Jitka Annen1,2, Gianluca Frasso3, Julia Sophia Crone4, Lizette Heine1,5, Carol Di Perri1,2,6, Charlotte Martial1,2, Helena Cassol1,2, Athena Demertzi1,7, Lionel Naccache7, Steven Laureys1,2. 1. Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium. 2. University Hospital of Liège, Liège, Belgium. 3. Faculty of Social Sciences, Quantitative Methods for Social Sciences, University of Liège, Liège, Belgium. 4. Department of Psychology, UCLA, Los Angeles, CA. 5. Auditory Cognition and Psychoacoustics Team, Lyon Neuroscience Research Center, Lyon, France. 6. Centre for Clinical Brain Sciences UK Dementia Research Institute, Centre for Dementia Prevention, University of Edinburgh, Edinburgh, United Kingdom. 7. INSERM, U 1127, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, 47 bd de l'Hôpital, 75013, Paris, France.
Abstract
OBJECTIVE: The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. METHODS: Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. RESULTS: Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. INTERPRETATION: Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853.
OBJECTIVE: The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. METHODS: Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. RESULTS:Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. INTERPRETATION: Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853.
Authors: A Demertzi; E Tagliazucchi; S Dehaene; G Deco; P Barttfeld; F Raimondo; C Martial; D Fernández-Espejo; B Rohaut; H U Voss; N D Schiff; A M Owen; S Laureys; L Naccache; J D Sitt Journal: Sci Adv Date: 2019-02-06 Impact factor: 14.136
Authors: Alfonso Magliacano; Martin Rosenfelder; Nina Hieber; Andreas Bender; Anna Estraneo; Luigi Trojano Journal: Sci Rep Date: 2021-11-17 Impact factor: 4.379