Evidence map›Paper›PMID 38720838›Full record

ArticleQuantitative imaging in medicine and surgery2024

Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder.

Yulong Wang, Yande Ren, Yuzhen Bi, Feng Zhao, Xingzhen Bai, Liangzhou Wei, Wanting Liu, Hancheng Ma, Peirui Bai

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yulong Wang *College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China.
Yande Ren *Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Yuzhen BiDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Feng ZhaoSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Xingzhen BaiCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China.
Liangzhou WeiDepartment of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Wanting LiuDepartment of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Hancheng MaDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Peirui BaiCollege of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods: A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results: The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions: The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.

Indexed as

functional connectivity (FC)graph neural networks (GNNs)Insomnia disorder (ID)transformer

Identifiers

PMID38720838
PMCPMC11074748

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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.