PPGCN: Phase-Aligned Periodic Graph Convolutional Network for Dual-Task-Based Cognitive Impairment Detection-Reference-Cited by-同舟云学术

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PPGCN: Phase-Aligned Periodic Graph Convolutional Network for Dual-Task-Based Cognitive Impairment Detection

Published:2024 Issue: Volume:12 Page:37679-37691
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Godó Ákos¹^ORCID,Wu Shuqiong¹^ORCID,Okura Fumio²^ORCID,Makihara Yasushi¹^ORCID,Ikeda Manabu³,Sato Shunsuke³^ORCID,Suzuki Maki⁴^ORCID,Satake Yuto³^ORCID,Taomoto Daiki³,Yagi Yasushi¹^ORCID

Affiliation:

1. SANKEN, Osaka University, Osaka, Japan

2. Graduate School of Information Science and Technology, Osaka University, Osaka, Japan

3. Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan

4. Department of Behavioral Neurology and Neuropsychiatry, United Graduate School of Child Development, Osaka University, Osaka, Japan

Funder

Japan Agency for Medical Research and Development

Japan Society for the Promotion of Science (JSPS) KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10453582.pdf?arnumber=10453582

Reference43 articles.

1. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease

2. The use of neuroimaging techniques in the early and differential diagnosis of dementia

3. MRI and CT in the diagnosis of vascular dementia

4. Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND

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