Neurorecovery and Cerebral Hemodynamics in Patients Undergoing Transcranial Direct Current Stimulation with Disorders of Consciousness (DoC): A Systematic Review
Bibliographic record
Abstract
Background: Disorders of consciousness (DoC), encompassing coma, vegetative state/unresponsive wakefulness syndrome (VS/UWS), and minimally conscious state (MCS), result from severe brain injuries that disrupt neural networks responsible for arousal and awareness. Non-invasive brain stimulation (NIBS) techniques, including transcranial direct current stimulation (tDCS) and its variants, such as high-definition tDCS (HD-tDCS) and transcranial alternating current stimulation (tACS), offer promising therapeutic strategies. This review synthesizes evidence on the efficacy of NIBS, focusing on its impact on brain hemodynamics, neurophysiology, and clinical outcomes. Methods: To this end, we searched the international databases (Web of Science, PubMed, Scopus) and extracted studies using the appropriate keywords. The Newcastle-Ottawa Scale (NOS) was used to assess the methodology and quality of the studies. Results: Research demonstrates that tDCS and its advanced forms improve EEG patterns, including alpha and theta band power, reduce P300 latency, and enhance cortical-cortical and thalamocortical connectivity, correlating with better behavioral outcomes, as measured by the Coma Recovery Scale-Revised (CRS-R). Moreover, personalized protocols based on MRI simulations and multimodal therapies, such as combining NIBS with music stimulation or robotic rehabilitation, further optimize outcomes by targeting specific brain areas and enhancing network reconfiguration. The dual application of HD-tDCS with transcutaneous auricular vagus nerve stimulation (taVNS) has also shown synergistic effects on EEG microstate dynamics and CRS-R scores in MCS patients. Conclusion: Overall, NIBS presents a promising approach for enhancing consciousness recovery, though challenges in protocol optimization and understanding the mechanisms of action remain. Future research should continue to explore these techniques' full potential, particularly through personalized, multi-target stimulation strategies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".