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Wang J, Lai Q, Han J, Qin P, Wu H. Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness. Brain Res 2024; 1843:149133. [PMID: 39084451 DOI: 10.1016/j.brainres.2024.149133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/29/2024] [Accepted: 07/25/2024] [Indexed: 08/02/2024]
Abstract
The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.
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Affiliation(s)
- Jiaying Wang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qiantu Lai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China; Pazhou Lab, Guangzhou 510330, China.
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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2
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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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3
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Mashour GA. Anesthesia and the neurobiology of consciousness. Neuron 2024; 112:1553-1567. [PMID: 38579714 PMCID: PMC11098701 DOI: 10.1016/j.neuron.2024.03.002] [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: 02/02/2024] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 04/07/2024]
Abstract
In the 19th century, the discovery of general anesthesia revolutionized medical care. In the 21st century, anesthetics have become indispensable tools to study consciousness. Here, I review key aspects of the relationship between anesthesia and the neurobiology of consciousness, including interfaces of sleep and anesthetic mechanisms, anesthesia and primary sensory processing, the effects of anesthetics on large-scale functional brain networks, and mechanisms of arousal from anesthesia. I discuss the implications of the data derived from the anesthetized state for the science of consciousness and then conclude with outstanding questions, reflections, and future directions.
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Affiliation(s)
- George A Mashour
- Center for Consciousness Science, Department of Anesthesiology, Department of Pharmacology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
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4
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Yang H, Wu H, Kong L, Luo W, Xie Q, Pan J, Quan W, Hu L, Li D, Wu X, Liang H, Qin P. Precise detection of awareness in disorders of consciousness using deep learning framework. Neuroimage 2024; 290:120580. [PMID: 38508294 DOI: 10.1016/j.neuroimage.2024.120580] [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: 01/19/2024] [Revised: 03/14/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024] Open
Abstract
Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.
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Affiliation(s)
- Huan Yang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Wen Luo
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 528199, China
| | - Qiuyou Xie
- Joint Research Center for disorders of consciousness, Department of Rehabilitation, Zhujiang Hospital, School of Rehabilitation Sciences, Southern Medical University, Guangzhou 510220, China
| | - Jiahui Pan
- School of Software, South China Normal University, Foshan 528225, China; Pazhou Lab, Guangzhou 510330, China
| | - Wuxiu Quan
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Lianting Hu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Dantong Li
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Xuehai Wu
- Pazhou Lab, Guangzhou 510330, China; Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Neurosurgical Institute of Fudan University, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Huiying Liang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China.
| | - Pengmin Qin
- Pazhou Lab, Guangzhou 510330, China; 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.
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5
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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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6
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Luppi AI, Hansen JY, Adapa R, Carhart-Harris RL, Roseman L, Timmermann C, Golkowski D, Ranft A, Ilg R, Jordan D, Bonhomme V, Vanhaudenhuyse A, Demertzi A, Jaquet O, Bahri MA, Alnagger NL, Cardone P, Peattie AR, Manktelow AE, de Araujo DB, Sensi SL, Owen AM, Naci L, Menon DK, Misic B, Stamatakis EA. In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape. SCIENCE ADVANCES 2023; 9:eadf8332. [PMID: 37315149 PMCID: PMC10266734 DOI: 10.1126/sciadv.adf8332] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/10/2023] [Indexed: 06/16/2023]
Abstract
To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs' effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain's functional architecture.
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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
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Justine Y. Hansen
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Robin L. Carhart-Harris
- Psychedelics Division - Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Leor Roseman
- Center for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Christopher Timmermann
- Center for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Daniel Golkowski
- Department of Neurology, Klinikum rechts der Isar, Technical University Munich, München, Germany
| | - Andreas Ranft
- School of Medicine, Department of Anesthesiology and Intensive Care, Technical University of Munich, Munich, Germany
| | - Rüdiger Ilg
- Department of Neurology, Klinikum rechts der Isar, Technical University Munich, München, Germany
- Department of Neurology, Asklepios Clinic, Bad Tölz, Germany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der Isar, Technical University Munich, München, Germany
- University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Vincent Bonhomme
- Department of Anesthesia and Intensive Care Medicine, Liege University Hospital, Liege, Belgium
- Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness Thematic Unit, GIGA-Research, Liege University, Liege, Belgium
| | - Audrey Vanhaudenhuyse
- Department of Anesthesia and Intensive Care Medicine, Liege University Hospital, Liege, Belgium
| | - Athena Demertzi
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liege, Liege, Belgium
| | - Oceane Jaquet
- Department of Anesthesia and Intensive Care Medicine, Liege University Hospital, Liege, Belgium
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liege, Liege, Belgium
| | - Naji L. N. Alnagger
- Department of Anesthesia and Intensive Care Medicine, Liege University Hospital, Liege, Belgium
| | - Paolo Cardone
- Department of Anesthesia and Intensive Care Medicine, Liege University Hospital, Liege, Belgium
| | - Alexander R. D. Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | | | - Stefano L. Sensi
- Department of Neuroscience and Imaging and Clinical Science, Center for Advanced Studies and Technology, Institute for Advanced Biomedical Technologies, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy
- Institute for Memory Impairments and Neurological Disorders, University of California-Irvine, Irvine, CA, USA
| | - Adrian M. Owen
- Department of Psychology and Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K. Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Wolfon Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Bratislav Misic
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Emmanuel A. Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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7
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Wu H, Xie Q, Pan J, Liang Q, Lan Y, Guo Y, Han J, Xie M, Liu Y, Jiang L, Wu X, Li Y, Qin P. Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability. Neuroimage 2023; 272:120050. [PMID: 36963740 DOI: 10.1016/j.neuroimage.2023.120050] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/04/2023] [Accepted: 03/21/2023] [Indexed: 03/26/2023] Open
Abstract
Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.
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Affiliation(s)
- Hang Wu
- 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, Guangdong, 510631, China
| | - Qiuyou Xie
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China; Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Jiahui Pan
- School of Software, South China Normal University, Foshan, 528225, China; Pazhou Lab, Guangzhou, 510330, China
| | - Qimei Liang
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yue Lan
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yequn Guo
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, 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, 510631 Guangzhou, 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, Guangdong, 510631, China
| | - Yueyao 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, Guangdong, 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, Guangdong, 510631, China
| | - Xuehai Wu
- Pazhou Lab, Guangzhou, 510330, China; Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200433, China.
| | - Yuanqing Li
- Pazhou Lab, Guangzhou, 510330, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, 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, Guangdong, 510631, China; Pazhou Lab, Guangzhou, 510330, China.
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8
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Luppi AI, Vohryzek J, Kringelbach ML, Mediano PAM, Craig MM, Adapa R, Carhart-Harris RL, Roseman L, Pappas I, Peattie ARD, Manktelow AE, Sahakian BJ, Finoia P, Williams GB, Allanson J, Pickard JD, Menon DK, Atasoy S, Stamatakis EA. Distributed harmonic patterns of structure-function dependence orchestrate human consciousness. Commun Biol 2023; 6:117. [PMID: 36709401 PMCID: PMC9884288 DOI: 10.1038/s42003-023-04474-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023] Open
Abstract
A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Here we decompose functional MRI signals from pathological and pharmacologically-induced perturbations of consciousness into distributed patterns of structure-function dependence across scales: the harmonic modes of the human structural connectome. We show that structure-function coupling is a generalisable indicator of consciousness that is under bi-directional neuromodulatory control. We find increased structure-function coupling across scales during loss of consciousness, whether due to anaesthesia or brain injury, capable of discriminating between behaviourally indistinguishable sub-categories of brain-injured patients, tracking the presence of covert consciousness. The opposite harmonic signature characterises the altered state induced by LSD or ketamine, reflecting psychedelic-induced decoupling of brain function from structure and correlating with physiological and subjective scores. Overall, connectome harmonic decomposition reveals how neuromodulation and the network architecture of the human connectome jointly shape consciousness and distributed functional activation across scales.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, CB2 1SB, UK.
| | - Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08005, Spain
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
- Department of Computing, Imperial College London, London, W12 0NN, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
- Psychedelics Division - Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Leor Roseman
- Center for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
| | - Ioannis Pappas
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Anne E Manktelow
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Barbara J Sahakian
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Psychiatry, MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
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9
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Huang Z, Mashour GA, Hudetz AG. Functional geometry of the cortex encodes dimensions of consciousness. Nat Commun 2023; 14:72. [PMID: 36604428 PMCID: PMC9814511 DOI: 10.1038/s41467-022-35764-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
Consciousness is a multidimensional phenomenon, but key dimensions such as awareness and wakefulness have been described conceptually rather than neurobiologically. We hypothesize that dimensions of consciousness are encoded in multiple neurofunctional dimensions of the brain. We analyze cortical gradients, which are continua of the brain's overarching functional geometry, to characterize these neurofunctional dimensions. We demonstrate that disruptions of human consciousness - due to pharmacological, neuropathological, or psychiatric causes - are associated with a degradation of one or more of the major cortical gradients depending on the state. Network-specific reconfigurations within the multidimensional cortical gradient space are associated with behavioral unresponsiveness of various etiologies, and these spatial reconfigurations correlate with a temporal disruption of structured transitions of dynamic brain states. In this work, we therefore provide a unifying neurofunctional framework for multiple dimensions of human consciousness in both health and disease.
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Affiliation(s)
- Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. .,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
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10
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Lor CS, Zhang M, Karner A, Steyrl D, Sladky R, Scharnowski F, Haugg A. Pre- and post-task resting-state differs in clinical populations. Neuroimage Clin 2023; 37:103345. [PMID: 36780835 PMCID: PMC9925974 DOI: 10.1016/j.nicl.2023.103345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/30/2022] [Accepted: 02/05/2023] [Indexed: 02/09/2023]
Abstract
Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.
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Affiliation(s)
- Cindy Sumaly Lor
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Lenggstrasse 31, 8032 Zürich, Switzerland.
| | - Mengfan Zhang
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Lenggstrasse 31, 8032 Zürich, Switzerland
| | - Alexander Karner
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Lenggstrasse 31, 8032 Zürich, Switzerland
| | - David Steyrl
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Lenggstrasse 31, 8032 Zürich, Switzerland
| | - Ronald Sladky
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria
| | - Frank Scharnowski
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Lenggstrasse 31, 8032 Zürich, Switzerland
| | - Amelie Haugg
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, Neumünsterallee 9, 8032 Zürich, Switzerland
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11
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What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. PLoS Comput Biol 2022; 18:e1010412. [PMID: 36067227 PMCID: PMC9481177 DOI: 10.1371/journal.pcbi.1010412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 09/16/2022] [Accepted: 07/18/2022] [Indexed: 11/19/2022] Open
Abstract
The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or ‘information structure’), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision. Brain states emerge through continuously evolving dynamics of brain networks. The usual way of modelling these dynamics is by using stationary systems: there is one structure (attractor) which is responsible of the brain dynamics. We adopt a different approach by characterising the brain activity through a landscape of informational structures (IS) changing in time. We use a model transformation procedure to produce these structures and look at several properties related to how the different brain networks interact not in the observed resting-state fMRI signal but in the information structure underlying it. These properties provide measures strongly related with relevant characteristics of conscious activity, such as metastability, information integration or synchronisation. The distribution of IS measures is studied for healthy controls (HC) and two groups of post-comatose patients with disorders of consciousness (DOC): minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). Based on IS measures, machine learners classifiers identify the state of consciousness with an outstanding discrimination (precision of 95.6% por HC/DOC and 86.6% for MCS/UWS).
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12
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Fu C, Zhang Y, Ye Y, Hou X, Wen Z, Yan Z, Luo W, Feng M, Liu B. Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis. Front Neurosci 2022; 16:937453. [PMID: 35992927 PMCID: PMC9388938 DOI: 10.3389/fnins.2022.937453] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMigraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment.MethodsA total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group.ResultsA biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p < 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = −0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = −0.684, p < 0.001), respectively.ConclusionsBy machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus.
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Affiliation(s)
- Chengwei Fu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yue Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yongsong Ye
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaoyan Hou
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zeying Wen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Zhaoxian Yan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wenting Luo
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Menghan Feng
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Bo Liu
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13
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Affiliation(s)
- George A. Mashour
- From the Departments of Anesthesiology and Pharmacology, Center for Consciousness Science, Michigan Neuroscience Institute, Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan
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14
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Al Zoubi O, Misaki M, Tsuchiyagaito A, Zotev V, White E, Paulus M, Bodurka J. Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets. Brain Connect 2022; 12:348-361. [PMID: 34269609 PMCID: PMC9131354 DOI: 10.1089/brain.2020.0878] [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: 11/12/2022] Open
Abstract
Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Department of Psychiatry, Harvard Medical School/McLean Hospital, Boston, Massachusetts, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Evan White
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
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15
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness. Commun Biol 2022; 5:384. [PMID: 35444252 PMCID: PMC9021270 DOI: 10.1038/s42003-022-03330-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 03/30/2022] [Indexed: 12/02/2022] Open
Abstract
The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
- Data Science Institute, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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16
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17
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Geng B, Gao M, Piao R, Liu C, Xu K, Zhang S, Zeng X, Liu P, Wang Y. Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine. Front Psychiatry 2022; 13:906404. [PMID: 35958632 PMCID: PMC9357875 DOI: 10.3389/fpsyt.2022.906404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE This study aimed to develop an effective support vector machine (SVM) classifier based on the multi-modal data for detecting the main brain networks involved in group separation of premature ejaculation (PE). METHODS A total of fifty-two patients with lifelong PE and 36 matched healthy controls were enrolled in this study. Structural MRI data, functional MRI data, and diffusion tensor imaging (DTI) data were used to process SPM12, DPABI4.5, and PANDA, respectively. A total of 12,735 features were reduced by the Mann-Whitney U test. The resilience nets method was further used to select features. RESULTS Finally, 36 features (3 structural MRI, 7 functional MRI, and 26 DTI) were chosen in the training dataset. We got the best SVM model with an accuracy of 97.5% and an area under the curve (AUC) of 0.986 in the training dataset as well as an accuracy of 91.4% and an AUC of 0.966 in the testing dataset. CONCLUSION Our findings showed that the majority of the brain abnormalities for the classification was located within or across several networks. This study may contribute to the neural mechanisms of PE and provide new insights into the pathophysiology of patients with lifelong PE.
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Affiliation(s)
- Bowen Geng
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Ming Gao
- Department of Urology, Xi'An Daxing Hospital Affiliated to Yan'an University, Xi'an, China
| | - Ruiqing Piao
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Chengxiang Liu
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Ke Xu
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Shuming Zhang
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Xiao Zeng
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Peng Liu
- Life Science Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Yanzhu Wang
- Department of Urology, Xi'An Daxing Hospital Affiliated to Yan'an University, Xi'an, China
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18
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Huang Z, Tarnal V, Vlisides PE, Janke EL, McKinney AM, Picton P, Mashour GA, Hudetz AG. Asymmetric neural dynamics characterize loss and recovery of consciousness. Neuroimage 2021; 236:118042. [PMID: 33848623 PMCID: PMC8310457 DOI: 10.1016/j.neuroimage.2021.118042] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/01/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
Anesthetics are known to disrupt neural interactions in cortical and subcortical brain circuits. While the effect of anesthetic drugs on consciousness is reversible, the neural mechanism mediating induction and recovery may be different. Insight into these distinct mechanisms can be gained from a systematic comparison of neural dynamics during slow induction of and emergence from anesthesia. To this end, we used functional magnetic resonance imaging (fMRI) data obtained in healthy volunteers before, during, and after the administration of propofol at incrementally adjusted target concentrations. We analyzed functional connectivity of corticocortical and subcorticocortical networks and the temporal autocorrelation of fMRI signal as an index of neural processing timescales. We found that en route to unconsciousness, temporal autocorrelation across the entire brain gradually increased, whereas functional connectivity gradually decreased. In contrast, regaining consciousness was associated with an abrupt restoration of cortical but not subcortical temporal autocorrelation and an abrupt boost of subcorticocortical functional connectivity. Pharmacokinetic effects could not account for the difference in neural dynamics between induction and emergence. We conclude that the induction and recovery phases of anesthesia follow asymmetric neural dynamics. A rapid increase in the speed of cortical neural processing and subcorticocortical neural interactions may be a mechanism that reboots consciousness.
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Affiliation(s)
- Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Vijay Tarnal
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Phillip E Vlisides
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Ellen L Janke
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Amy M McKinney
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Paul Picton
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA.
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19
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Sanz Perl Y, Pallavicini C, Pérez Ipiña I, Demertzi A, Bonhomme V, Martial C, Panda R, Annen J, Ibañez A, Kringelbach M, Deco G, Laufs H, Sitt J, Laureys S, Tagliazucchi E. Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness. PLoS Comput Biol 2021; 17:e1009139. [PMID: 34314430 PMCID: PMC8315553 DOI: 10.1371/journal.pcbi.1009139] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 06/01/2021] [Indexed: 01/07/2023] Open
Abstract
Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.
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Affiliation(s)
- Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires, Intendente Güiraldes 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
- * E-mail: (YSP); (ET)
| | - Carla Pallavicini
- Department of Physics, University of Buenos Aires, Intendente Güiraldes 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
| | - Ignacio Pérez Ipiña
- Department of Physics, University of Buenos Aires, Intendente Güiraldes 2160—Ciudad Universitaria—Buenos Aires, Argentina
- Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI), Buenos Aires, Argentina
| | - Athena Demertzi
- Physiology of Cognition Research Lab, GIGA-Consciousness, 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 Cerveau², Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau², Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau², 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, California, United States and Trinity College Dublin, Ireland
| | - Morten Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- 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, United Kingdom
| | - 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 Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
- Department of Neurology, Christian Albrechts University, Kiel, Germany
| | - Jacobo Sitt
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
- Inserm U 1127, Paris, France
- CNRS UMR 7225, Paris, France
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau², Centre Hospitalier Universitaire de Liège (CHU Liège), Liège, Belgium
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Intendente Güiraldes 2160—Ciudad Universitaria—Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Department of Neurology, Christian Albrechts University, Kiel, Germany
- * E-mail: (YSP); (ET)
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20
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Luppi AI, Golkowski D, Ranft A, Ilg R, Jordan D, Menon DK, Stamatakis EA. Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane. Hum Brain Mapp 2021; 42:2802-2822. [PMID: 33738899 PMCID: PMC8127159 DOI: 10.1002/hbm.25405] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/10/2021] [Accepted: 02/27/2021] [Indexed: 12/22/2022] Open
Abstract
The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.
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Affiliation(s)
- Andrea I. Luppi
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Daniel Golkowski
- Department of Neurology, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - Andreas Ranft
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - Rüdiger Ilg
- Department of Neurology, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
- Department of NeurologyAsklepios ClinicBad TölzGermany
| | - Denis Jordan
- Department of Anaesthesiology and Intensive Care Medicine, Klinikum rechts der IsarTechnische Universität MünchenMünchenGermany
| | - David K. Menon
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Wolfon Brain Imaging CentreUniversity of CambridgeCambridgeUK
| | - Emmanuel A. Stamatakis
- Division of AnaesthesiaUniversity of CambridgeCambridgeUK
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
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21
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Tang TB, Chong JS, Kiguchi M, Funane T, Lu CK. Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity. IEEE Trans Neural Syst Rehabil Eng 2021; 29:894-904. [PMID: 33970862 DOI: 10.1109/tnsre.2021.3078460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.
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22
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Cao B, Guo Y, Guo Y, Xie Q, Chen L, Huang H, Yu R, Huang R. Time-delay structure predicts clinical scores for patients with disorders of consciousness using resting-state fMRI. NEUROIMAGE: CLINICAL 2021; 32:102797. [DOI: 10.1016/j.nicl.2021.102797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 08/08/2021] [Accepted: 08/17/2021] [Indexed: 01/22/2023] Open
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23
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Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human brain research. PLoS One 2020; 15:e0231192. [PMID: 32251489 PMCID: PMC7135272 DOI: 10.1371/journal.pone.0231192] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/18/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.
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Affiliation(s)
- Xieling Chen
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
| | - Juan Chen
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science and the School of Psychology, South China Normal University, Guangzhou, China
| | - Gary Cheng
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
- * E-mail: (GC); (TG)
| | - Tao Gong
- Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou, China
- Educational Testing Service, Princeton, NJ, United States of America
- * E-mail: (GC); (TG)
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