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Editorial
. 2023 Aug 2:17:1256168.
doi: 10.3389/fnhum.2023.1256168. eCollection 2023.

Editorial: Dissociations between neural activity and conscious state: a key to understanding consciousness

Joel Frohlich  1   2 , Julia S Crone  3 , Pedro A M Mediano  4   5 , Daniel Toker  6 , Daniel Bor  5   7
Affiliations
Editorial

Editorial: Dissociations between neural activity and conscious state: a key to understanding consciousness

Joel Frohlich et al. Front Hum Neurosci. .
No abstract available

Keywords: EEG; biomarkers; coma; consciousness; disorders of consciousness; general anesthesia; sleep; unresponsive wakefulness syndrome.

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Conflict of interest statement

JF was a former employee of F. Hoffmann-La Roche Ltd. (October 2016–July 2017). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Matrix of study populations for investigating neural biomarkers of consciousness broken down by the presence or absence of abnormal cortical dynamics and the presence or absence of high-confidence data labels to guide supervised machine learning. In the top left quadrant, such labels are available, but the risk of overfitting a binary classifier model to normal EEG features is high. This includes most studies of sleep and general anesthesia [but note that cognitive-motor dissociation sometimes occurs under anesthesia (Sebel et al., 2004); see also (Nilsen et al.)]. In the bottom left quadrant, cortical dynamics are normal (so we risk overfitting), and we don't even know if our subjects possess consciousness. This includes human fetuses and newborns (Frohlich et al., 2023a) and other animal species, especially those that are only distantly related to humans (Birch, 2022). Next, the bottom right quadrant includes subjects with abnormal cortical dynamics whose level of consciousness is uncertain, namely, DoC patients. Finally, the top right quadrant represents our ideal study population: cortical dynamics are abnormal and lack the features that we might otherwise overfit a classifier model to in the top left quadrant. Nonetheless, these participants are behaviorally responsive, giving us labels with which to train a classifier. Besides children with 15q disorders (Frohlich et al., 2022), this quadrant also includes healthy adults challenged with drugs like tiagabine (Nutt et al., ; Barnett et al., ; Darmani et al., 2021) which result in paradoxical pharmacological dissociations (Frohlich et al., 2023b). Alzheimer's disease (Huntley et al.) can be understood as an ambiguous case that might fall under either the top right or bottom right quadrant.
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References

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