Assessment of Statistically Significant Command-Following in Pediatric Patients with Disorders of Consciousness, Based on Visual, Auditory and Tactile Event-Related Potentials
Disorders of consciousness (DOC) are among the major challenges of contemporary medicine, mostly due to the high rates of misdiagnoses in clinical assessment, based on behavioral scales. This turns our attention to potentially objective neuroimaging methods. Paradigms based on electroencephalography (EEG) are most suited for bedside applications, but sensitive to artifacts. These problems are especially pronounced in pediatric patients.
We present the first study on the assessment of pediatric DOC patients by means of command-following procedures and involving long-latency cognitive event-related potentials. To deal with the above mentioned challenges, we construct a specialized signal processing scheme including artifact correction and rejection, parametrization, classification and final assessment of the statistical significance. To compensate for the possible bias of the tests involved in the final diagnosis, we propose the Monte Carlo evaluation of the processing pipeline. To compensate for possible sensory impairments of DOC patients, for each subject we check command-following responses to the stimuli in the major modalities: visual, tactile, and audio (words and sounds).
We test the scheme on 20 healthy volunteers and present results for 15 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis on the Coma Recovery Scale-Revised (CRS-R).
- 1. , From unresponsive wakefulness to minimally conscious plus and functional locked-in syndromes: Recent advances in our understanding of disorders of consciousness, J. Neurol. 258 (2011) 1373–1384. Crossref, Medline, ISI, Google Scholar
- 2. , The minimally conscious state definition and diagnostic criteria, Neurology 58(3) (2002) 349–353. Crossref, Medline, ISI, Google Scholar
- 3. , The JFK coma recovery scale-revised: Measurement characteristics and diagnostic utility, Arch. Phys. Med. Rehabil. 85 (2004) 2020–2029. Crossref, Medline, ISI, Google Scholar
- 4. , Assessment scales for disorders of consciousness: Evidence-based recommendations for clinical practice and research, Arch. Phys. Med. Rehabil. 91(12) (2010) 1795–1813. Crossref, Medline, Google Scholar
- 5. , Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: A clinical validation study, The Lancet 384(9942) (2014) 514–522. Crossref, Medline, Google Scholar
- 6. , Evoked and event-related potentials in disorders of consciousness: A quantitative review, Conscious Cogn. 54 (2017) 155–167. Crossref, Medline, Google Scholar
- 7. , Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state, Brain 137(8) (2014) 2258–2270. Crossref, Medline, Google Scholar
- 8. , Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation, Q. J. R. Meteorol. Soc. 128(584) (2002) 2145–2166. Crossref, ISI, Google Scholar
- 9. , Bedside detection of awareness in the vegetative state: A cohort study, The Lancet 378 (2011) 2088–2094. Crossref, Medline, Google Scholar
- 10. , Reanalysis of Bedside detection of awareness in the vegetative state: A cohort study, The Lancet 381 (2013) 289–291. Crossref, Medline, Google Scholar
- 11. , A P300-based cognitive assessment battery, Brain Behav. 5 (2015) e00336. Crossref, Medline, Google Scholar
- 12. , On the robust parametric detection of EEG artifacts in polysomnographic recordings, Neuroinformatics 7(2) (2009) 147–160. Crossref, Medline, Google Scholar
- 13. E. Jones, T. Oliphant, P. Peterson et al., SciPy: Open source scientific tools for Python (2001–), [accessed 2017-10-26, version 1.0.0]. Google Scholar
- 14. , MNE software for processing MEG and EEG data, NeuroImage 86(Suppl C) (2014) 446–460. Crossref, Medline, Google Scholar
- 15. , MEG and EEG data analysis with mne-python, Front. Neurosci. 7 (2013) 267. Crossref, Medline, Google Scholar
- 16. E. Larson, A. Gramfort, D. A. Engemann, Jaeilepp, C. Brodbeck, M. Jas, T. L. Brooks, Jona Sassenhagen, M. Luessi, J.-R. King, R. Goj, M. Wronkiewicz, M. van Vliet, C. Holdgraf, Yousrabk, A. Leggitt, A. R. Dykstra, R. Trachel, Lorenzo Desantis, A. Panda, mbillingr, dgwakeman, M. Magnuski, D. Strohmeier, T. Linzen, H. Bharadwaj, E. Ruzich, alexandre barachant, cmoutard and C. Bailey, mne-tools/mne-python: v0.14 (March 2017). Google Scholar
- 17. , Scikit-learn: Machine learning in Python, J. Mach. Learn. Res. 12 (2011) 2825–2830. ISI, Google Scholar
- 18. , The area above the ordinal dominance graph and the area below the receiver operating characteristic graph, J. Math. Psychol. 12(4) (1975) 387–415. Crossref, Google Scholar
- 19. , Nonparametric statistical testing of EEG-and MEG-data, J. Neurosci. Methods 164(1) (2007) 177–190. Crossref, Medline, ISI, Google Scholar