Evidence map›Paper›PMID 37746052›Full record

ReviewFrontiers in human neuroscience2023

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis.

Ming-Lin Li, Fei Zhang, Yi-Yang Chen, Han-Yong Luo, Zi-Wei Quan, Yi-Fei Wang, Le-Tian Huang, Jia-He Wang

Abstract readReview
In one paragraph

Review in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024
    Review
  6. Review
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.

Ming-Lin Li *Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Fei Zhang *Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Yi-Yang ChenDepartment of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Han-Yong LuoDepartment of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Zi-Wei QuanDepartment of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Yi-Fei WangDepartment of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Le-Tian HuangDepartment of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Jia-He WangDepartment of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

Indexed as

diagnosisfunctional magnetic resonance imagingmachine learningpathophysiologyprimary headachesstatistical modeling

Identifiers

PMID37746052
PMCPMC10513061

What Socratic 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 Socratic graph.