Evidence map›Paper›PMID 42558387›Full record

ArticleFrontiers in neurology

The diagnostic and prognostic value of auditory statistical learning ability in patients with disorders of consciousness.

Mingyan Yu, Xiangyue Xiao, Junhua Ding, Mengying Zhao, Sara Cruz, Nantu Hu, Mingwei Zhang, Steven Laureys, Haibo Di, Yan Chen

Abstract read
In one paragraph

Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mingyan YuInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Xiangyue XiaoInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Junhua DingState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Mengying ZhaoInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Sara CruzDepartment of Education and Psychology, William James Center for Research, University of Aveiro, Aveiro, Portugal.
Nantu HuInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Mingwei ZhangInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Steven LaureysInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Haibo DiInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.
Yan ChenInternational Unresponsive Wakefulness Syndrome and Consciousness Science Institute, School of Basic Medicine, Hangzhou Normal University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Accurate assessment of residual cognition in patients with disorders of consciousness (DoC) remains challenging due to their limited behavioral responsiveness. Statistical learning (SL), defined as the ability to extract regularities from sensory input, reflects neural processing associated with residual cognition. This study examined how SL is preserved across different levels of consciousness and whether neural markers of SL can inform diagnosis and prognosis in patients with DoC. Methods: We employed an auditory paradigm with structured trisyllabic pseudowords and recorded electroencephalography (EEG) from 46 patients and 25 healthy controls. Neural entrainment to statistical regularities was quantified using frequency-tagging analysis. Results: The within-group effect pattern was observed in the healthy control group and in the minimally conscious state (MCS) group, whereas the unresponsive wakefulness syndrome (UWS) group did not show a significant effect. Moreover, stronger neural learning predicted better behavioral scores in patients with MCS, but not in patients with UWS. Further analyses showed that SL-related effect correlated significantly with behavioral scores and 6-month outcomes in the traumatic group, whereas no such association was observed in the non-traumatic group. Importantly, SL-based EEG models effectively distinguished healthy controls from patients and further differentiated MCS from UWS patients. Combining the SL model with clinical behavioral performance could enable a more precise assessment of patients' level of consciousness. Discussion: These findings suggest that SL may require a minimal level of consciousness in the integration of structured regularities and that it may serve as an index for clinical diagnosis and prognosis in patients with DoC.

Indexed as

diagnosisdisorders of consciousnesselectroencephalographyprognosisstatistical learning

Identifiers

PMID42558387
PMCPMC13437382

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.