Literature DB >> 29024197

Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns.

Carol Di Perri1,2, Enrico Amico1,3,4, Lizette Heine1, Jitka Annen1, Charlotte Martial1, Stephen Karl Larroque1, Andrea Soddu5, Daniele Marinazzo3, Steven Laureys1.   

Abstract

INTRODUCTION: Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness.
METHODS: Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames.
RESULTS: Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames
CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018.
© 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  co-activation patterns; dynamic network interactions; pathological hyperconnectivity; unresponsive wakefulness syndrome

Mesh:

Substances:

Year:  2017        PMID: 29024197      PMCID: PMC6866397          DOI: 10.1002/hbm.23826

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  51 in total

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Authors:  Steven Laureys
Journal:  Trends Cogn Sci       Date:  2005-11-03       Impact factor: 20.229

3.  Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis.

Authors:  Peter Fransson
Journal:  Hum Brain Mapp       Date:  2005-09       Impact factor: 5.038

4.  Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome.

Authors:  Steven Laureys; Gastone G Celesia; Francois Cohadon; Jan Lavrijsen; José León-Carrión; Walter G Sannita; Leon Sazbon; Erich Schmutzhard; Klaus R von Wild; Adam Zeman; Giuliano Dolce
Journal:  BMC Med       Date:  2010-11-01       Impact factor: 8.775

5.  Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

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10.  Posterior cingulate cortex-related co-activation patterns: a resting state FMRI study in propofol-induced loss of consciousness.

Authors:  Enrico Amico; Francisco Gomez; Carol Di Perri; Audrey Vanhaudenhuyse; Damien Lesenfants; Pierre Boveroux; Vincent Bonhomme; Jean-François Brichant; Daniele Marinazzo; Steven Laureys
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6.  Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study.

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7.  Consciousness-specific dynamic interactions of brain integration and functional diversity.

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8.  Resting-State Co-activation Patterns as Promising Candidates for Prediction of Alzheimer's Disease in Aged Mice.

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