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Mapping the functional connectome traits of levels of consciousness

(2017) NEUROIMAGE. 148. p.201-211
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Abstract
Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connfCA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connlCA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a "resting" human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.
Keywords
brain connectivity, independent component analysis, consciousness, TRAUMATIC BRAIN-INJURY, HUMAN CEREBRAL-CORTEX, COMA RECOVERY SCALE, RESTING-STATE, NETWORK CONNECTIVITY, DEFAULT NETWORK, DISORDERS, RECOMMENDATIONS, SYSTEM

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Citation

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MLA
Amico, Enrico, et al. “Mapping the Functional Connectome Traits of Levels of Consciousness.” NEUROIMAGE, vol. 148, 2017, pp. 201–11.
APA
Amico, E., Marinazzo, D., Di Perri, C., Heine, L., Annen, J., Martial, C., … Goñi, J. (2017). Mapping the functional connectome traits of levels of consciousness. NEUROIMAGE, 148, 201–211.
Chicago author-date
Amico, Enrico, Daniele Marinazzo, Carol Di Perri, Lizette Heine, Jitka Annen, Charlotte Martial, Mario Dzemidzic, et al. 2017. “Mapping the Functional Connectome Traits of Levels of Consciousness.” NEUROIMAGE 148: 201–11.
Chicago author-date (all authors)
Amico, Enrico, Daniele Marinazzo, Carol Di Perri, Lizette Heine, Jitka Annen, Charlotte Martial, Mario Dzemidzic, Murielle Kirsch, Vincent Bonhomme, Steven Laureys, and Joaquín Goñi. 2017. “Mapping the Functional Connectome Traits of Levels of Consciousness.” NEUROIMAGE 148: 201–211.
Vancouver
1.
Amico E, Marinazzo D, Di Perri C, Heine L, Annen J, Martial C, et al. Mapping the functional connectome traits of levels of consciousness. NEUROIMAGE. 2017;148:201–11.
IEEE
[1]
E. Amico et al., “Mapping the functional connectome traits of levels of consciousness,” NEUROIMAGE, vol. 148, pp. 201–211, 2017.
@article{8503205,
  abstract     = {Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care.

We present a novel data-driven methodology, connfCA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups.

We here apply connlCA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a "resting" human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings.

Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.},
  author       = {Amico, Enrico and Marinazzo, Daniele and Di Perri, Carol and Heine, Lizette and Annen, Jitka and Martial, Charlotte and Dzemidzic, Mario and Kirsch, Murielle and Bonhomme, Vincent and Laureys, Steven and Goñi, Joaquín},
  issn         = {1053-8119},
  journal      = {NEUROIMAGE},
  keywords     = {brain connectivity,independent component analysis,consciousness,TRAUMATIC BRAIN-INJURY,HUMAN CEREBRAL-CORTEX,COMA RECOVERY SCALE,RESTING-STATE,NETWORK CONNECTIVITY,DEFAULT NETWORK,DISORDERS,RECOMMENDATIONS,SYSTEM},
  language     = {eng},
  pages        = {201--211},
  title        = {Mapping the functional connectome traits of levels of consciousness},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2017.01.020},
  volume       = {148},
  year         = {2017},
}

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