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Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness.
MedLine Citation:
PMID:  21484953     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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. © 2011 Wiley-Liss, Inc.
Authors:
Andrea Soddu; Audrey Vanhaudenhuyse; Mohamed Ali Bahri; Marie-Aurelie Bruno; Mélanie Boly; Athena Demertzi; Jean-Flory Tshibanda; Christophe Phillips; Mario Stanziano; Smadar Ovadia-Caro; Yuval Nir; Pierre Maquet; Michele Papa; Rafael Malach; Steven Laureys; Quentin Noirhomme
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-4-11
Journal Detail:
Title:  Human brain mapping     Volume:  -     ISSN:  1097-0193     ISO Abbreviation:  -     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-4-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9419065     Medline TA:  Hum Brain Mapp     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Wiley-Liss, Inc.
Affiliation:
Coma Science Group, Cyclotron Research Centre, University of Liège, Liège, Belgium. andrea.soddu@ulg.ac.be.
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