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EEG Power spectra and subcortical pathology in chronic disorders of consciousness

Published online by Cambridge University Press:  23 September 2020

Evan S. Lutkenhoff
Affiliation:
Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
Anna Nigri
Affiliation:
Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Davide Rossi Sebastiano
Affiliation:
Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Davide Sattin
Affiliation:
Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Elisa Visani
Affiliation:
Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Cristina Rosazza
Affiliation:
Scientific Direction, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Ludovico D'Incerti
Affiliation:
Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Maria Grazia Bruzzone
Affiliation:
Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Silvana Franceschetti
Affiliation:
Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Matilde Leonardi
Affiliation:
Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Stefania Ferraro*
Affiliation:
Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China: On the behalf of the Coma Research Center, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
Martin M. Monti
Affiliation:
Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
*
Author for correspondence: Stefania Ferraro, E-mail: stefania.ferraro@istituto-besta.it

Abstract

Background

Despite a growing understanding of disorders of consciousness following severe brain injury, the association between long-term impairment of consciousness, spontaneous brain oscillations, and underlying subcortical damage, and the ability of such information to aid patient diagnosis, remains incomplete.

Methods

Cross-sectional observational sample of 116 patients with a disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018.

Results

In the final analyzed sample of 61 patients, systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in the brainstem. Furthermore, we showed that the combination of demographics, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis.

Conclusions

These results ground, for the first time, electroencephalographic presentation detected with routine clinical techniques in the underlying brain pathology of disorders of consciousness and demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of this patient cohort.

Type
Original Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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