Send to

Choose Destination
PLoS One. 2015 Dec 29;10(12):e0144963. doi: 10.1371/journal.pone.0144963. eCollection 2015.

Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month.

Chen B1,2, Xu T3, Zhou C1, Wang L2, Yang N3,4,5, Wang Z2, Dong HM3,4,5, Yang Z3,5, Zang YF2, Zuo XN3,5,6,7, Weng XC1,2.

Author information

Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China.
Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Faculty of Psychology, Southwest University, Beibei, Chongqing 400715, China.
Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi 530001, China.


Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center