“Look at my classifier's result”: Disentangling unresponsive from (minimally) conscious patients

NeuroImage

Volumn 145, Issue , 2017, Pages 288-303

  Noirhomme, Quentin ^a,b,c   Brecheisen, Ralph ^a,b   Lesenfants, Damien ^d   Antonopoulos, Georgios ^e   Laureys, Steven ^e  

^a BRAIN INNOVATION B V   (Netherlands)

^b MAASTRICHT UNIVERSITY   (Netherlands)

^c UNIVERSITY OF LIÈGE   (Belgium)

^d Brown University   (United States)

^e UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

Classifier; Coma; Diagnosis; Disorders of consciousness; Locked in syndrome; Machine learning; Minimally conscious state; Prognosis; Unresponsive wakefulness syndrome; Vegetative state

Indexed keywords

AROUSAL; ARTICLE; ATTENTION; AUDITORY ATTENTION; CLASSIFICATION; CLINICAL FEATURE; CLINICAL PRACTICE; CLINICAL PROTOCOL; COMMAND FOLLOWING TEST; CONFIDENCE INTERVAL; CONSCIOUSNESS DISORDER; CONSCIOUSNESS LEVEL; DIAGNOSTIC VALUE; ELECTROENCEPHALOGRAPHY; EXPERIMENTAL BEHAVIORAL TEST; EYE MOVEMENT; FATIGUE; FUNCTIONAL ASSESSMENT; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENETIC ASSOCIATION; GOLD STANDARD; HEARING; HEARING DISORDER; HUMAN; INTERMETHOD COMPARISON; INTERPERSONAL COMMUNICATION; MACHINE LEARNING; MEMORY; MENTAL PERFORMANCE; MENTAL TASK; NEUROIMAGING; NOISE; OUTCOME ASSESSMENT; PREDICTION; PRIORITY JOURNAL; PSYCHOMOTOR DISORDER; RELIABILITY; RESPIRATORY FUNCTION; SAMPLE SIZE; STATISTICAL ANALYSIS; UNRESPONSIVE WAKEFULNESS SYNDROME; VALIDATION PROCESS; VISUAL ATTENTION; VISUAL DISORDER; DIAGNOSTIC IMAGING; PATHOPHYSIOLOGY;

CONSCIOUSNESS DISORDERS; HUMANS; MACHINE LEARNING;

EID: 84953311338     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.12.006     Document Type: Article

References (121)

1. Altman, D.G., Bland, J.M., Statistics Notes: Diagnostic tests 2: predictive values. BMJ, 309(6947), 1994, 102.
2. Andrews, K., Murphy, L., Munday, R., Littlewood, C., Misdiagnosis of the vegetative state: retrospective study in a rehabilitation unit. BMJ 313 (1996), 13–16.
3. Bardin, J.C., Fins, J.J., Katz, D.I., Hersh, J., Heier, L.A., Tabelow, K., Dyke, J.P., Ballon, D.J., Schiff, N.D., Voss, H.U., Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134:3 (2011), 769–782.
4. Barttfeld, P., Uhrig, L., Sitt, J.D., Sigman, M., Jarraya, B., Dehaene, S., Signature of consciousness in the dynamics of resting-state brain activity. Proceedings of the National Academy of Sciences 112:3 (2015), 887–892.
5. Bekinschtein, T.A., Coleman, M.R., Niklison, J., Pickard, J.D., Manes, F.F., Can electromyography objectively detect voluntary movement in disorders of consciousness?. J. Neurol. Neurosurg. Psychiatry 79:7 (2008), 826–-828.
6. Bekinschtein, T.A., Dehaene, S., Rohaut, B., Tadel, F., Cohen, L., Naccache, L., Neural signature of the conscious processing of auditory regularities. Proceedings of the National Academy of Sciences 106:5 (2009), 1672–1677.
7. Berrar, D., Bradbury, I., Dubitzky, W., Avoiding model selection bias in small-sample genomic datasets. Bioinformatics 22:10 (2006), 1245–1250.
8. Billinger, M., Daly, I., Kaiser, V., Jin, J., Allison, B.Z., Müller-Putz, G.R., Brunner, C., Is it significant? Guidelines for reporting BCI performance. In Towards Practical Brain-Computer Interfaces, 2013, Springer, Berlin Heidelberg, 333–-354.
9. Blankertz, B., Lemm, S., Treder, M., Haufe, S., Müller, K.-R., Single-trial analysis and classification of ERP components—a tutorial. NeuroImage 56:2 (2011), 814–825.
10. Bodart, O., Laureys, S., Predicting outcome from subacute unresponsive wakefulness syndrome or vegetative state. Crit. Care, 18(2), 2014, 132, 10.1186/cc13831 (Apr 15).
11. Boly, M., Coleman, M.R., Davis, M.H., Hampshire, A.A., Bor, D., MMoonen, G., Maquet, P.A., Pickard, J.D., LLaureys, S., Owen, A.A.M., When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients. Neuroimage 36:3 (2007), 979–992.
12. Brodley, C.E., Friedl, M.A., Identifying mislabeled training data. Journal of Artificial Intelligence Research 11 (1999), 131–167.
13. Bruno, M.-A., Vanhaudenhuyse, A., Thibaut, A., Moonen, G., Laureys, S., From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: recent advances in our understanding of disorders of consciousness. J. Neurol. 258:7 (2011), 1373–1384.
14. Casali, A.G., Gosseries, O., Rosanova, M., Boly, M., Sarasso, S., Casali, K.R., Casarotto, S., et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci. Transl. Med., 5(198), 2013 (198ra105-198ra105).
15. Childs, N.L., Mercer, W.N., Misdiagnosing the persistent vegetative state. Misdiagnosis certainly occurs. BMJ, 313, 1996, 944.
16. Charland-Verville, V., Lesenfants, D., Sela, L., Noirhomme, Q., Ziegler, E., Chatelle, C., Plotkin, A., Sobel, N., Laureys, S., Detection of response to command using voluntary control of breathing in disorders of consciousness. Front. Hum. Neurosci., 8, 2014.
17. Chatelle, C., Laureys, S., Noirhomme, Q., Brain-Computer Interfaces and Diagnosis. Brain-Computer-Interfaces in their ethical, social and cultural contexts, 2014, Springer, Netherlands, 39–-47.
18. Chatelle, C., Lesenfants, D., Guller, Y., Laureys, S., Noirhomme, Q., Brain-Computer Interface for Assessing Consciousness in Severely Brain-Injured Patients. Clinical Neurophysiology in Disorders of Consciousness, 2015, Springer, Vienna, 133–-148.
19. Chennu, S., Finoia, P., Kamau, E., Monti, M.M., Allanson, J., Pickard, J.D., Owen, A.M., Bekinschtein, T.A., Dissociable endogenous and exogenous attention in disorders of consciousness. NeuroImage 3 (2013), 450–461.
20. Chow, S.-C., Hansheng Wang, and Jun Shao. 2007, CRC Press, Sample size calculations in clinical research.
21. Cohen, J., Statistical power for the social sciences. 1988, Laurence Erlbaum and Associates, Hillsdale, NJ.
22. Combrisson, E., Jerbi, K., Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods, 2015.
23. Cox, D.R., The Regression Analysis of Binary Sequences (with discussion). Journal of the Royal Statistical Society 20 (1958), 215–242.
24. Coyle, D., Stow, J., McCreadie, K., McElligott, J., Carroll, Á., Sensorimotor Modulation Assessment and Brain-Computer Interface Training in Disorders of Consciousness. Arch. Phys. Med. Rehabil. 96:3 (2015), S62–S70.
25. Cruse, D., Chennu, S., Chatelle, C., Bekinschtein, T.A., Fernández-Espejo, D., Pickard, J.D., Laureys, S., Owen, A.M., Bedside detection of awareness in the vegetative state: a cohort study. The Lancet 378:9809 (2011), 2088–2094.
26. Cruse, D., Chennu, S., Chatelle, C., Fernández-Espejo, D., Bekinschtein, T.A., Pickard, J.D., Laureys, S., Owen, A.M., Relationship between etiology and covert cognition in the minimally conscious state. Neurology 78:11 (2012), 816–822.
27. Cruse, D., Chennu, S., Fernández-Espejo, D., Payne, W.L., Young, G.B., Owen, A.M., Detecting awareness in the vegetative state: electroencephalographic evidence for attempted movements to command. 2012, e49933.
28. Cruse, D., Chennu, S., Chatelle, C., Bekinschtein, T.A., Fernández-Espejo, D., Pickard, J.D., Laureys, S., Owen, A.M., Reanalysis of “bedside detection of awareness in the vegetative state: a cohort study”–authors' reply. Lancet 381:9863 (2013), 291–292.
29. Cruse, D., Gantner, I., Soddu, A., Owen, A.M., Lies, damned lies and diagnoses: estimating the clinical utility of assessments of covert awareness in the vegetative state. Brain Inj. 28:9 (2014), 1197–1201.
30. Cumming, The new statistics: why and how. Psychol. Sci. 25:1 (2014), 7–29 (2014).
31. Deeks, J.J., Altman, D.G., Diagnostic tests 4: likelihood ratios. BMJ 329:7458 (2004), 168–169.
32. Demertzi, A., Gomez, F., Crone, J.S., Vanhaudenhuyse, A., Tshibanda, L., Noirhomme, Q., Thonnard, M., et al. Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. Cortex 52 (2014), 35–46.
33. Demertzi, A., Antonopoulos, G., Heine, L., Voss, H.U., Crone, J.S., de Los Angeles, C., Bahri, M.A., et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain, 2015, awv169.
34. Efron, B., Tibshirani, R., Improvements on cross-validation: the 632 + bootstrap method. J. Am. Stat. Assoc. 92:438 (1997), 548–560.
35. Etzel, J.A., Gazzola, V., Keysers, C., An introduction to anatomical ROI-based fMRI classification analysis. Brain Res. 1282 (2009), 114–125.
36. Faugeras, F., Rohaut, B., Weiss, N., Bekinschtein, T.A., Galanaud, D., Puybasset, L., Bolgert, F., et al. Probing consciousness with event-related potentials in the vegetative state. Neurology 77:3 (2011), 264–268.
37. Faugeras, F., Rohaut, B., Weiss, N., Bekinschtein, T., Galanaud, D., Puybasset, L.L., Bolgert, F., et al. Event related potentials elicited by violations of auditory regularities in patients with impaired consciousness. Neuropsychologia 50:3 (2012), 403–418.
38. Fernández-Espejo, D., Bekinschtein, T., Monti, M.M., Pickard, J.D., Junque, C., Coleman, M.R., Owen, A.M., Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. NeuroImage 54:1 (2011), 103–112.
39. Fernández-Espejo, D., Owen, A.M., Detecting awareness after severe brain injury. Nat. Rev. Neurosci. 14:11 (2013), 801–809.
40. Fernandez-Espejo, D., Norton, L., Owen, A.A.M., The clinical utility of fMRI for identifying covert awareness in the vegetative state: a comparison of sensitivity between 3 T and 1.5 T. PLoS One, 9(4), 2014, e95082.
41. Forgacs, P.B., Conte, M.M., Fridman, E.A., Voss, H.U., Victor, J.D., Schiff, N.D., Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following. Ann. Neurol. 76:6 (2014), 869–879.
42. Furukawa, T.A., Strauss, S., Bucher, H.C., Guyatt, G., Diagnostic tests. Guyatt, G., Rennie, D., (eds.) Users' guides to the medical literature, second ed., 2008, AMA Press, Chicago, 419–438.
43. Gabriel, D., Henriques, J., Comte, A., Lyudmila, G., Ortega, J.-P., Cretin, E., Brunotte, G., et al. Substitute or complement? Defining the relative place of EEG and fMRI in the detection of voluntary brain reactions. Neuroscience 290 (2015), 435–444.
44. Galanaud, D., Perlbarg, V., Gupta, R., Stevens, R.D., Sanchez, P., Tollard, E.E., De Champfleur, N.M.M., et al. Assessment of white matter injury and outcome in severe brain trauma. A prospective multicenter cohort. Surv. Anesthesiol. 57:4 (2013), 171–172.
45. Gardner, M.J., Altman, D.G., Estimating with confidence. Altman Douglas, G., David, M., Bryant Trevor, N., Gardner Martin, J., (eds.) Statistics with confidence. Confidence intervals and statistical guidelines, second ed. British Medical Journal, London, 2000.
46. Giacino, J.T., Ashwal, S., Childs, N., Cranford, R., Jennett, B., Katz, D.I., Kelly, J.P., et al. the minimally conscious state definition and diagnostic criteria. Neurology 58:3 (2002), 349–353.
47. Giacino, J.T., Kalmar, K., Whyte, J., The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch. Phys. Med. Rehabil. 85:12 (2004), 2020–2029.
48. Giacino, J.T., Fins, J.J., Laureys, S., Schiff, N.D., Disorders of consciousness after acquired brain injury: the state of the science. Nat. Rev. Neurol. 10:2 (2014), 99–114.
49. Giacino, J.T., Schnakers, C., Rodriguez-Moreno, D., Kalmar, K., Schiff, N., Hirsch, J., Behavioral assessment in patients with disorders of consciousness: gold standard or fool's gold?. Prog. Brain Res. 177 (2009), 33–48.
50. Gibson, R.M., Fernández-Espejo, D., Gonzalez-Lara, L.E., Kwan, B.Y., Lee, D.H., Owen, A.M., Cruse, D., Multiple tasks and neuroimaging modalities increase the likelihood of detecting covert awareness in patients with disorders of consciousness. Front. Hum. Neurosci., 8, 2014, 950, 10.3389/fnhum.2014.00950.
51. Goldfine, A.M., Victor, J.D., Conte, M.M., Bardin, J.C., Schiff, N.D., Determination of awareness in patients with severe brain injury using EEG power spectral analysis. Clin. Neurophysiol. 122:11 (2011), 2157–2168.
52. Goldfine, A.M., Bardin, J.C., Noirhomme, Q., Fins, J.J., Schiff, N.D., Victor, J.D., Reanalysis of “Bedside detection of awareness in the vegetative state: a cohort study. Lancet, 381(9863), 2013, 289.
53. Gosseries, O., Schnakers, C., Ledoux, D., Vanhaudenhuyse, A., Bruno, M.M.-A.A., Demertzi, A.A., Noirhomme, Q., et al. automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct. Neurol., 2011.
54. Gosseries, O., Zasler, N.D., Laureys, S., Recent advances in disorders of consciousness: focus on the diagnosis. Brain Inj. 28:9 (2014), 1141–1150, 10.3109/02699052.2014.920522.
55. Guan, D., Yuan, W., Lee, Y.-K., Lee, S., Identifying mislabeled training data with the aid of unlabeled data. Appl. Intell. 35:3 (2011), 345–358.
56. Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., Pfurtscheller, G., How many people are able to operate an EEG-based brain-computer interface (BCI)?. IEEE Trans. Neural Syst. Rehabil. Eng. 11:2 (2003), 145–147.
57. Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., Edlinger, G., How many people are able to control a P300-based brain–computer interface (BCI)?. Neurosci. Lett. 462:1 (2009), 94–98.
58. Guger, C., Allison, B.Z., Großwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., Edlinger, G., How many people could use an SSVEP BCI?. Front. Neurosci., 6, 2012.
59. Habbal, D., Gosseries, O., Noirhomme, Q., Renaux, J., Lesenfants, D., Bekinschtein, T.A., Majerus, S., Laureys, S., Schnakers, C., Volitional electromyographic responses in disorders of consciousness. Brain Inj. 28:9 (2014), 1171–1179.
60. Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., Bießmann, F., On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 87 (2014), 96–110.
61. Henriques, J., Gabriel, D., Grigoryeva, L., Haffen, E., Moulin, T., Aubry, R., Pazart, L., Ortega, J.-P., Protocol design challenges in the detection of awareness in aware subjects using EEG signals. Clin. EEG Neurosci., 2014 (1550059414560397).
62. Hoffmann, U., Vesin, J.-M., Ebrahimi, T., Diserens, K., An efficient P300-based brain–computer interface for disabled subjects. J. Neurosci. Methods 167:1 (2008), 115–125.
63. Höller, Yvonne, Jürgen Bergmann, Aljoscha Thomschewski, Martin Kronbichler, Peter Höller, Julia S. Crone, Elisabeth V. Schmid, Kevin Butz, Raffaele Nardone, Eugen Trinka. “Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness.” (2013): e80479.
64. Höller, Y., Thomschewski, A., Bergmann, J., Kronbichler, M., Crone, J.S., Schmid, E.V., Butz, K., Höller, P., Nardone, R., Trinka, E., Connectivity biomarkers can differentiate patients with different levels of consciousness. Clin. Neurophysiol. 125:8 (2014), 1545–1555.
65. Howell, D.C., Statistical Methods for Psychology. 2012, Wadsworth.
66. King, J.R., Faugeras, F., Gramfort, A., Schurger, A., El Karoui, I., Sitt, J.D., Dehaene, S., Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness. NeuroImage 83 (2013), 726–738.
67. King, J.-R., Sitt, J.D., Faugeras, F.F., Rohaut, B., El Karoui, I.I., Cohen, L., Naccache, L., Dehaene, S.S., information sharing in the brain indexes consciousness in noncommunicative patients. Curr. Biol. 23:19 (2013), 1914–1919.
68. Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Ashburner, J., Frackowiak, R.S.J., A plea for confidence intervals and consideration of generalizability in diagnostic studies. Brain, 132(4), 2009, e102.
69. Kriegeskorte, N., Kyle Simmons, W., Bellgowan, P.S.F., Baker, C.I., circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12:5 (2009), 535–540.
70. Kübler, A., Birbaumer, N., Brain–computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?. Clin. Neurophysiol. 119:11 (2008), 2658–2666.
71. Landsness, E., Bruno, M.-A., Noirhomme, Q., Riedner, B., Gosseries, O., Schnakers, C., Massimini, M., Laureys, S., Tononi, G., Boly, M., Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state. Brain 134:8 (2011), 2222–2232.
72. Laureys, S., Celesia, G.G., Cohadon, F., Lavrijsen, J., León-Carrión, J., Sannita, W.G., Sazbon, L., Schmutzhard, E., von Wild, K.R., Zeman, A., Dolce, G., European Task Force on Disorders of Consciousness, Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med., 8, 2010, 68, 10.1186/1741-7015-8-68 (Nov 1).
73. Lehembre, R., Bruno, M.-A., Vanhaudenhuyse, A., Chatelle, C., Cologan, V., Leclercq, Y., Soddu, A., Macq, B., Laureys, S., Noirhomme, Q., Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states. Funct. Neurol., 27(1), 2012.
74. Lemm, S., Blankertz, B., Dickhaus, T., Müller, K.-R., Introduction to machine learning for brain imaging. NeuroImage 56:2 (2011), 387–399.
75. Lesenfants, D., Habbal, D., Lugo, Z., Lebeau, M., Horki, P., Amico, E., Pokorny, C., et al. an independent SSVEP-based brain–computer interface in locked-in syndrome. J. Neural Eng., 11(3), 2014, 035002.
76. Lugo, Z.R., Rodriguez, J., Lechner, A., Ortner, R., Gantner, I.S., Laureys, S., Noirhomme, Q., Guger, C., A vibrotactile p300-based brain–computer interface for consciousness detection and communication. Clin. EEG Neurosci., 2014 (1550059413505533).
77. Lulé, D., Noirhomme, Q., Kleih, S.C., Chatelle, C., Halder, S., Demertzi, A., Bruno, M.M.-A.A., et al. probing command following in patients with disorders of consciousness using a brain–computer interface. Clin. Neurophysiol. 124:1 (2013), 101–106.
78. Lutkenhoff, E.S., Chiang, J., Tshibanda, L., Kamau, E., Kirsch, M., Pickard, J.D., Laureys, S., Owen, A.M., Monti, M.M., Thalamic and extrathalamic mechanisms of consciousness after severe brain injury. Ann. Neurol. 78:1 (2015), 68–76, 10.1002/ana.24423 (Jul).
79. Luyt, C.-E., Galanaud, D., Perlbarg, V., Vanhaudenhuyse, A., Stevens, R.D., Gupta, R., Besancenot, H., et al. Diffusion Tensor Imaging to Predict Long-term Outcome after Cardiac ArrestA Bicentric Pilot Study. Anesthesiology 117:6 (2012), 1311–1321.
80. Malinowska, U., Chatelle, C., Bruno, M.-A., Noirhomme, Q., Laureys, S., Durka, P.J., Electroencephalographic profiles for differentiation of disorders of consciousness. Biomed. Eng. Online, 12(1), 2013, 109.
81. Mallett, S., Halligan, S., Thompson, M., Collins, G.S., Altman, D.G., Interpreting diagnostic accuracy studies for patient care. BMJ, 345, 2012, e3999.
82. Maris, E., Oostenveld, R., Nonparametric statistical testing of EEG-and MEG-data. J. Neurosci. Methods 164:1 (2007), 177–190.
83. Martin, J.K., Hirschberg, D.S., Small sample statistics for classification error rates II: Confidence intervals and significance tests. 1996, Department of Information and Computer Science, University of California, Irvine, CA.
84. Monti, M.M., Vanhaudenhuyse, A., Coleman, M.R., Boly, M., Pickard, J.D., Tshibanda, L., Owen, A.M., Laureys, S., Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362:7 (2010), 579–589.
85. Mueller-Putz, G., Scherer, R., Brunner, C., Leeb, R., Pfurtscheller, G., Better than random: A closer look on BCI results. Int. J. Bioelectromagn. 10 (2008), 52–55 no. EPFL-ARTICLE-164768.
86. Näätänen, R., Paavilainen, P., Rinne, T., Alho, K., The mismatch negativity (MMN) in basic research of central auditory processing: A review. Clin. Neurophysiol. 118:12 (2007), 2544–2590.
87. Naci, L., Owen, A.M., Making Every Word Count for Nonresponsive Patients. JAMA Neurol. 70:10 (2013), 1235–1241, 10.1001/jamaneurol.2013.3686.
88. Naci, L., Cusack, R., Jia, V.Z., Owen, A.M., The brain's silent messenger: using selective attention to decode human thought for brain-based communication. J. Neurosci. 33:22 (2013), 9385–9393.
89. Nakase-Richardson, R., Yablon, S.A., Sherer, M.M., Nick, T.G., Evans, C.C., emergence from minimally conscious state insights from evaluation of posttraumatic confusion. Neurology 73:14 (2009), 1120–1126.
90. Newcombe, R.G., Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat. Med. 17:8 (1998), 857–872.
91. Nichols, T.E., Holmes, A.P., Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15:1 (2002), 1–25.
92. Noirhomme, Q., Lehembre, R., Lugo, Z., Lesenfants, D., Luxen, A., Laureys, S., Oddo, M., Rossetti, A.O., Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest. Clin. EEG Neurosci. 45:1 (2014), 6–13, 10.1177/1550059413509616 (Jan).
93. Noirhomme, Q., Lesenfants, D., Gomez, F., Soddu, A., Schrouff, J., Garraux, G., Luxen, A., Phillips, C., Laureys, S., Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions. NeuroImage 4 (2014), 687–694.
94. Owen, A.M., Coleman, M.R., Boly, M., Davis, M.H., Laureys, S., Pickard, J.D., Detecting awareness in the vegetative state. Science, 313(5792), 2006, 1402.
95. Pan, J., Xie, Q., He, Y., Wang, F., Di, H., Laureys, S., Yu, R., Li, Y., Detecting awareness in patients with disorders of consciousness using a hybrid brain–computer interface. J. Neural Eng., 11(5), 2014, 056007.
96. Pereira, F., Mitchell, T., Botvinick, M., Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45:1 (2009), S199–S209.
97. Perlbarg, V., Puybasset, L., Tollard, E., Lehericy, S., Benali, H., Galanaud, D., Relation between brain lesion location and clinical outcome in patients with severe traumatic brain injury: a diffusion tensor imaging study using voxel-based approaches. Hum. Brain Mapp. 30:12 (2009), 3924–3933.
98. Peterson, A., Cruse, D., Naci, L., Weijer, C., Owen, A.M., Risk, diagnostic error, and the clinical science of consciousness. NeuroImage 7 (2015), 588–597.
99. Phillips, C.L., Bruno, M.-A.A., Maquet, P., Boly, M.M., Noirhomme, Q., Schnakers, C., Vanhaudenhuyse, A., et al. relevance vector machine” consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients. NeuroImage 56:2 (2011), 797–808.
100. Plum, F., Posner, J.B., The diagnosis of stupor and coma. Contemp. Neurol. Ser. 10 (1971), 1–286.
101. Pokorny, C., Klobassa, D.S., Pichler, G., Erlbeck, H., Real, R.G.L., Kübler, A., Lesenfants, D., et al. the auditory P300-based single-switch brain–computer interface: paradigm transition from healthy subjects to minimally conscious patients. Artif. Intell. Med. 59:2 (2013), 81–90.
102. Rasmussen, C.E., Gaussian processes in machine learning“. Advanced Lectures on Machine Learning. Lect. Notes Comput. Sci 3176 (2004), 63–71.
103. Rosanova, M., Gosseries, O., Casarotto, S., Boly, M., Casali, A.G., Bruno, M.-A., Mariotti, M., et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain, 2012, awr340.
104. Ruf, C.A., De Massari, D., Wagner-Podmaniczky, F., Matuz, T., Birbaumer, N., semantic conditioning of salivary pH for communication. Artif. Intell. Med. 59:2 (2013), 91–98.
105. Sackett, D.L., Straus, S., Richardson, W.S., Rosenberg, W., Haynes, R.B., Evidence based medicine. How to practise and teach EBM, second ed., 2000, Churchill Livingstone, Edinburgh, 67–93.
106. Sanders, R.D., Tononi, G., Laureys, S., Sleigh, J., Unresponsiveness ≠ unconsciousness. Anesthesiology, 116(4), 2012, 946.
107. Schiff, N.D., Rodriguez-Moreno, D., Kamal, A.A., Kim, K.H.S., Giacino, J.J.T., Plum, F., Hirsch, J.J., fMRI reveals large-scale network activation in minimally conscious patients. Neurology 64:3 (2005), 514–523.
108. Schnakers, C., Perrin, F., Schabus, M., Majerus, S., Ledoux, D., Damas, P., Boly, M.M., et al. voluntary brain processing in disorders of consciousness. Neurology 71:20 (2008), 1614–1620.
109. Schnakers, C., Vanhaudenhuyse, A., Giacino, J., Ventura, M., Boly, M., Majerus, S., Moonen, G., Laureys, S., Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment. BMC Neurol., 9(1), 2009, 35.
110. Schnakers, C., Perrin, F., Schabus, M., Hustinx, R., Majerus, S., Moonen, G., Boly, M., Vanhaudenhuyse, A., Bruno, M.-A., Laureys, S., Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase 15:4 (2009), 271–277.
111. Sitt, J.D., King, J.-R., El Karoui, I., Rohaut, B., Faugeras, F., Gramfort, A., Cohen, L., Sigman, M., Dehaene, S., Naccache, L., Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain 137:8 (2014), 2258–2270.
112. Stender, J., Gosseries, O., Bruno, M.M.-A.A., Charland-Verville, V., Vanhaudenhuyse, A., Demertzi, A., Chatelle, C., et al. diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet 384:9942 (2014), 514–522.
113. Steppacher, I., Kaps, M., Kissler, J., Will time heal? A long-term follow-up of severe disorders of consciousness. Ann. Clin. Transl. Neurol. 1:6 (2014), 401–408, 10.1002/acn3.63 (Jun).
114. Stoll, J., Chatelle, C., Carter, O., Koch, C., Laureys, S., Einhäuser, W., Pupil responses allow communication in locked-in syndrome patients. Curr. Biol. 23:15 (2013), R647–R648.
115. The Multi-Society Task Force on PVS, Medical aspects of the persistent vegetative state (2). N. Engl. J. Med. 330:22 (1994), 1572–1579.
116. Tipping, M.E., Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1 (2001), 211–244.
117. Tzovara, A., Rossetti, A.O., Spierer, L., Grivel, J., Murray, M.M., Oddo, M., De Lucia, M., Progression of auditory discrimination based on neural decoding predicts awakening from coma. Brain 136 (2013), 81–89.
118. Vanhaudenhuyse, A., Noirhomme, Q., Tshibanda, L.J.-F., Bruno, M.-A., Boveroux, P., Schnakers, C., Soddu, A., et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 133:1 (2010), 161–171.
119. Wannez, S., Annen, J., Aubinet, C., Thonnard, M., Charland-Verville, V., Heine, L., Habbal, D., Martial, C., Bodart, O., Vanhaudenhuyse, A., Chatelle, C., Thibaut, A., Schnakers, C., Demertzi, A., Gosseries, O., Laureys, S., The Eleventh World Congress on Brain Injury, The Hague, The Nederlands, 2-5 March. 2016.
120. Wilhelm, B., Jordan, M., Birbaumer, N., Communication in locked-in syndrome: effects of imagery on salivary pH. Neurology 67:3 (2006), 534–535.
121. Wolfers, T., Buitelaar, J.K., Beckmann, C., Franke, B., Marquand, A., From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci. Biobehav. Rev. 57 (2015), 328–349, 10.1016/j.neubiorev.2015.08.001.