亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease

动态功能连接 功能磁共振成像 计算机科学 认知 人工智能 神经科学 大脑活动与冥想 心理学 脑电图
作者
Biao Jie,Mingxia Liu,Dinggang Shen
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:47: 81-94 被引量:139
标识
DOI:10.1016/j.media.2018.03.013
摘要

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer’s disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi-task feature learning and multi-kernel learning techniques. Results on 149 subjects with baseline rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that our method can not only improve the classification performance in comparison with state-of-the-art methods, but also provide insights into the spatio-temporal interaction patterns of brain activity and their changes in brain disorders.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
towerman发布了新的文献求助10
刚刚
上官若男应助研友_qZ6V1Z采纳,获得10
4秒前
FengyaoWang完成签到,获得积分10
4秒前
5秒前
enchanted发布了新的文献求助10
10秒前
ZHANG123完成签到,获得积分10
14秒前
21秒前
七色光完成签到,获得积分10
22秒前
23秒前
23秒前
ceeray23发布了新的文献求助20
26秒前
爆米花应助Bowman采纳,获得30
29秒前
30秒前
研友_qZ6V1Z发布了新的文献求助10
35秒前
35秒前
shaylie完成签到 ,获得积分10
35秒前
伽拉发布了新的文献求助10
36秒前
轻松的惜芹应助linkman采纳,获得10
38秒前
Karol发布了新的文献求助10
38秒前
39秒前
hhw发布了新的文献求助10
39秒前
43秒前
充电宝应助hhw采纳,获得10
48秒前
霜鸣发布了新的文献求助10
48秒前
热爱科研的小白鼠完成签到,获得积分10
48秒前
还单身的心情完成签到 ,获得积分10
53秒前
研友_qZ6V1Z发布了新的文献求助10
53秒前
轻松的惜芹应助linkman采纳,获得10
54秒前
慕青应助我爱物理采纳,获得10
57秒前
57秒前
充电宝应助霜鸣采纳,获得10
59秒前
1分钟前
隐形曼青应助伽拉采纳,获得10
1分钟前
hhw完成签到,获得积分10
1分钟前
咕噜噜发布了新的文献求助10
1分钟前
1分钟前
1分钟前
研友_qZ6V1Z发布了新的文献求助10
1分钟前
涨秋池发布了新的文献求助10
1分钟前
燕子完成签到 ,获得积分10
1分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990012
求助须知:如何正确求助?哪些是违规求助? 3532049
关于积分的说明 11256153
捐赠科研通 3270925
什么是DOI,文献DOI怎么找? 1805123
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809216