Evidence map›Paper›PMID 41382202›Full record

GuidelineMilitary Medical Research2025

A methodological guideline for consciousness assessment via neural electrophysiological activity.

An-An Ping, Long-Zhou Guan, Yong Wang, Sheng Yang, Chao Yang, Xiao-Qing Hu, Yi-Heng Tu, He Chen, Wei-Guang Li, Xiao-Li Li

Abstract readPractice Guideline
PubMed Full text at PMC Publisher
In one paragraph

Guideline in Military Medical Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

An-An Ping *State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Long-Zhou Guan *Institute of Advanced Technology, South China University of Technology, Guangzhou, 511442, China.
Yong Wang *Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Sheng YangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Chao YangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Xiao-Qing HuDepartment of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong SAR, 999077, China.
Yi-Heng TuState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. tuyh@psych.ac.cn.
He ChenSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China. chenhe@scut.edu.cn.
Wei-Guang LiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. wgli@icmm.ac.cn.
Xiao-Li LiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. xiaolili@scut.edu.cn.ORCID 0000-0003-1359-5130

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysiological, pharmacological, and pathological alterations of consciousness provide critical windows into its neural substrates. Given the inherent complexity and multidimensionality of consciousness, defining quantitative, dynamic signatures of neural activity, and translating them into clinically applicable tools remains challenge. This study aimed to build an electroencephalography (EEG)-based methodological guideline for clinical consciousness assessment.

methodsEEG signals were systematically categorized across periodic and aperiodic activity, connectivity and network topology, spatiotemporal dynamics, self-organized criticality, and transcranial magnetic stimulation (TMS)-evoked responses. These biomarkers were mapped onto a conceptual framework of consciousness, comprising wakefulness and internal/external awareness, based on their validation across clinical conditions. The discriminative efficacy of various biomarkers was then evaluated across 4 independent datasets.

resultsIntegrated EEG features each captured distinct yet complementary dimensions of consciousness, supporting a unified neurophysiological architecture underlying diverse alterations of consciousness. Spectral power and peak frequency tracked the loss of consciousness during propofol anesthesia and sleep. Steeper aperiodic slopes, loss of frontoparietal connectivity, disrupted small-world organization, and reduced effective dimensionality were particularly effective in distinguishing minimally conscious state (MCS) from unresponsive wakefulness syndrome (UWS). Additionally, spatiotemporal patterns exhibited consciousness-specific alterations, with both pharmacological and pathological alterations influencing specific microstate dynamics.

conclusionsSynthesizing integrated neural dynamics and multidimensional consciousness, this guideline establishes both methodological and theoretical foundations for translating neurophysiological biomarkers into clinical applications. While this work advances both conceptual clarity and practical methodology, large-scale validation across expanded clinical cohorts, experimental models, and multimodal platforms is essential to fully establish causal linkages and translational utility.

Indexed as

ConsciousnessElectroencephalographyAdultFemaleHumansMaleTranscranial Magnetic StimulationConsciousnessDisorders of consciousnessElectroencephalogramGeneral anesthesiaSleepTemporo-spatio-spectral analysis

Identifiers

PMID41382202
PMCPMC12699880

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.