Dear Editors:

There is growing scientific interest regarding the use of electroencephalogram (EEG) and multivariate analysis as a means to classify states of consciousness. A recent work assessed the validity of 28 potential EEG markers of consciousness, using the Disorders of Consciousness (DoC)-Forest (the name of the algorithm) tool, to establish states of awareness in a large population of patients with unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS).1 Using the DoC-Forest approach, the authors demonstrated that combining multiple EEG data (including alpha-band power, theta-band connectivity, and time series complexity) in the analysis provides complementary information to clinical assessments of states of consciousness, significantly reducing the influence of different EEG configurations and experimental protocols on the distribution and performance of the EEG markers. The gold-standard approach for diagnosing a consciousness disorder is repeated clinical assessment using the Coma Recovery Scale (CRS-R).2,3 However, even the best standardized behavioral assessments can miss signs of residual conscious processing in some patients. Using advanced para-clinical approaches, these signs are more easily detected,4,5 and the patients may be labeled as “with covert awareness” or “with cognitive-motor dissociation.”6,7 We have shown that different experimental approaches based on EEG data are useful in refining the clinical diagnosis in cases of consciousness,8,9 as recently as that proposed by Engemann et al.1 As the authors confirmed, the DoC-Forest complex analyses tool consistently demonstrated its usefulness in differentiating states of consciousness.

EEG analysis offers rich temporal information on cognitive operations, capturing even small fluctuations in awareness, which are not only biasing factors when attempting to differentiate disorders of consciousness, but are important predictors of awareness recovery.10 Additionally, EEG analysis using DoC-Forest could potentially be used at bedside or during home assessment. The authors highlight the importance of approaching EEG markers with DoC-Forest. Quantitative metrics of specific neural networks have been shown to correlate with the continuum of behavioral recovery in patients with disorders of consciousness (from UWS to locked-in syndrome).11 We recently demonstrated the usefulness of combining different network metrics to further refine the correlation between EEG connectivity and behavioral recovery.12 Specifically, this multivariate approach was shown to better characterize the primary pathophysiological features of (un)awareness, including involvement of the interhemispheric fronto-parietal functional connectivity and the aberrant connectome organization, at both network topology and nodal level.13

In conclusion, evidence supporting the use of EEG and the multivariate approach in the differential diagnosis of consciousness disorders is growing. Indeed, analyzing combinations of markers (either neurophysiologically or through neuroimaging) synergistically outperforms the univariate approach, complementing behavioral assessment and reducing the rate of misdiagnosis in patients with consciousness disorders. The DoC-Forest approach can help better identify patients who require further assessments, as well as estimate prognosis and track patient response to interventions.


  1. Engemann DA, Raimondo F, King JR, et al. Robust EEG based cross-site and cross-protocol classification of states of consciousness. Brain. 2018;141(11):3179–3192.
  2. Wannez S, Heine L, Thonnard M, et al. The repetition of behavioral assessments in diagnosis of disorders of consciousness. Ann Neurol. 2017;81:883–889.
  3. Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004;85:2020–2029.
  4. Monti MM, Vanhaudenhuyse A, Coleman MR, et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med. 2010;362:579–589.
  5. Schnakers C, Vanhaudenhuyse A, Giacino J, et al. Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment. BMC Neurol. 2009;9:35
  6. Schiff ND. Cognitive motor dissociation following severe brain injuries. JAMA Neurol. 2015; 72:1413–1415.
  7. Curley WH, Forgacs PB, Voss HU, et al. Characterization of EEG signals revealing covert cognition in the injured brain. Brain. 2018;141:1404–1421.
  8. Calabrò RS, Bramanti P, Naro A. Towards new methods of diagnosis in disorders of consciousness. Lancet Neurol. 2016;15(11):1114–1115.
  9. Naro A, Bramanti A, Leo A, et al. Shedding new light on disorders of consciousness diagnosis: the dynamic functional connectivity. Cortex. 2018;103:316–328.
  10. Faugeras F, Rohaut B, Valente M, et al. Survival and consciousness recovery are better in the minimally conscious state than in the vegetative state. Brain Inj. 2018; 32:72–77.
  11. Chennu S, Annen J, Wannez S, et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain. 2017;140:2120–2132.
  12. Cacciola A, Naro A, Milardi D, et al. Functional brain network topology discriminates between patients with minimally conscious state and unresponsive wakefulness syndrome. J Clin Med. 2019;8(3)pii:E306.
  13. Long J, Xie Q, Ma Q, Urbin MA, et al. Distinct interactions between fronto-parietal and default mode networks in impaired consciousness. Sci Rep. 2016;6:38866.

With regards,

Antonino Naro, MD, PhD, and Rocco Salvatore Calabrò, MD, PhD

IRCCS Centro Neurolesi Bonino Pulejo Messina, Italy

Funding/financial disclosures. The authors have no conflict of interest relevant to the content of this letter. No funding was received for the preparation of this letter.