Brain gray matter MRI morphometry for neuroprognostication after cardiac arrest

Article; Belgium; France; Italy; Adult; Brain; Cerebellar Cortex; Coma; Female; Gray Matter; Heart Arrest; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Prognosis; Prospective Studies

Abstract :

[en] Objectives: We hypothesize that the combined use of MRI cortical thickness measurement and subcortical gray matter volumetry could provide an early and accurate in vivo assessment of the structural impact of cardiac arrest and therefore could be used for long-term neuroprognostication in this setting. Design: Prospective cohort study. Setting: Five Intensive Critical Care Units affiliated to the University in Toulouse (France), Paris (France), Clermont-Ferrand (France), Liège (Belgium), and Monza (Italy). Patients: High-resolution anatomical T1-weighted images were acquired in 126 anoxic coma patients ("learning" sample) 16 ± 8 days after cardiac arrest and 70 matched controls. An additional sample of 18 anoxic coma patients, recruited in Toulouse, was used to test predictive model generalization ("test" sample). All patients were followed up 1 year after cardiac arrest. Interventions: None. Measurements and Main Results: Cortical thickness was computed on the whole cortical ribbon, and deep gray matter volumetry was performed after automatic segmentation. Brain morphometric data were employed to create multivariate predictive models using learning machine techniques. Patients displayed significantly extensive cortical and subcortical brain volumes atrophy compared with controls. The accuracy of a predictive classifier, encompassing cortical and subcortical components, has a significant discriminative power (learning area under the curve = 0.87; test area under the curve = 0.96). The anatomical regions which volume changes were significantly related to patient's outcome were frontal cortex, posterior cingulate cortex, thalamus, putamen, pallidum, caudate, hippocampus, and brain stem. Conclusions: These findings are consistent with the hypothesis of pathologic disruption of a striatopallidal-thalamo-cortical mesocircuit induced by cardiac arrest and pave the way for the use of combined brain quantitative morphometry in this setting. Copyright © 2017 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Disciplines :

Neurology

Author, co-author :

Silva, Stein; Department of Anaesthesiology and Critical Care, Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France, Critical Care and Anaesthesiology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France, Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Peran, Patrice; Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Kerhuel, Lionel; Department of Anaesthesiology and Critical Care, Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France, Critical Care and Anaesthesiology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France, Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Malagurski, Briguita; Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Chauveau, Nicolas; Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Bataille, Benoit; Department of Anaesthesiology and Critical Care, Critical Care Unit, Hopital Dieu Hospital, Narbonne, France

Lotterie, Jean Albert; Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Celsis, Pierre; Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Aubry, Florent; Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France

Citerio, Giuseppe; Department of Anaesthesiology and Critical Care, School of Medicine and Surgery, University Milano Bicocca and Hospital San Gerardo, Monza, Italy

More authors (9 more)

Language :

English

Title :

Brain gray matter MRI morphometry for neuroprognostication after cardiac arrest

Publication date :

2017

Journal title :

Critical Care Medicine

ISSN :

0090-3493

eISSN :

1530-0293

Publisher :

Lippincott Williams and Wilkins

Volume :

Issue :

Pages :

e763-e771

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 06 January 2020

Statistics

Number of views

88 (1 by ULiège)

Number of downloads

59 (2 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Laureys S, Boly M: The changing spectrum of coma. Nat Clin Pract Neurol 2008; 4:544-546
Greer DM, Rosenthal ES, Wu O: Neuroprognostication of hypoxicischaemic coma in the therapeutic hypothermia era. Nat Rev Neurol 2014; 10:190-203
Young GB: Clinical practice. Neurologic prognosis after cardiac arrest. N Engl J Med 2009; 361:605-611
Adams JH, Graham DI, Jennett B: The neuropathology of the vegetative state after an acute brain insult. Brain 2000; 123 (Pt 7):1327-1338
Nolan JP, Neumar RW, Adrie C, et al: Post-cardiac arrest syndrome: Epidemiology, pathophysiology, treatment, and prognostication. A Scientific Statement from the International Liaison Committee on Resuscitation; the American Heart Association Emergency Cardiovascular Care Committee; the Council on Cardiovascular Surgery and Anesthesia; the Council on Cardiopulmonary, Perioperative, and Critical Care; the Council on Clinical Cardiology; the Council on Stroke. Resuscitation 2008; 79:350-379
Péran P, Cherubini A, Assogna F, et al: Magnetic resonance imaging markers of Parkinson's disease nigrostriatal signature. Brain 2010; 133:3423-3433
Querbes O, Aubry F, Pariente J, et al; Alzheimer's Disease Neuroimaging Initiative: Early diagnosis of Alzheimer's disease using cortical thickness: Impact of cognitive reserve. Brain 2009; 132: 2036-2047
Jennett B, Snoek J, Bond MR, et al: Disability after severe head injury: Observations on the use of the Glasgow Outcome Scale. J Neurol Neurosurg Psychiatry 1981; 44:285-293
Jones SE, Buchbinder BR, Aharon I: Three-dimensional mapping of cortical thickness using Laplace's equation. Hum Brain Mapp 2000; 11:12-32
McIntosh AR, Lobaugh NJ: Partial least squares analysis of neuroimaging data: Applications and advances. Neuroimage 2004; 23 (Suppl 1):S250-S263
Bewick V, Cheek L, Ball J: Statistics review 13: Receiver operating characteristic curves. Crit Care 2004; 8:508-512
Baars BJ, Ramsøy TZ, Laureys S: Brain, conscious experience and the observing self. Trends Neurosci 2003; 26:671-675
Crick F, Koch C: A framework for consciousness. Nat Neurosci 2003; 6:119-126
Achard S, Delon-Martin C, Vértes PE, et al: Hubs of brain functional networks are radically reorganized in comatose patients. Proc Natl Acad Sci U S A 2012; 109:20608-20613
Sporns O: The human connectome: A complex network. Ann N Y Acad Sci 2011; 1224:109-125
Horovitz SG, Braun AR, Carr WS, et al: Decoupling of the brain's default mode network during deep sleep. Proc Natl Acad Sci U S A 2009; 106:11376-11381
Amico E, Gomez F, Di Perri C, et al: Posterior cingulate cortex-related co-activation patterns: A resting state FMRI study in propofol-induced loss of consciousness. PLoS One 2014; 9:e100012
Silva S, de Pasquale F, Vuillaume C, et al: Disruption of posteromedial large-scale neural communication predicts recovery from coma. Neurology 2015; 85:2036-2044
Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ, et al: Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 2010; 133:161-171
Silva S, Alacoque X, Fourcade O, et al: Wakefulness and loss of awareness: Brain and brainstem interaction in the vegetative state. Neurology 2010; 74:313-320
Di Perri C, Bastianello S, Bartsch AJ, et al: Limbic hyperconnectivity in the vegetative state. Neurology 2013; 81:1417-1424
Shepherd GM: Corticostriatal connectivity and its role in disease. Nat Rev Neurosci 2013; 14:278-291
Zuo XN, Di Martino A, Kelly C, et al: The oscillating brain: Complex and reliable. Neuroimage 2010; 49:1432-1445
Posner MI, Dehaene S: Attentional networks. Trends Neurosci 1994; 17:75-79
Bremmer F, Schlack A, Shah NJ, et al: Polymodal motion processing in posterior parietal and premotor cortex: A human fMRI study strongly implies equivalencies between humans and monkeys. Neuron 2001; 29:287-296
Noirhomme Q, Soddu A, Lehembre R, et al: Brain connectivity in pathological and pharmacological coma. Front Syst Neurosci 2010; 4:160
Monti MM, Rosenberg M, Finoia P, et al: Thalamo-frontal connectivity mediates top-down cognitive functions in disorders of consciousness. Neurology 2015; 84:167-173
Schiff ND: Recovery of consciousness after brain injury: A mesocircuit hypothesis. Trends Neurosci 2010; 33:1-9
Choi DW: Ischemia-induced neuronal apoptosis. Curr Opin Neurobiol 1996; 6:667-672
Galanaud D, Naccache L, Puybasset L: Exploring impaired consciousness: The MRI approach. Curr Opin Neurol 2007; 20:627-631
Dehaene S, Changeux JP: Experimental and theoretical approaches to conscious processing. Neuron 2011; 70:200-227
Hahn DK, Geocadin RG, Greer DM: Quality of evidence in studies evaluating neuroimaging for neurologic prognostication in adult patients resuscitated from cardiac arrest. Resuscitation 2014; 85:165-172
Mohri M, Rostamizadeh A, Talwalkar A: Foundations of Machine Learning. Cambridge, MA, The MIT Press, 2012
Laureys S, Schiff ND: Coma and consciousness: Paradigms (re) framed by neuroimaging. Neuroimage 2012; 61:478-491
Coffey CE, Saxton JA, Ratcliff G, et al: Relation of education to brain size in normal aging: Implications for the reserve hypothesis. Neurology 1999; 53:189-196
Sandroni C, Cariou A, Cavallaro F, et al: Prognostication in comatose survivors of cardiac arrest: An advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation 2014; 85:1779-1789