Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

连接体 人类连接体项目 功能磁共振成像 神经科学 静息状态功能磁共振成像 功能连接 心理学 认知 大脑定位 神经网络 鉴定(生物学) 神经影像学 连接组学 计算机科学 人工智能 生物 植物
作者
Emily S. Finn,Xilin Shen,Dustin Scheinost,Monica D. Rosenberg,Jessica S. Huang,Marvin M. Chun,Xenophon Papademetris,R. Todd Constable
出处
期刊:Nature Neuroscience [Springer Nature]
卷期号:18 (11): 1664-1671 被引量:2515
标识
DOI:10.1038/nn.4135
摘要

This study shows that every individual has a unique pattern of functional connections between brain regions. This functional connectivity profile acts as a ‘fingerprint’ that can accurately identify the individual from a large group. Furthermore, an individual's connectivity profile can predict his or her level of fluid intelligence. Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
大有阳光应助九点一定起采纳,获得10
刚刚
2秒前
清风发布了新的文献求助10
2秒前
乐乐应助PaulaD采纳,获得10
2秒前
Akim应助顺心的书包采纳,获得10
2秒前
orixero应助Tyche采纳,获得10
3秒前
chenhd完成签到,获得积分10
3秒前
Charail发布了新的文献求助10
3秒前
洛小叶完成签到,获得积分10
3秒前
传奇3应助HonglinGao采纳,获得10
5秒前
华hua完成签到,获得积分10
7秒前
iuyol完成签到,获得积分10
7秒前
清风完成签到,获得积分10
8秒前
8秒前
8秒前
踏雪无痕完成签到 ,获得积分10
8秒前
Cecilia完成签到,获得积分10
9秒前
10秒前
糊涂的元珊完成签到 ,获得积分10
10秒前
Light完成签到,获得积分10
10秒前
狮子卷卷完成签到,获得积分10
11秒前
huazhangchina发布了新的文献求助30
11秒前
所所应助哎呦魏采纳,获得10
12秒前
xiaofei666完成签到,获得积分10
13秒前
Tyche发布了新的文献求助10
13秒前
14秒前
14秒前
无花果应助Light采纳,获得10
14秒前
zho关闭了zho文献求助
15秒前
15秒前
卷毛完成签到,获得积分20
15秒前
研友_Zb1rln发布了新的文献求助10
17秒前
王润完成签到,获得积分10
18秒前
苏夏完成签到 ,获得积分10
21秒前
有信仰的无神论者完成签到,获得积分10
22秒前
zzz发布了新的文献求助10
22秒前
111完成签到,获得积分10
22秒前
天麓北发布了新的文献求助10
23秒前
当蒋不当蒋完成签到 ,获得积分10
26秒前
26秒前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
A new species of Velataspis (Hemiptera Coccoidea Diaspididae) from tea in Assam 500
Diagnostic immunohistochemistry : theranostic and genomic applications 6th Edition 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3155593
求助须知:如何正确求助?哪些是违规求助? 2806820
关于积分的说明 7870825
捐赠科研通 2465126
什么是DOI,文献DOI怎么找? 1312144
科研通“疑难数据库(出版商)”最低求助积分说明 629889
版权声明 601892