Brain network correlates of recovery of consciousness and non-invasive brain stimulation
Bibliographic record
Abstract
Brain-injured individuals may survive but become behaviorally unresponsive and are then said to be in a disorder of consciousness (DoC). Some of these patients are transiently aware of themselves and their surroundings (i.e. are conscious), while the rest are completely unconscious. This distinction is critical, as it has implications for end-of-life decision-making and access to care. Yet, 15 to 41% of patients who are minimally conscious are misdiagnosed as unconscious (Kondziella et al., 2016; Schnakers et al., 2009a). While neuroimaging methods such as positron emission tomography are available for prognosis, they are not accessible to all patients due to cost and medical contraindications. Further, there are no recommended treatment options for DoC patients beyond 4-16 weeks post-injury (Giacino et al., 2018). There is therefore a need for novel approaches to the prognosis and treatment of DoC. First, we investigated the relationship between 3-node functional motifs derived from high-density electroencephalogram (EEG) networks and states of anesthetic-induced unconsciousness in healthy adults (n=9). Node participation in a motif composed of long-range, source-sink connections was disrupted during states of anesthetic-induced unresponsiveness. Participation in a loop-like motif composed of short-range connections was disrupted during high levels of anesthesia and returned to its baseline state prior to recovery of responsiveness. Second, we measured the association between 3-node functional network motifs and recovery of consciousness in three cases of DoC. At baseline, the topography of node participation in motifs was similar to healthy controls in patients who eventually recovered. The ability of topographic network properties to reconfigure in response to an anesthetic perturbation was also associated with recovery. Third, we measured the effects of transcranial direct current stimulation (tDCS) applied to the left dorsolateral prefrontal cortex of healthy adults on scalp and source EEG networks to inform the eventual treatment of DoC. We found no statistically significant impact of 1 or 2 mA tDCS on brain networks as compared to sham stimulation.Taken together, these three studies highlight the usefulness of graph theoretical measures derived from high-density EEG for the prognostication of DoC and the neurophysiological assessment of tDCS response. This work raises several avenues for future exploration in the areas of prognosis and treatment of DoC, such as perturbation studies, optimized tDCS targeting algorithms and tDCS dose-response studies in DoC populations
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".