Evidence map›Paper›PMID 35459119›Full record

ArticleBMC psychiatry2022

Frontotemporal EEG as potential biomarker for early MCI: a case-control study.

Yasue Mitsukura, Brian Sumali, Hideto Watanabe, Toshiharu Ikaga, Toshihiko Nishimura

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Review
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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

5 authors.

Yasue Mitsukura *Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan. mitsukura@keio.jp.ORCID 0000-0001-5575-8589
Brian Sumali *Keio Global Institute(KGRI), Keio University, Tokyo, Japan.
Hideto WatanabeDepartment of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan.
Toshiharu IkagaDepartment of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan.
Toshihiko NishimuraDepartment of Anesthesia, School of Medicine, Stanford University, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77).

methodsWe recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' severity, were examined using the ratio of power spectrum at each frequency.

resultsIn comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals.

conclusionsA study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDementiaBiomarkersCase-Control StudiesElectroencephalographyHumansReproducibility of ResultsBiomarkersClinical decision-makingDementiaEEGMCI

Identifiers

PMID35459119
PMCPMC9027034

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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 OpenQuestion graph.