRI with hippocampal recording revealed potentiated sharp-wave ripple triggered global patterns during NREM than awake state, potentially attributable to co-occurrence of spindle events. To conclude, we established mouse sleep fMRI with simultaneous electrophysiology, and demonstrated its capability by revealing global dynamics of state transitions and neural events.
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Affiliation(s)
- Yalin Yu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yue Qiu
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gen Li
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China
| | - Kaiwei Zhang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jingjing Ye
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | | | - Jing Cang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Fang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
- The Central Hospital of Xuhui District, Shanghai, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xiaojie Duan
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China.
| | - Chuanjun Tong
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
| | - Zhifeng Liang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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Chen Q, Chen F, Zhu Y, Long C, Lu J, Zhang X, Nedelska Z, Hort J, Chen J, Ma G, Zhang B. Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline. Neurobiol Aging 2023; 127:82-93. [PMID: 37116409 DOI: 10.1016/j.neurobiolaging.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/12/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.
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35
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Zhang S, Goodale SE, Gold BP, Morgan VL, Englot DJ, Chang C. Vigilance associates with the low-dimensional structure of fMRI data. Neuroimage 2023; 267:119818. [PMID: 36535323 PMCID: PMC10074161 DOI: 10.1016/j.neuroimage.2022.119818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA.
| | - Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin P Gold
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dario J Englot
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Catie Chang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
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Cabral J, Fernandes FF, Shemesh N. Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI. Nat Commun 2023; 14:375. [PMID: 36746938 PMCID: PMC9902553 DOI: 10.1038/s41467-023-36025-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/12/2023] [Indexed: 02/08/2023] Open
Abstract
Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in particular the link with locally-detected ultra-slow oscillations, are not fully understood. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from female rats across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with the addition of isoflurane. Peaking in power within clear anatomical boundaries, these oscillatory modes point to an emergent systemic property. This work provides additional insight into the origin of oscillations detected in fMRI and the organizing principles underpinning spontaneous long-range functional connectivity.
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Affiliation(s)
- Joana Cabral
- Preclinical MRI Lab, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal. .,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal. .,ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal.
| | - Francisca F Fernandes
- Preclinical MRI Lab, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Noam Shemesh
- Preclinical MRI Lab, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
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Kavi PC. Conscious entry into sleep: Yoga Nidra and accessing subtler states of consciousness. PROGRESS IN BRAIN RESEARCH 2023; 280:43-60. [PMID: 37714572 DOI: 10.1016/bs.pbr.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Human sleep is a dynamic and complex process comprising sleep stages with REM and NREM sleep characteristics that come in cycles. During sleep, there is a loss of responsiveness or a perceptual loss of conscious awareness with increasing thresholds for wakefulness as sleep progresses. There are brief bursts of wakefulness or Wake After Sleep Onset (WASO) throughout a nocturnal sleep. Conscious experience during nocturnal sleep is known to occur during lucid dreaming when one is aware during dreams when the dream is occurring. Most cultures have known lucid dreaming since antiquity. However, conscious experience during dreamless sleep is relatively lesser known. Nevertheless, selected Indo-Tibetan meditation literature has documented it since antiquity. Minimal Phenomenal Experience (MPE) research describes lucid dreamless sleep as its target phenomenology. "Conscious entry into sleep" posits tonic alertness is maintained post sleep onset through the sleep stages for sustained durations of time until an eventual loss of conscious awareness. Entering sleep consciously and being aware during dreamless sleep, including Slow Wave Activity, is plausibly to be in the state of "Yoga Nidra" or Yogic sleep. An attentive sleepful state provides access to subtler states of consciousness and significantly deepens the levels of silence. It is phenomenologically distinct from hypnagogic hallucinations and lucid dreaming. Unfortunately, sleep studies validating this phenomenology are yet to be done. Therefore, an experimental methodology akin to those used in lucid dreaming experiments is described.
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38
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Steffens SK, Stenberg TH, Wigren HKM. Alterations in microglial morphology concentrate in the habitual sleeping period of the mouse. Glia 2023; 71:366-376. [PMID: 36196985 PMCID: PMC10092278 DOI: 10.1002/glia.24279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 09/10/2022] [Accepted: 09/20/2022] [Indexed: 11/05/2022]
Abstract
In nocturnal animals, waking appears during the dark period while maximal non-rapid-eye-movement sleep (NREMS) with electroencephalographic slow-wave-activity (SWA) takes place at the beginning of the light period. Vigilance states associate with variable levels of neuronal activity: waking with high-frequency activity patterns while during NREMS, SWA influences neuronal activity in many brain areas. On a glial level, sleep deprivation modifies microglial morphology, but only few studies have investigated microglia through the physiological sleep-wake cycle. To quantify microglial morphology (territory, volume, ramification) throughout the 24 h light-dark cycle, we collected brain samples from inbred C57BL male mice (n = 51) every 3 h and applied a 3D-reconstruction method for microglial cells on the acquired confocal microscopy images. As microglia express regional heterogeneity and are influenced by local neuronal activity, we chose to investigate three interconnected and functionally well-characterized brain areas: the somatosensory cortex (SC), the dorsal hippocampus (HC), and the basal forebrain (BF). To temporally associate microglial morphology with vigilance stages, we performed a 24 h polysomnography in a separate group of animals (n = 6). In line with previous findings, microglia displayed de-ramification in the 12 h light- and hyper-ramification in the 12 h dark period. Notably, we found that the decrease in microglial features was most prominent within the early hours of the light period, co-occurring with maximal sleep SWA. By the end of the light period, all features reached maximum levels and remained steadily elevated throughout the dark period with minor regional differences. We propose that vigilance-stage specific neuronal activity, and SWA, could modify microglial morphology.
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Affiliation(s)
| | - Tarja Helena Stenberg
- SleepWell Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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Xu F, Zhao J, Liu M, Yu X, Wang C, Lou Y, Shi W, Liu Y, Gao L, Yang Q, Zhang B, Lu S, Tang J, Leng J. Exploration of sleep function connection and classification strategies based on sub-period sleep stages. Front Neurosci 2023; 16:1088116. [PMID: 36760796 PMCID: PMC9906994 DOI: 10.3389/fnins.2022.1088116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/30/2022] [Indexed: 01/26/2023] Open
Abstract
Background As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.
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Affiliation(s)
- Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China,*Correspondence: Fangzhou Xu,
| | - Jinzhao Zhao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yitai Lou
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Weiyou Shi
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yanbing Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Licai Gao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Qingbo Yang
- School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Baokun Zhang
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Shanshan Lu
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China,Department of Neurology, Cheeloo College of Medicine, Shandong Qianfoshan Hospital, Shandong University, Jinan, Shandong, China,Shanshan Lu,
| | - Jiyou Tang
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China,Department of Neurology, Cheeloo College of Medicine, Shandong Qianfoshan Hospital, Shandong University, Jinan, Shandong, China,Jiyou Tang,
| | - Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China,Jiancai Leng,
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40
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Cheng X, Hu F, Yang B, Wang F, Olofsson T. Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study. INDOOR AIR 2022; 32:e13175. [PMID: 36567523 DOI: 10.1111/ina.13175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/18/2022] [Accepted: 10/24/2022] [Indexed: 06/17/2023]
Abstract
Thermal comfort during sleep is essential for both sleep quality and human health while sleeping. There are currently few effective contactless methods for detecting the sleep thermal comfort at any time of day or night. In this paper, a vision-based detection approach for human thermal comfort while sleeping was proposed, which is intended to avoid overcooling/overheating supply, meet the thermal comfort needs of human sleep, and improve human sleep quality and health. Based on 438 valid questionnaire surveys, 10 types of thermal comfort sleep postures were summarized. By using a large number of data captured, a fundamental framework of detection algorithm was constructed to detect human sleeping postures, and corresponding weighting model was established. A total of 2.65 million frames of posture data in natural sleep status were collected, and thermal comfort-related sleep postures dataset was created. Finally, the robustness and effectiveness of the proposed algorithm were validated. The validation results show that the sleeping posture and human skeleton keypoints can be used for estimating sleeping thermal comfort, and the the quilt coverage area can be fused to improve the detection accuracy.
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Affiliation(s)
- Xiaogang Cheng
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
- Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
| | - Fei Hu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Bin Yang
- Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
- School of Energy and Safety Engineering, Tianjing Chengjian University, Tianjin, China
| | - Faming Wang
- Department of Biosystems (BIOSYST), KU Leuven, Leuven, Belgium
| | - Thomas Olofsson
- Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
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41
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Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling. Comput Struct Biotechnol J 2022; 21:335-345. [PMID: 36582443 PMCID: PMC9792354 DOI: 10.1016/j.csbj.2022.11.060] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.
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Key Words
- Brain State
- Brain stimulation
- Deep Brain Stimulation, DBS
- Magnetic Resonance Imaging, MRI
- Non-Invasive Brain Stimulations, NIBS
- Position Emission Tomography, PET
- Probability Metastable Substates, PMS
- Spatio-temporal dynamics
- Transcranial Magnetic Stimulation, TMS
- Transition Probability Matrix, TPM
- Whole-brain models
- diffusion Magnetic Resonance Imaging, dMRI
- dynamic Functional Connectivity, dFC
- functional Magnetic Resonance Imaging, fMRI
- static Functional Connectivity, sFC
- transcranial Alternating Current Stimulation, tACS
- transcranial Direct Stimulation, tDCS
- transcranial Electric Stimulation, tES
- transcranial Random Noise Stimulation, tRNS
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42
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Hao S, Wang Z, Alexander AD, Yuan J, Zhang W. MICOS: Mixed supervised contrastive learning for multivariate time series classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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43
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Zhang J, Zhuang K, Sun J, Liu C, Fan L, Wang X, Gu J, Qiu J. Retrieval flexibility links to creativity: evidence from computational linguistic measure. Cereb Cortex 2022; 33:4964-4976. [PMID: 36218835 DOI: 10.1093/cercor/bhac392] [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: 05/19/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 11/13/2022] Open
Abstract
Creativity, the ability to generate original and valuable products, has long been linked to semantic retrieval processes. The associative theory of creativity posits flexible retrieval ability as an important basis for creative idea generation. However, there is insufficient research on how flexible memory retrieval acts on creative activities. This study aimed to capture different dynamic aspects of retrieval processes and examine the behavioral and neural associations between retrieval flexibility and creativity. We developed 5 metrics to quantify retrieval flexibility based on previous studies, which confirmed the important role of creativity. Our findings showed that retrieval flexibility was positively correlated with multiple creativity-related behavior constructs and can promote distinct search patterns in different creative groups. Moreover, high flexibility was associated with the lifetime of a specific brain state during rest, characterized by interactions among large-scale cognitive brain systems. The flexible functional connectivity within and between default mode, executive control, and salience provides further evidence on brain dynamics of creativity. Retrieval flexibility mediated the links between the lifetime of the related brain state and creativity. This new approach is expected to enhance our knowledge of the role of retrieval flexibility in creativity from a dynamic perspective.
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Affiliation(s)
- Jingyi Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jiangzhou Sun
- College of International Studies, Southwest University, Chongqing 400715, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Li Fan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jing Gu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715 , China
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44
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Olsen AS, Høegh RMT, Hinrich JL, Madsen KH, Mørup M. Combining electro- and magnetoencephalography data using directional archetypal analysis. Front Neurosci 2022; 16:911034. [PMID: 35968377 PMCID: PMC9374169 DOI: 10.3389/fnins.2022.911034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/11/2022] [Indexed: 11/20/2022] Open
Abstract
Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.
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Affiliation(s)
- Anders S. Olsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Rasmus M. T. Høegh
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- WS Audiology, Lynge, Denmark
| | - Jesper L. Hinrich
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Kristoffer H. Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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45
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Singh MF, Cole MW, Braver TS, Ching S. Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement. ANNUAL REVIEWS IN CONTROL 2022; 54:363-376. [PMID: 38250171 PMCID: PMC10798814 DOI: 10.1016/j.arcontrol.2022.05.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.
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Affiliation(s)
- Matthew F Singh
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, 63130, MO, USA
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 07102, NJ, USA
- Psychological and Brain Science, Washington University in St. Louis, St. Louis, 63130, MO, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 07102, NJ, USA
| | - Todd S Braver
- Psychological and Brain Science, Washington University in St. Louis, St. Louis, 63130, MO, USA
| | - ShiNung Ching
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, 63130, MO, USA
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46
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Huijben IAM, Nijdam AA, Hermans LWA, Overeem S, Van Gilst MM, Van Sloun RJG. Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2945-2948. [PMID: 36086087 DOI: 10.1109/embc48229.2022.9871236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Nowadays, high amounts of data can be acquired in various applications, spurring the need for interpretable data representations that provide actionable insights. Algorithms that yield such representations ideally require as little a priori knowledge about the data or corresponding annotations as possible. To this end, we here investigate the use of Kohonen's Self-Organizing Map (SOM) in combination with data-driven low-dimensional embeddings obtained through self-supervised Contrastive Predictive Coding. We compare our approach to embeddings found with an auto-encoder and, moreover, investigate three ways to deal with node selection during SOM optimization. As a challenging experiment we analyze nocturnal sleep recordings of healthy subjects, and conclude that - for this noisy real-life data - contrastive learning yields a better low-dimensional embedding for the purpose of SOM training, compared to an auto-encoder. In addition, we show that a stochastic temperature-annealed SOM-training outperforms both a deterministic and a non-temperature-annealed stochastic approach. Clinical relevance - The hypnogram has for decades been the clinical standard in sleep medicine despite the fact that it is a highly simplified representation of a polysomnography recording. We propose a sensor-agnostic algorithm that is able to reveal more intricate patterns in sleep recordings which might teach us about sleep structure and sleep disorders.
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47
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Tan C, Liu X, Zhang G. Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM. Neuroinformatics 2022; 20:737-753. [PMID: 35244856 DOI: 10.1007/s12021-022-09568-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2022] [Indexed: 12/31/2022]
Abstract
The brain functional mechanisms underlying emotional changes have been primarily studied based on the traditional task design with discrete and simple stimuli. However, the brain state transitions when exposed to continuous and naturalistic stimuli with rich affection variations remain poorly understood. This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity. The hidden Markov model (HMM) was used to study how the brain dynamics responds to emotion during long-time movie-viewing activity. The results showed that dHA significantly improved inter-subject consistency and allowed more consistent temporal HMM states across participants. Afterward, grouping the emotions in a clustering dendrogram revealed a hierarchical grouping of the HMM states. Further emotional sensitivity and specificity analyses of ordered states revealed the most significant differences in happiness and sadness. We then compared the activation map in HMM states during happiness and sadness and found significant differences in the whole brain, but strong activation was observed during both in the superior temporal gyrus, which is related to the early process of emotional prosody processing. A comparison of the inter-network functional connections indicates unique functional connections of the memory retrieval and cognitive network with the cerebellum network during happiness. Moreover, the persistent bilateral connections among salience, cognitive, and sensorimotor networks during sadness may reflect the interaction between high-level cognitive networks and low-level sensory networks. The main results were verified by the second session of the dataset. All these findings enrich our understanding of the brain states related to emotional variation during naturalistic stimuli.
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Affiliation(s)
- Chenhao Tan
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, No. 135 Yaguan Road, Haihe Education Park, Tianjin, 300350, People's Republic of China
| | - Xin Liu
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, No. 135 Yaguan Road, Haihe Education Park, Tianjin, 300350, People's Republic of China
| | - Gaoyan Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, No. 135 Yaguan Road, Haihe Education Park, Tianjin, 300350, People's Republic of China.
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48
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Charquero‐Ballester M, Kleim B, Vidaurre D, Ruff C, Stark E, Tuulari JJ, McManners H, Bar‐Haim Y, Bouquillon L, Moseley A, Williams SCR, Woolrich MW, Kringelbach ML, Ehlers A. Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks. Hum Brain Mapp 2022; 43:3207-3220. [PMID: 35393717 PMCID: PMC9188968 DOI: 10.1002/hbm.25846] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 12/03/2022] Open
Abstract
In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+ ) and healthy trauma-exposed controls (only mtDMN+ ). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function.
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Affiliation(s)
| | - Birgit Kleim
- Experimental Psychopathology and Psychotherapy, Department of PsychologyUniversity of ZurichZurichSwitzerland
- Department of Psychiatry, Psychotherapy and PsychosomaticsUniversity of ZurichZurichSwitzerland
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative NeuroImaging, Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
| | - Christian Ruff
- Zurich Center for Neuroeconomics (ZNE), Department of EconomicsUniversity of ZurichZurichSwitzerland
| | - Eloise Stark
- Department of PsychiatryUniversity of OxfordOxfordUK
| | | | | | - Yair Bar‐Haim
- School of Psychological SciencesTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Linda Bouquillon
- Department of Psychology, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Allison Moseley
- Department of Psychology, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & NeurosciencesKing's College LondonLondonUK
| | - Mark W. Woolrich
- Wellcome Trust Centre for Integrative NeuroImaging, Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
| | - Morten L. Kringelbach
- Department of PsychiatryUniversity of OxfordOxfordUK
- Scars of War FoundationThe Queen's CollegeOxfordUK
- Centre for Music in the BrainAarhus UniversityAarhusDenmark
| | - Anke Ehlers
- Oxford Centre for Anxiety Disorders and Trauma, Department of Experimental PsychologyUniversity of OxfordOxfordUK
- Oxford Health NHS Foundation TrustOxfordUK
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49
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Unifying turbulent dynamics framework distinguishes different brain states. Commun Biol 2022; 5:638. [PMID: 35768641 PMCID: PMC9243255 DOI: 10.1038/s42003-022-03576-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 06/10/2022] [Indexed: 12/21/2022] Open
Abstract
Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto’s turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states. A unifying turbulent dynamics framework using both model-free and modelbased measures of whole-brain information provides insights into brain states.
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50
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Decat N, Walter J, Koh ZH, Sribanditmongkol P, Fulcher BD, Windt JM, Andrillon T, Tsuchiya N. Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series. Sleep Med 2022; 98:39-52. [PMID: 35779380 DOI: 10.1016/j.sleep.2022.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 05/23/2022] [Accepted: 06/02/2022] [Indexed: 11/30/2022]
Abstract
The widely used guidelines for sleep staging were developed for the visual inspection of electrophysiological recordings by the human eye. As such, these rules reflect a limited range of features in these data and are therefore restricted in accurately capturing the physiological changes associated with sleep. Here we present a novel analysis framework that extensively characterizes sleep dynamics using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their time-series features, without relying on established scoring conventions. The resulting sleep structure overlapped substantially with that defined by visual scoring. However, we also observed discrepancies between our approach and traditional scoring. This divergence principally stemmed from the extensive characterization by hctsa features, which captured distinctive time-series properties within the traditionally defined sleep stages that are overlooked with visual scoring. Lastly, we report time-series features that are highly discriminative of stages. Our framework lays the groundwork for a data-driven exploration of sleep sub-stages and has significant potential to identify new signatures of sleep disorders and conscious sleep states.
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Affiliation(s)
- Nicolas Decat
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
| | - Jasmine Walter
- Philosophy Department, Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia; Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia
| | - Zhao H Koh
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
| | - Piengkwan Sribanditmongkol
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
| | - Ben D Fulcher
- School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Jennifer M Windt
- Philosophy Department, Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia; Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia
| | - Thomas Andrillon
- Philosophy Department, Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia; Paris Brain Institute, Sorbonne Université, Inserm-CNRS, Paris, 75013, France
| | - Naotsugu Tsuchiya
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia; Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, 565-0871, Japan; Advanced Telecommunications Research Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
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