Assessing residual consciousness and cognitive abilities in unresponsive patients is a major clinical concern and a challenge for cognitive neuroscience. Although neuroimaging studies have demonstrated a potential for informing diagnosis and prognosis in unresponsive patients, these methods involve sophisticated brain imaging technologies, which limit their clinical application. In this study, we adopted a new language paradigm that elicited rhythmic brain responses tracking the single-word, phrase and sentence rhythms in speech, to examine whether bedside electroencephalography (EEG) recordings can help inform diagnosis and prognosis. EEG-derived neural signals, including both speech-tracking responses and temporal dynamics of global brain states, were associated with behavioral diagnosis of consciousness. Crucially, multiple EEG measures in the language paradigm were robust to predict future outcomes in individual patients. Thus, EEG-based language assessment provides a new and reliable approach to objectively characterize and predict states of consciousness and to longitudinally track individual patients’ language processing abilities at the bedside.
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A functional near-infrared spectroscopy study on hemodynamic changes of patients with prolonged disorders of consciousness responding to different auditory stimuli
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The data that support the findings of this study are available from the corresponding authors upon reasonable request.
EEG data analyses were performed in BrainVision Analyzer, the freely available toolbox EEGLAB and MicrostateAnalysis in combination with custom MATLAB scripts. The software code that support the findings of this study is available from the corresponding authors upon reasonable request.
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We thank L. Melloni, J. Sitt, P. Barttfeld and J. Wu for their suggestions on the study and helpful comments on the manuscript. We also thank R. Zheng and W. Peng from the Institute of Neuroscience, the Institute of Neuroscience Primate Physiology Research Platform Core Facility and the nursing staff from Shanghai Huashan Hospital for their assistance in data acquisition. We are grateful to all the patients and their families and the healthy individuals for their participation.
This work was supported by the Key Research Program of Frontier Sciences (QYZDY-SSW-SMC001), the Strategic Priority Research Program (XDB32070200), the Pioneer Hundreds of Talents Program from the Chinese Academy of Sciences and the Shanghai Municipal Science and Technology Major Project (2018SHZDZX05) to L.W.; the National Natural Science Foundation of China (81571025) and the International Scientific and Technological Collaboration Foundation (18410711300) from the Shanghai Science and Technology Committee (STCSM) to X.W.; Shanghai Municipal Science and Technology Major Project (2018SHZDZX03) and ZJLab to Y.M.; and the Shanghai Sailing Program (17YF1426600) from the STCSM to Z.Q.
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks Damian Cruse and Ziv Williams for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The flowchart showing patients selection in data analysis.
Extended Data Fig. 2 Individual ITPC responses to hierarchical linguistic structures in Healthy controls (n = 47), MCS (n = 42) and UWS (n = 36) patients at three task levels.
In each inset, the dots in the left represent the ITPC values from individual subject at target frequencies (1/2/4 Hz), while the dots in the right represent the individual mean value at its respective neighbours. Solid black dots represent grand mean values. n.s., P > 0.1; ~, P < 0.1; *, P < 0.001; one-sided paired-sample t-test: see legend of Fig. 2b for precise statistical values.
Extended Data Fig. 3 ITPC responses to hierarchical linguistic structures in individual patients at three task levels.
Each bar denotes the response from one subject. Red dots indicate the significance (exact P < 0.05; one-sided exact test, the statistical significance (exact P) of the ITPC response at a target frequency is the probability that the target frequency response differs from the null distribution, which consisted of responses at all non-target frequencies, see Methods).
Number of subjects: nHealthy-Resting = 34, nHealthy-Task =47, nMCS-Resting = 41, nMCS-Task = 42, nUWS-Resting = 30, nUWS-Task =36. Top row: Template Anterior-Posterior (A-P) and Left-Right (L-R) maps obtained from healthy controls. Bottom three rows: Original four maps (Maps A, B, C, and D) of each group in each condition.
a, The duration of the A-P Map for the healthy control (nResting = 34, nTask = 47), MCS (nResting = 41, nTask = 42), and UWS (nUWS = 30, nTask =36) groups in all four task conditions. b, The occurrence of the L-R Map for healthy control, MCS, and UWS groups. Note that there were no differences between the three groups. Boxes represent IQR, central dots indicate the median, and whiskers indicate 1.5 × IQR. Colored dots indicate outliers. c, The duration of the L-R map for healthy controls (gray; nResting = 34, nTask = 47), recovery patients (+ve, green; nResting = 11, nTask = 19), and non-recovery MCS (-ve MCS, blue; nResting = 30, nTask = 23) and non-recovered UWS (-ve UWS, red; nResting = 30, nTask = 36) patients in all four conditions. One-way ANOVA, Bonferroni corrected: Healthy vs. +ve, PResting = 0.001, PWord = 0.018, PPhrase = 0.013, PSentence = 0.008; +ve vs. -ve MCS, PWord = 0.012, PPhrase = 0.009, PSentence = 0.002. d, The occurrence of the A-P map for healthy controls, recovery patients, non-recovery MCS patients, and non-recovery UWS patients. One-way ANOVA, Bonferroni corrected: Healthy vs. +ve, PResting = 6.8×10-5, PWord = 8.1×10-7, PPhrase = 5.7×10-7, PSentence = 1.8×10-7; +ve vs. -ve MCS, PWord = 0.046, PPhrase = 0.048, PSentence = 0.026. Panel c and d: colored dots represent individual subjects. Black dots represent mean values. Error bars represent S.E.M. All panels: *, P < 0.05; **, P < 0.01; ***, p < 0.001.
a, The comparison of volumes of brain injury between MCS (n = 17) and UWS (n = 10) patients. The black dots and error bars denote the mean value and S.E.M. t25 = 0.64, P = 0.53, two-tailed two-sample t-test. b, The correlation between ∆Cρ and the volumes of brain injury in three task conditions. Pearson’s correlation test (two-tailed), nMCS = 17, nUWS = 10. c, An example patient (Patient 7): the MRI data and maps of the stroke patient without brain damage. d, An example patient (Patient 17): the MRI data and maps of the TBI patient with large brain damage. e, The comparison of ∆probability between the stroke patient without brain damage and the TBI patient with brain damage, as shown in c and d. P1: Patient 7, P2: Patient 17. f, ∆Cρ, the same format as e. g, The MRI data and brain states of a stroke patient with brain damage (an example patient, Patient 2). The orange box indicates the first EEG recording in unrecovered state. The green box indicates the last EEG recording in recovery state. h, The comparison of ∆probability in Patient 2 between the first EEG recording in unrecovered state and the last EEG recording in recovery state. i, ∆Cρ, the same format as h. W: Word, P: Phrase, S: Sentence. F: First recording, L: Last recording. The percentage under each spatial map indicates the probability of each map.
a, The confusion matrix of diagnosed consciousness classification generated by the cross-validated SVM. The feature combinations we used were [∆Cρ + DurationL-R+ OccurrenceA-P + ITPC1Hz + ITPC2Hz + ITPC4Hz] for Sentence task. b, The performance of outcome prediction on training data using SVM classifier with the best feature combinations. Left: Outcome prediction accuracies by EEG on 38 EEG recordings (15 outcome-positive patients). Right: Comparison of individual predictions and actual outcomes. The patients with UWS are shown to the left of dashed line, and the patients with MCS are shown to the right. The dots above the threshold (gray line, prediction score = 0.3) represent the patients with predicted positive outcomes, while the others represent those with predicted negative outcomes. The actual outcome-negative patients are marked by orange dots, and the actual outcome-positive patients are marked by green diamonds. Solid green diamonds represent the outcome in patients that regained wakefulness. The feature combinations we used were: [∆Probability + DurationL-R + TransitionA-P] for Word condition, [∆Probability + OccurrenceA-P + DurationL-R + TransitionL-R + ITPC4Hz] for Phrase condition, [OccurrenceA-P + DurationL-R + TransitionL-R + ITPC1Hz + ITPC2Hz] for Sentence condition.
Extended Data Fig. 8 Comparison of EEG-based and CRS-based classifiers for diagnosis and outcome prediction.
a, Performance of clinical diagnosis using the CRS-R total-score. The classification model (LDA) was trained on the first 38 patients with cross validation (Left) and then tested (without retraining) on a novel dataset of 25 patients (Right). b, Left: Performance of outcome prediction using the optimal CRS-R sub-score (Visual subscale). Right: The prognostic validity of this model. The classifier was trained with cross-validation on the first dataset of 38 patients, then tested for generalization on a new dataset of 25 patients. c, Comparison of the prediction performance for models with the same number of features, based either on CRS-R (7 features, as in b) or the EEG recording under the word condition (7 features: 1 ITPC and 6 microstates). The optimal feature combination was ∆Probability + OccurrenceA-P. d, To test whether the superior EEG generalization ability was due to the larger number of EEG features used in the model, we then ran another way of selecting features by merely using the features with the first two highest weights in the model, and compared their performance of generalization. For the model using EEG see c, and for CRS-R, the best two features were Visual and Arousal subscales. e, Comparison of the performance of outcome prediction, using a standard LDA without feature selection, using all 7 features under the word condition of EEG versus all 7 features of the CRS-R scores (1 total-score and 6 sub-scores: auditory, visual, motor, oromotor, communication and arousal). Generalizations using EEG recorded during the other two task conditions (phrase: AUC = 89%, χ2 = 13.1, P = 5.5×10-4, accuracy = 84%; sentence: AUC = 93%, χ2 = 13.1, P = 5.5×10-4, accuracy = 84%; chi-squared test) showed similar results as that during the word condition.
Extended Data Fig. 9 Comparisons of performance of outcome prediction using EEG versus EEG plus CRS-R scores.
Upper: The confusion matrix of outcome prediction by EEG scores. Lower: The confusion matrix of outcome prediction by the combination of EEG and CRS-R scores.
a, Individual patients (n = 15). Within each inset, every blue dot indicates one CRS-R rating, and the gray line indicates the GLM fitting of all ratings in the entire period. Day 0 and red vertical dashed lines indicate the day of first EEG recording. b, The comparison of CRS-R scores between the EEG recording day and the day within a week (on average within 2.67 days). Colored lines indicate the ratings of individual patients. Black line indicates the mean. No significant difference was found between the two ratings (n = 15, t14 = 0.899, P = 0.384; two-sided paired-sample t-test).
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Gui, P., Jiang, Y., Zang, D. et al. Assessing the depth of language processing in patients with disorders of consciousness. Nat Neurosci 23, 761–770 (2020). https://doi.org/10.1038/s41593-020-0639-1
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