Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness

Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciou...

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Published inHuman brain mapping Vol. 33; no. 4; pp. 778 - 796
Main Authors Soddu, Andrea, Vanhaudenhuyse, Audrey, Bahri, Mohamed Ali, Bruno, Marie-Aurelie, Boly, Mélanie, Demertzi, Athena, Tshibanda, Jean-Flory, Phillips, Christophe, Stanziano, Mario, Ovadia-Caro, Smadar, Nir, Yuval, Maquet, Pierre, Papa, Michele, Malach, Rafael, Laureys, Steven, Noirhomme, Quentin
Format Journal Article Web Resource
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2012
Wiley-Liss
John Wiley & Sons, Inc
Wiley Liss, Inc
Subjects
Adolescent
Adult
Aged
Aged, 80 and over
Biological and medical sciences
Brain - physiopathology
Brain Mapping - methods
Child
consciousness
Consciousness Disorders - physiopathology
default mode
Female
fMRI
Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy
Humans
Image Interpretation, Computer-Assisted - methods
independent component analysis
Infant
Investigative techniques, diagnostic techniques (general aspects)
locked-in syndrome
Magnetic Resonance Imaging - methods
Male
Medical sciences
Middle Aged
Nerve Net - physiopathology
Nervous system
Nervous system (semeiology, syndromes)
Neurology
Physical, chemical, mathematical & earth Sciences
Physics
Physique
Physique, chimie, mathématiques & sciences de la terre
Radiodiagnosis. Nmr imagery. Nmr spectrometry
resting state
vegetative state
Young Adult
Coma
Human
default mode
Nervous system diseases
Radiodiagnosis
consciousness
Locked-in syndrome
Consciousness impairment
Spatial analysis
Vegetative state
Nuclear magnetic resonance imaging
fMRI
independent component analysis
resting state
Neurological disorder
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Abstract Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis‐based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation‐corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain‐damaged patients [locked‐in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. Principal observations: All vegetative patients showed fewer connections in the default‐mode areas, when compared with controls, contrary to locked‐in patients who showed near‐normal connectivity. In the minimally conscious‐state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assessing resting‐state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non‐neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
AbstractList Abstract Objectives: Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis‐based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation‐corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain‐damaged patients [locked‐in syndrome ( n = 2), minimally conscious ( n = 1), and vegetative state ( n = 8)]. Principal observations: All vegetative patients showed fewer connections in the default‐mode areas, when compared with controls, contrary to locked‐in patients who showed near‐normal connectivity. In the minimally conscious‐state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assessing resting‐state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non‐neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
Objectives: Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis‐based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation‐corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain‐damaged patients [locked‐in syndrome ( n = 2), minimally conscious ( n = 1), and vegetative state ( n = 8)]. Principal observations: All vegetative patients showed fewer connections in the default‐mode areas, when compared with controls, contrary to locked‐in patients who showed near‐normal connectivity. In the minimally conscious‐state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assessing resting‐state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non‐neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.
Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. Principal observations: All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT]
OBJECTIVESRecent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. EXPERIMENTAL DESIGNA spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. PRINCIPAL OBSERVATIONSAll vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. CONCLUSIONSWhen assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.
Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis‐based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation‐corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain‐damaged patients [locked‐in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. Principal observations: All vegetative patients showed fewer connections in the default‐mode areas, when compared with controls, contrary to locked‐in patients who showed near‐normal connectivity. In the minimally conscious‐state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assessing resting‐state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non‐neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma. Experimental design: A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)]. Principal observations: All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin. Conclusions: When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients. Hum Brain Mapp, 2011. (c) 2011 Wiley-Liss, Inc.
Author Ovadia-Caro, Smadar
Malach, Rafael
Maquet, Pierre
Phillips, Christophe
Boly, Mélanie
Bahri, Mohamed Ali
Noirhomme, Quentin
Stanziano, Mario
Bruno, Marie-Aurelie
Soddu, Andrea
Laureys, Steven
Demertzi, Athena
Tshibanda, Jean-Flory
Vanhaudenhuyse, Audrey
Nir, Yuval
Papa, Michele
AuthorAffiliation 1 Coma Science Group, Cyclotron Research Centre, University of Liège, Liège, Belgium
7 Department of Psychiatry, University of Wisconsin, Madison, Wisconsin
6 Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
3 Neurology Department, CHU Sart Tilman Hospital, University of Liège, Liège, Belgium
4 Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
5 Medicina Pubblica Clinica e Preventiva, Second University of Naples, Naples, Italy
2 Cyclotron Research Centre, University of Liège, Liège, Belgium
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– name: 6 Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
– name: 4 Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
– name: 5 Medicina Pubblica Clinica e Preventiva, Second University of Naples, Naples, Italy
– name: 3 Neurology Department, CHU Sart Tilman Hospital, University of Liège, Liège, Belgium
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  organization: Cyclotron Research Centre, University of Liège, Liège, Belgium
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  surname: Stanziano
  fullname: Stanziano, Mario
  organization: Medicina Pubblica Clinica e Preventiva, Second University of Naples, Naples, Italy
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https://www.ncbi.nlm.nih.gov/pubmed/21484953$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
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Contributor GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging & Memory - ULiège
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Issue 4
Keywords Coma
Human
default mode
Nervous system diseases
Radiodiagnosis
consciousness
Locked-in syndrome
Consciousness impairment
Spatial analysis
Vegetative state
Nuclear magnetic resonance imaging
fMRI
independent component analysis
resting state
Neurological disorder
Language English
License CC BY 4.0
Copyright © 2011 Wiley Periodicals, Inc.
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scopus-id:2-s2.0-84858292861
OpenAccessLink https://europepmc.org/articles/pmc6870518?pdf=render
PMID 21484953
PQID 1517355972
PQPubID 996345
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PublicationDate April 2012
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PublicationTitle Human brain mapping
PublicationTitleAlternate Hum. Brain Mapp
PublicationYear 2012
Publisher Wiley Subscription Services, Inc., A Wiley Company
Wiley-Liss
John Wiley & Sons, Inc
Wiley Liss, Inc
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Snippet Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state...
Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state...
Abstract Objectives: Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by...
Objectives:Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state...
OBJECTIVESRecent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state...
Objectives: Recent fMRI studies have shown that it is possible to reliably identify the default‐mode network (DMN) in the absence of any task, by resting‐state...
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SubjectTerms Adolescent
Adult
Aged
Aged, 80 and over
Biological and medical sciences
Brain - physiopathology
Brain Mapping - methods
Child
consciousness
Consciousness Disorders - physiopathology
default mode
Female
fMRI
Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy
Humans
Image Interpretation, Computer-Assisted - methods
independent component analysis
Infant
Investigative techniques, diagnostic techniques (general aspects)
locked-in syndrome
Magnetic Resonance Imaging - methods
Male
Medical sciences
Middle Aged
Nerve Net - physiopathology
Nervous system
Nervous system (semeiology, syndromes)
Neurology
Physical, chemical, mathematical & earth Sciences
Physics
Physique
Physique, chimie, mathématiques & sciences de la terre
Radiodiagnosis. Nmr imagery. Nmr spectrometry
resting state
vegetative state
Young Adult
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Title Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness
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https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhbm.21249
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Volume 33
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