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fMRI scans reveal key signs of consciousness

By 9th February 2019 June 11th, 2019 No Comments

An international team that includes researchers from France is reporting on functional magnetic resonance imaging (fMRI) measurements that can distinguish between the brain activity in conscious or unconscious patients. The technique could help in medical decision-making for patients in a vegetative state, and test whether a particular rehabilitation treatment is working.

We temporarily appear to lose consciousness every day, from the moment we fall asleep to when we wake up. Anaesthesia can also induce temporary unconsciousness. Permanent unconsciousness can occur with certain brain injuries.

This state is characterized as the inability to respond to stimuli and report on subjective experience, but this behaviour is not always the result of unconsciousness. Some apparently unresponsive patients can indeed show brain activity similar to that of conscious individuals when asked to imagine performing a physical task.

Constantly changing activity and connectivity

Previous research has shown that electroencephalography (EEG) recordings of the brain’s activities reveal patterns in brain waves associated with consciousness. These patterns do not provide reliable spatial information about brain activity, however. fMRI appears to be better in this respect and this technique has already shown that the brain spontaneously generates a dynamic series of constantly changing activity and connectivity between brain regions during normal wakefulness*.

To better understand what occurs in the human brain during consciousness and unconsciousness, and ultimately develop reliable markers to distinguish between the two states, a team of researchers led by Jacobo Sitt of INSERM and the Institut du Cerveau et de la Moelle Epinière in Paris has now performed fMRI scans on 159 subjects. 47 of these were healthy individuals who had undergone anaesthesia and 78 were patients who were suffering from unresponsive wakefulness syndrome (UWS) or who were in a minimally conscious state (MCS).

The researchers analyzed how fluctuations of the fMRI blood oxygenation level-dependent signals (an indicator for neuronal activity) were coordinated across 42 key brain regions, representing six brain networks known to be important for cognition.

Four main identifiable patterns

Data from the scans revealed four main identifiable patterns of activity. The first pattern, which was seen more often in healthy, conscious people, was highly complex with long-distance, brain-wide coordination between region. The fourth pattern, however, showed low interareal coordination and was seen more in UWS patients. MCS patients had patterns that were generally in between the two.

The scans were carried out at four independent research sites to improve result reliabilities.

These findings provide insight into the large-scale brain dynamics that support conscious behaviour – suggesting that highly complex, long-distance brain-wide coordination is a key characteristic of consciousness – and they offer important clues in the search for biological markers of consciousness, say the authors, who report their work in Science Advances.

The other French laboratories involved in this work:

Cognitive Neuroimaging Unit (CEA, INSERM, Université Paris-Sud, Université Paris-Saclay)

Collège de France

Sorbonne Universités (UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière)


Human consciousness is supported by dynamic complex patterns of brain signal coordination, by A. Demertzi, E. Tagliazucchi, S. Dehaene, G. Deco, P. Barttfeld, F. Raimondo, C. Martial1, D. Fernández-Espejo, B. Rohaut, H. U. Voss, N. D. Schiff, A. M. Owen, S. Laureys, L. Naccache and J. D. Sitt in Science Advances 10.1126/sciadv.aat7603

*A. Zalesky et al., Time-resolved resting-state brain networksProc. Natl. Acad. Sci. U.S.A. 111, 10341–10346 (2014)

F.Betzel et al., Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. Neuroimage 127, 287–297 (2016)

M.Hutchison et al., Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80, 360–378 (2013)