Evidence map›Paper›PMID 32508627›Full record

ArticleFrontiers in aging neuroscience2020

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy.

Dalin Yang, Ruisen Huang, So-Hyeon Yoo, Myung-Jun Shin, Jin A Yoon, Yong-Il Shin, Keum-Shik Hong

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 4 pooled it
5.9field-weighted citation impact, top 3% 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

37 citing papers in PubMed, 4 syntheses or guidelines pooled it, 100 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Identification of ∆9-tetrahydrocannabinol (THC) impairment using functional brain imaging.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022
    Trial
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

7 authors at 3 institutions in 1 country.

Dalin YangSchool of Mechanical Engineering, Pusan National University, Busan, South Korea.
Ruisen HuangSchool of Mechanical Engineering, Pusan National University, Busan, South Korea.
So-Hyeon YooSchool of Mechanical Engineering, Pusan National University, Busan, South Korea.
Myung-Jun ShinDepartment of Rehabilitation Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea.
Jin A YoonDepartment of Rehabilitation Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea.
Yong-Il ShinDepartment of Rehabilitation Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan-si, South Korea.
Keum-Shik HongSchool of Mechanical Engineering, Pusan National University, Busan, South Korea.
Pusan National University · KRPusan National University Hospital · KRPusan National University Yangsan Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks:

Indexed as

brain mapconvolutional neural network (CNN)functional near-infrared spectroscopy (fNIRS)mild cognitive impairment (MCI)N-backStrooptemporal featureverbal fluency task

Identifiers

PMID32508627
PMCPMC7253632
OpenAlexW3026272888

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.