Robust EEG-based cross-site and cross-protocol classification of states of consciousness

Engemann, Denis A.; Raimondo, Federico; J, King; Rohaut, Benjamin; Louppe, Gilles; Faugeras, Frédéric; Annen, Jitka; Cassol, Héléna; Gosseries, Olivia; Slezak, Diego Fernández; Laureys, Steven; Naccache, Lionel; Dehaene, Stanislas; Sitt, Jacobo

doi:10.1093/brain/awy251

Cited by 243 publications

(297 citation statements)

References 60 publications

Supporting

Mentioning

283

Contrasting

Unclassified

Order By: Relevance

“…Previous EEG network-based studies in the context of DOC have been performed at the scalp level (Chennu et al, 2014;Chennu et al, 2017) with satisfactory accuracies in classifying UWS and MCS patients (Sitt et al, 2014;Chennu et al, 2017;Engemann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Decreased integration of EEG source-space networks in disorders of consciousness

Rizkallah

Annen

Modolo

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using highdensity electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS-(n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness. Keywords: Disorders of consciousness, high-density electroencephalography, functionalbrain networks, unresponsive wakefulness syndrome, minimally conscious state.

show abstract

Section: Introductionmentioning

confidence: 99%

Decreased integration of EEG source-space networks in disorders of consciousness

Rizkallah

Annen

Modolo

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Preprocessing EEG data were processed using an automatized and hierarchical pipeline for artefact removal and extraction of EEG-measures previously described (13,15,26). Written in Python, C, and bash shell scripts and based on open source technologies, including the software MNE (61), the preprocessing workflow proceeded as follows:…”

Section: Acquisitionmentioning

confidence: 99%

“…However, this behavioral assessment has limitations and 15-20% of behaviorally-diagnosed VS/UWS patients show patterns of brain activity suggestive of higher states of consciousness (4)(5)(6)(7)(8). Among the different brain-imaging techniques used, EEG has proved to be a reliable, non-invasive bedside tool to probe signatures of both conscious state and conscious access to external stimuli in DoC patients (9)(10)(11)(12)(13)(14)(15). Specifically, increases of spectral power, complexity and functional connectivity in the theta-alpha bands correlate with state of consciousness, but the specificity and causal value of these putative signatures of consciousness remain to be demonstrated.…”

Section: Main Text Introductionmentioning

confidence: 99%

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

Hermann¹,

Raimondo²,

Hirsch³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials. This non-invasive and inexpensive tool may turn out to be the preferred treatment option. However, its mechanisms of action and physiological effects on brain activity remain unclear and debated. Here, we stimulated 60 DoC patients with the anode placed over left-dorsolateral prefrontal cortex in a prospective open-label study. Clinical behavioral assessment improved in twelve patients (20%) and none deteriorated. This behavioral response after tDCS coincided with an enhancement of putative EEG markers of consciousness: in comparison with non-responders, responders showed increases of power and long-range cortico-cortical functional connectivity in the theta-alpha band, and a larger and more sustained P300 suggesting improved conscious access to auditory novelty. The EEG changes correlated with electric fields strengths in prefrontal cortices, and no correlation was found on the scalp. Taken together, this prospective intervention in a large cohort of DoC patients strengthens the validity of the proposed EEG signatures of consciousness, and is suggestive of a direct causal effect of tDCS on consciousness.

show abstract

“…However, it has been shown that in correlated trees, variable importance can be biased and lead to masking effects, i.e., fail to detect important variables (Louppe et al, 2013) or suggest noise-variables to be important. One potential remedy is to increase the randomness of the trees, e.g., by selecting randomly a single variable for splitting and using extremely randomized trees (Geurts et al, 2006;Engemann et al, 2018), as it can be mathematically guaranteed that in fully randomized trees only actually important variables are assigned importance (Louppe et al, 2013). However, such measures may mitigate performance.…”

Section: Stacked Cross-validationmentioning

confidence: 99%

“…Unfortunately, the effectiveness of machine learning in psychiatry and neurology is constrained by the lack of large high-quality datasets (Woo et al, 2017;Varoquaux, 2017;Bzdok and Yeo, 2017;Engemann et al, 2018) and comparably limited understanding about the data generating mechanisms (Jonas and Kording, 2017). This, potentially, limits the advantage of complex learning strategies proven successful in purely somatic problems (Esteva et al, 2017;Yoo et al, 2019;Ran et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Engemann¹,

Kozynets²,

Sabbagh³

et al. 2019

Preprint

View full text Add to dashboard Cite

Electrophysiological methods, i.e., M/EEG provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as surrogate biomarker in 674 subjects from the Cam-CAN. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by source-topography of power spectra between 8 and 30 Hz. Finally, we demonstrate that the model maintains benefits of stacking when data is missing. The proposed framework hence enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

show abstract

Robust EEG-based cross-site and cross-protocol classification of states of consciousness

Cited by 243 publications

References 60 publications

Decreased integration of EEG source-space networks in disorders of consciousness

Decreased integration of EEG source-space networks in disorders of consciousness

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Contact Info

Product

Resources

About