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DOI: 10.1055/a-1892-1894
Applications of Advanced MRI to Disorders of Consciousness
Funding D.F. is supported by the NIH National Institute of Neurologic Disorders and Stroke (R25NS06574309). V.N. is supported by the Academy of Medical Sciences/The Health Foundation Clinician Scientist Fellowship. D.F.-E. has no funding to report. S.B.S. is supported by the American Academy of Neurology Clinical Research Training Scholarship.Abstract
Disorder of consciousness (DoC) after severe brain injury presents numerous challenges to clinicians, as the diagnosis, prognosis, and management are often uncertain. Magnetic resonance imaging (MRI) has long been used to evaluate brain structure in patients with DoC. More recently, advances in MRI technology have permitted more detailed investigations of the brain's structural integrity (via diffusion MRI) and function (via functional MRI). A growing literature has begun to show that these advanced forms of MRI may improve our understanding of DoC pathophysiology, facilitate the identification of patient consciousness, and improve the accuracy of clinical prognostication. Here we review the emerging evidence for the application of advanced MRI for patients with DoC.
Publikationsverlauf
Accepted Manuscript online:
05. Juli 2022
Artikel online veröffentlicht:
13. September 2022
© 2022. Thieme. All rights reserved.
Thieme Medical Publishers, Inc.
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References
- 1 Pratt AK, Chang JJ, Sederstrom NO. A fate worse than death: prognostication of devastating brain injury. Crit Care Med 2019; 47 (04) 591-598
- 2 Elmer J, Torres C, Aufderheide TP. et al; Resuscitation Outcomes Consortium. Association of early withdrawal of life-sustaining therapy for perceived neurological prognosis with mortality after cardiac arrest. Resuscitation 2016; 102: 127-135
- 3 Turgeon AF, Lauzier F, Simard JF. et al; Canadian Critical Care Trials Group. Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study. CMAJ 2011; 183 (14) 1581-1588
- 4 Hirsch KG, Fischbein N, Mlynash M. et al. Prognostic value of diffusion-weighted MRI for post-cardiac arrest coma. Neurology 2020; 94 (16) e1684-e1692
- 5 Rohaut B, Doyle KW, Reynolds AS. et al. Deep structural brain lesions associated with consciousness impairment early after hemorrhagic stroke. Sci Rep 2019; 9 (01) 4174
- 6 Snider SB, Edlow BL. MRI in disorders of consciousness. Curr Opin Neurol 2020; 33 (06) 676-683
- 7 Laureys S, Schiff ND. Coma and consciousness: paradigms (re)framed by neuroimaging. Neuroimage 2012; 61 (02) 478-491
- 8 Koch C, Massimini M, Boly M, Tononi G. Neural correlates of consciousness: progress and problems. Nat Rev Neurosci 2016; 17 (05) 307-321
- 9 Schiff ND. Cognitive motor dissociation following severe brain injuries. JAMA Neurol 2015; 72 (12) 1413-1415
- 10 Sair HI, Hannawi Y, Li S. et al; Neuroimaging for Coma Emergence and Recovery (NICER) Consortium. Early functional connectome integrity and 1-year recovery in comatose survivors of cardiac arrest. Radiology 2018; 287 (01) 247-255
- 11 Velly L, Perlbarg V, Boulier T. et al; MRI-COMA Investigators. Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. Lancet Neurol 2018; 17 (04) 317-326
- 12 Fischer DB, Boes AD, Demertzi A. et al. A human brain network derived from coma-causing brainstem lesions. Neurology 2016; 87 (23) 2427-2434
- 13 Parvizi J, Damasio AR. Neuroanatomical correlates of brainstem coma. Brain 2003; 126 (Pt 7): 1524-1536
- 14 Hindman J, Bowren MD, Bruss J, Wright B, Geerling JC, Boes AD. Thalamic strokes that severely impair arousal extend into the brainstem. Ann Neurol 2018; 84 (06) 926-930
- 15 Snider SB, Hsu J, Darby RR. et al. Cortical lesions causing loss of consciousness are anticorrelated with the dorsal brainstem. Hum Brain Mapp 2020; 41 (06) 1520-1531
- 16 Adams JH, Jennett B, McLellan DR, Murray LS, Graham DI. The neuropathology of the vegetative state after head injury. J Clin Pathol 1999; 52 (11) 804-806
- 17 Posner JB, Saper CB, Schiff ND, Claassen J. Plum and Posner's Diagnosis and Treatment of Stupor and Coma. 5th ed.. Oxford; New York: Oxford University Press; 2019
- 18 Owen AM, Coleman MR, Menon DK. et al. Residual auditory function in persistent vegetative state: a combined PET and fMRI study. Neuropsychol Rehabil 2005; 15 (3-4): 290-306
- 19 Di HB, Yu SM, Weng XC. et al. Cerebral response to patient's own name in the vegetative and minimally conscious states. Neurology 2007; 68 (12) 895-899
- 20 Monti MM, Pickard JD, Owen AM. Visual cognition in disorders of consciousness: from V1 to top-down attention. Hum Brain Mapp 2013; 34 (06) 1245-1253
- 21 Menon DK, Owen AM, Williams EJ. et al; Wolfson Brain Imaging Centre Team. Cortical processing in persistent vegetative state. Lancet 1998; 352 (9123): 200
- 22 Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007; 8 (09) 700-711
- 23 Yeo BT, Krienen FM, Sepulcre J. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011; 106 (03) 1125-1165
- 24 Raichle ME. The brain's default mode network. Annu Rev Neurosci 2015; 38: 433-447
- 25 Buckner RL, DiNicola LM. The brain's default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci 2019; 20 (10) 593-608
- 26 Fernández-Espejo D, Soddu A, Cruse D. et al. A role for the default mode network in the bases of disorders of consciousness. Ann Neurol 2012; 72 (03) 335-343
- 27 Rosazza C, Andronache A, Sattin D. et al; Coma Research Center, Besta Institute. Multimodal study of default-mode network integrity in disorders of consciousness. Ann Neurol 2016; 79 (05) 841-853
- 28 Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ-F. et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 2010; 133 (Pt 1): 161-171
- 29 Norton L, Hutchison RM, Young GB, Lee DH, Sharpe MD, Mirsattari SM. Disruptions of functional connectivity in the default mode network of comatose patients. Neurology 2012; 78 (03) 175-181
- 30 Demertzi A, Antonopoulos G, Heine L. et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain 2015; 138 (Pt 9): 2619-2631
- 31 Seeley WW, Menon V, Schatzberg AF. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007; 27 (09) 2349-2356
- 32 Wu X, Zou Q, Hu J. et al. Intrinsic functional connectivity patterns predict consciousness level and recovery outcome in acquired brain injury. J Neurosci 2015; 35 (37) 12932-12946
- 33 Di Perri C, Bahri MA, Amico E. et al. Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study. Lancet Neurol 2016; 15 (08) 830-842
- 34 Threlkeld ZD, Bodien YG, Rosenthal ES. et al. Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury. Cortex 2018; 106: 299-308
- 35 Kondziella D, Fisher PM, Larsen VA. et al. Functional MRI for assessment of the default mode network in acute brain injury. Neurocrit Care 2017; 27 (03) 401-406
- 36 Di Perri C, Amico E, Heine L. et al. Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns. Hum Brain Mapp 2018; 39 (01) 89-103
- 37 Demertzi A, Tagliazucchi E, Dehaene S. et al. Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv 2019; 5 (02) eaat7603
- 38 Cao B, Chen Y, Yu R. et al. Abnormal dynamic properties of functional connectivity in disorders of consciousness. Neuroimage Clin 2019; 24: 102071
- 39 Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996; 36 (06) 893-906
- 40 Weng L, Xie Q, Zhao L. et al. Abnormal structural connectivity between the basal ganglia, thalamus, and frontal cortex in patients with disorders of consciousness. Cortex 2017; 90: 71-87
- 41 Zheng ZS, Reggente N, Lutkenhoff E, Owen AM, Monti MM. Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning. Hum Brain Mapp 2017; 38 (01) 431-443
- 42 Snider SB, Bodien YG, Bianciardi M, Brown EN, Wu O, Edlow BL. Disruption of the ascending arousal network in acute traumatic disorders of consciousness. Neurology 2019; 93 (13) e1281-e1287
- 43 Newcombe VF, Williams GB, Scoffings D. et al. Aetiological differences in neuroanatomy of the vegetative state: insights from diffusion tensor imaging and functional implications. J Neurol Neurosurg Psychiatry 2010; 81 (05) 552-561
- 44 Guldenmund P, Soddu A, Baquero K. et al. Structural brain injury in patients with disorders of consciousness: a voxel-based morphometry study. Brain Inj 2016; 30 (03) 343-352
- 45 Annen J, Frasso G, Crone JS. et al; Coma Science Group Collaborators. Regional brain volumetry and brain function in severely brain-injured patients. Ann Neurol 2018; 83 (04) 842-853
- 46 Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science 2006; 313 (5792): 1402
- 47 Monti MM, Vanhaudenhuyse A, Coleman MR. et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med 2010; 362 (07) 579-589
- 48 Edlow BL, Chatelle C, Spencer CA. et al. Early detection of consciousness in patients with acute severe traumatic brain injury. Brain 2017; 140 (09) 2399-2414
- 49 Fernández-Espejo D, Rossit S, Owen AM. A thalamocortical mechanism for the absence of overt motor behavior in covertly aware patients. JAMA Neurol 2015; 72 (12) 1442-1450
- 50 Stafford CA, Owen AM, Fernández-Espejo D. The neural basis of external responsiveness in prolonged disorders of consciousness. Neuroimage Clin 2019; 22: 101791
- 51 Giacino JT, Katz DI, Schiff ND. et al. Practice guideline update recommendations summary: disorders of consciousness: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Neurology 2018; 91 (10) 450-460
- 52 Kondziella D, Bender A, Diserens K. et al; EAN Panel on Coma, Disorders of Consciousness. European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness. Eur J Neurol 2020; 27 (05) 741-756
- 53 Monti MM, Schnakers C. Flowchart for implementing advanced imaging and electrophysiology in patients with disorders of consciousness: To fMRI or not to fMRI?. Neurology 2022; 98 (11) 452-459
- 54 Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004; 85 (12) 2020-2029
- 55 Mahadevan AS, Tooley UA, Bertolero MA, Mackey AP, Bassett DS. Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data. bioRxiv 2018; 1-36
- 56 Turgeon AF, Lauzier F, Burns KEA. et al; Canadian Critical Care Trials Group. Determination of neurologic prognosis and clinical decision making in adult patients with severe traumatic brain injury: a survey of Canadian intensivists, neurosurgeons, and neurologists. Crit Care Med 2013; 41 (04) 1086-1093
- 57 Leblanc G, Boutin A, Shemilt M. et al. Incidence and impact of withdrawal of life-sustaining therapies in clinical trials of severe traumatic brain injury: a systematic review. Clin Trials 2018; 15 (04) 398-412
- 58 Edlow BL, Claassen J, Schiff ND, Greer DM. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat Rev Neurol 2021; 17 (03) 135-156
- 59 Di H, Boly M, Weng X, Ledoux D, Laureys S. Neuroimaging activation studies in the vegetative state: predictors of recovery?. Clin Med (Lond) 2008; 8 (05) 502-507
- 60 Coleman MR, Davis MH, Rodd JM. et al. Towards the routine use of brain imaging to aid the clinical diagnosis of disorders of consciousness. Brain 2009; 132 (Pt 9): 2541-2552
- 61 Wang F, Di H, Hu X. et al. Cerebral response to subject's own name showed high prognostic value in traumatic vegetative state. BMC Med 2015; 13: 83
- 62 Koenig MA, Holt JL, Ernst T. et al. MRI default mode network connectivity is associated with functional outcome after cardiopulmonary arrest. Neurocrit Care 2014; 20 (03) 348-357
- 63 Silva S, de Pasquale F, Vuillaume C. et al. Disruption of posteromedial large-scale neural communication predicts recovery from coma. Neurology 2015; 85 (23) 2036-2044
- 64 Song M, Yang Y, He J. et al. Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics. eLife 2018; 7: 7
- 65 Guo H, Liu R, Sun Z. et al. Evaluation of prognosis in patients with severe traumatic brain injury using resting-state functional magnetic resonance imaging. World Neurosurg 2019; 121: e630-e639
- 66 Yu Y, Meng F, Zhang L. et al. A multi-domain prognostic model of disorder of consciousness using resting-state fMRI and laboratory parameters. Brain Imaging Behav 2021; 15 (04) 1966-1976
- 67 Pugin D, Hofmeister J, Gasche Y. et al. Resting-state brain activity for early prediction outcome in postanoxic patients in a coma with indeterminate clinical prognosis. AJNR Am J Neuroradiol 2020; 41 (06) 1022-1030
- 68 Peran P, Malagurski B, Nemmi F. et al. Functional and structural integrity of frontoparietal connectivity in traumatic and anoxic coma. Crit Care Med 2020; 48 (08) e639-e647
- 69 Fischer D, Threlkeld ZD, Bodien YG. et al. Intact brain network function in an unresponsive patient with COVID-19. Ann Neurol 2020; 88 (04) 851-854
- 70 Qin P, Wu X, Huang Z. et al. How are different neural networks related to consciousness?. Ann Neurol 2015; 78 (04) 594-605
- 71 Maknojia S, Churchill NW, Schweizer TA, Graham SJ. Resting state fMRI: going through the motions. Front Neurosci 2019; 13: 825
- 72 Kirsch M, Guldenmund P, Ali Bahri M. et al. Sedation of patients with disorders of consciousness during neuroimaging: effects on resting state functional brain connectivity. Anesth Analg 2017; 124 (02) 588-598
- 73 Stamatakis EA, Adapa RM, Absalom AR, Menon DK. Changes in resting neural connectivity during propofol sedation. PLoS One 2010; 5 (12) e14224
- 74 Liang P, Zhang H, Xu Y, Jia W, Zang Y, Li K. Disruption of cortical integration during midazolam-induced light sedation. Hum Brain Mapp 2015; 36 (11) 4247-4261
- 75 Bonhomme V, Vanhaudenhuyse A, Demertzi A. et al. Resting-state network-specific breakdown of functional connectivity during ketamine alteration of consciousness in volunteers. Anesthesiology 2016; 125 (05) 873-888
- 76 Guldenmund P, Demertzi A, Boveroux P. et al. Thalamus, brainstem and salience network connectivity changes during propofol-induced sedation and unconsciousness. Brain Connect 2013; 3 (03) 273-285
- 77 Ni L, Wen J, Zhang LJ. et al. Aberrant default-mode functional connectivity in patients with end-stage renal disease: a resting-state functional MR imaging study. Radiology 2014; 271 (02) 543-552
- 78 Murray GD, Butcher I, McHugh GS. et al. Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. J Neurotrauma 2007; 24 (02) 329-337
- 79 Galanaud D, Perlbarg V, Gupta R. et al; Neuro Imaging for Coma Emergence and Recovery Consortium. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology 2012; 117 (06) 1300-1310
- 80 Tollard E, Galanaud D, Perlbarg V. et al. Experience of diffusion tensor imaging and 1H spectroscopy for outcome prediction in severe traumatic brain injury: Preliminary results. Crit Care Med 2009; 37 (04) 1448-1455
- 81 Betz J, Zhuo J, Roy A, Shanmuganathan K, Gullapalli RP. Prognostic value of diffusion tensor imaging parameters in severe traumatic brain injury. J Neurotrauma 2012; 29 (07) 1292-1305
- 82 Luyt CE, Galanaud D, Perlbarg V. et al; Neuro Imaging for Coma Emergence and Recovery Consortium. Diffusion tensor imaging to predict long-term outcome after cardiac arrest: a bicentric pilot study. Anesthesiology 2012; 117 (06) 1311-1321
- 83 Greicius MD, Kiviniemi V, Tervonen O. et al. Persistent default-mode network connectivity during light sedation. Hum Brain Mapp 2008; 29 (07) 839-847
- 84 Bevers MB, Scirica BM, Avery KR, Henderson GV, Lin AP, Lee JW. Combination of clinical exam, MRI and EEG to predict outcome following cardiac arrest and targeted temperature management. Neurocrit Care 2018; 29 (03) 396-403
- 85 Bruno MA, Fernández-Espejo D, Lehembre R. et al. Multimodal neuroimaging in patients with disorders of consciousness showing “functional hemispherectomy”. Prog Brain Res 2011; 193: 323-333
- 86 Barth M, Breuer F, Koopmans PJ, Norris DG, Poser BA. Simultaneous multislice (SMS) imaging techniques. Magn Reson Med 2016; 75 (01) 63-81
- 87 Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med 2012; 67 (05) 1210-1224
- 88 Chan ST, Evans KC, Song TY. et al. Dynamic brain-body coupling of breath-by-breath O2-CO2 exchange ratio with resting state cerebral hemodynamic fluctuations. PLoS One 2020; 15 (09) e0238946
- 89 Chang C, Glover GH. Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. Neuroimage 2009; 47 (04) 1381-1393
- 90 Kalthoff D, Seehafer JU, Po C, Wiedermann D, Hoehn M. Functional connectivity in the rat at 11.7T: impact of physiological noise in resting state fMRI. Neuroimage 2011; 54 (04) 2828-2839
- 91 Birn RM, Diamond JB, Smith MA, Bandettini PA. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage 2006; 31 (04) 1536-1548
- 92 Glover GH, Li TQ, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 2000; 44 (01) 162-167
- 93 Edlow BL, Barra ME, Zhou DW. et al. Personalized connectome mapping to guide targeted therapy and promote recovery of consciousness in the intensive care unit. Neurocrit Care 2020; 33 (02) 364-375
- 94 Crone JS, Schurz M, Höller Y. et al. Impaired consciousness is linked to changes in effective connectivity of the posterior cingulate cortex within the default mode network. Neuroimage 2015; 110: 101-109
- 95 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 (50) 20608-20613
- 96 Thibaut A, Schiff N, Giacino J, Laureys S, Gosseries O. Therapeutic interventions in patients with prolonged disorders of consciousness. Lancet Neurol 2019; 18 (06) 600-614
- 97 Thibaut A, Chennu S, Chatelle C. et al. Theta network centrality correlates with tDCS response in disorders of consciousness. Brain Stimul 2018; 11 (06) 1407-1409
- 98 Horn A, Fox MD. Opportunities of connectomic neuromodulation. Neuroimage 2020; 221: 117180
- 99 Spindler LRB, Luppi AI, Adapa RM. et al. Dopaminergic brainstem disconnection is common to pharmacological and pathological consciousness perturbation. Proc Natl Acad Sci U S A 2021; 118 (30) 118