Evidence map›Paper›PMID 33935840›Full record

ArticleFrontiers in psychiatry2021

Deep Neural Network to Differentiate Brain Activity Between Patients With First-Episode Schizophrenia and Healthy Individuals: A Multi-Channel Near Infrared Spectroscopy Study.

Po-Han Chou, Yun-Han Yao, Rui-Xuan Zheng, Yi-Long Liou, Tsung-Te Liu, Hsien-Yuan Lane, Albert C Yang, Shao-Cheng Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 9% 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Functional Near-Infrared Spectroscopy in Schizophrenia Research: Progress, Challenges, and Future Directions.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Deep learning in fNIRS: a review.Neurophotonics · 2022
    Review
  14. Article
  15. Article
  16. Article
  17. 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

8 authors at 5 institutions in 2 countries.

Po-Han ChouDepartment of Psychiatry, China Medical University Hsinchu Hospital, Hsinchu, Taiwan.
Yun-Han YaoMediaTek Inc., Hsinchu, Taiwan.
Rui-Xuan ZhengMediaTek Inc., Hsinchu, Taiwan.
Yi-Long LiouMediaTek Inc., Hsinchu, Taiwan.
Tsung-Te LiuGraduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan.
Hsien-Yuan LaneGraduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.
Albert C YangInstitute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
Shao-Cheng WangDepartment of Forensic and Addiction Psychiatry, Jianan Psychiatric Center, Ministry of Health and Welfare, Tainan, Taiwan.
National Taiwan University · TWChina Medical University · TWBali Psychiatric Center · TWJohns Hopkins University · USMediaTek (Taiwan) · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

deep learningdeep neural networkfNIRSmachine learningnear infrared spectroscopyschizophrenia

Identifiers

PMID33935840
PMCPMC8081971
OpenAlexW3153875331

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.