Evidence map›Paper›PMID 35911952›Full record

ArticleHealth information science and systems2022

Microstate feature fusion for distinguishing AD from MCI.

Yupan Shi, Qinying Ma, Chunyu Feng, Mingwei Wang, Hualong Wang, Bing Li, Jiyu Fang, Shaochen Ma, Xin Guo, Tongliang Li

Open access · greenAbstract read
In one paragraph

Article in Health information science and systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
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

11 citing papers in PubMed, 23 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Yupan ShiInstitute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China.
Qinying MaDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.ORCID 0000-0002-3428-2187
Chunyu FengInstitute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China.
Mingwei WangDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Hualong WangDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Bing LiDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Jiyu FangDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Shaochen MaDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Xin GuoDepartment of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Tongliang LiInstitute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China.
Hebei Medical University · CNHebei Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (

Indexed as

ADEEG microstatesMCITime-factor transition probabilitiesTransition probabilities

Identifiers

PMID35911952
PMCPMC9325930
OpenAlexW4288081373

What OpenQuestion holds

Textmetadata
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.