干旱区生态环境知识资源中心(Arid): Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning

Arid

DOI	10.1109/JBHI.2023.3306460
	Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning
	Li, Chaolin; Liu, Mianxin; Xia, Jing; Mei, Lang; Yang, Qing; Shi, Feng; Zhang, Han; Shen, Dinggang
通讯作者	Zhang, H ; Shen, DG
来源期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
EISSN	2168-2208
出版年	2023
卷号	27 期号:11 页码:5430-5438
英文摘要	PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-beta deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-beta PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in A beta-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level A beta-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.
英文关键词	Functional connectivity high-order functional connectivity amyloid beta graph convolutional networks
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001129955100021
WOS关键词	STATE FUNCTIONAL CONNECTIVITY ; MILD COGNITIVE IMPAIRMENT ; ALZHEIMERS-DISEASE ; VISUALIZATION ; PATTERNS
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396904
推荐引用方式 GB/T 7714	Li, Chaolin,Liu, Mianxin,Xia, Jing,et al. Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning[J],2023,27(11):5430-5438.
APA	Li, Chaolin.,Liu, Mianxin.,Xia, Jing.,Mei, Lang.,Yang, Qing.,...&Shen, Dinggang.(2023).Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,27(11),5430-5438.
MLA	Li, Chaolin,et al."Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 27.11(2023):5430-5438.