title: Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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)].
  
  
  RESULTS
  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.
authors.affiliation: Coma Science Group, Cyclotron Research Centre, University of Liège, Liège, Belgium. andrea.soddu@ulg.ac.be
authors.forename: Andrea
authors.initials: A
authors.lastname: Soddu
authors.forename: Audrey
authors.initials: A
authors.lastname: Vanhaudenhuyse
authors.forename: Mohamed Ali
authors.initials: MA
authors.lastname: Bahri
authors.forename: Marie-Aurelie
authors.initials: MA
authors.lastname: Bruno
authors.forename: Mélanie
authors.initials: M
authors.lastname: Boly
authors.forename: Athena
authors.initials: A
authors.lastname: Demertzi
authors.forename: Jean-Flory
authors.initials: JF
authors.lastname: Tshibanda
authors.forename: Christophe
authors.initials: C
authors.lastname: Phillips
authors.forename: Mario
authors.initials: M
authors.lastname: Stanziano
authors.forename: Smadar
authors.initials: S
authors.lastname: Ovadia-Caro
authors.forename: Yuval
authors.initials: Y
authors.lastname: Nir
authors.forename: Pierre
authors.initials: P
authors.lastname: Maquet
authors.forename: Michele
authors.initials: M
authors.lastname: Papa
authors.forename: Rafael
authors.initials: R
authors.lastname: Malach
authors.forename: Steven
authors.initials: S
authors.lastname: Laureys
authors.forename: Quentin
authors.initials: Q
authors.lastname: Noirhomme
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D003244:Consciousness Disorders / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000503:physiopathology; D055815:Young Adult