Chen Bing, Xu Ting, Zhou Changle, Wang Luoyu, Yang Ning, Wang Ze, Dong Hao-Ming, Yang Zhi, Zang Yu-Feng, Zuo Xi-Nian, Weng Xu-Chu
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.
PLoS One. 2015 Dec 29;10(12):e0144963. doi: 10.1371/journal.pone.0144963. eCollection 2015.
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.
人们认为，心理和行为方面的个体差异反映了人类大脑的功能变异性。由于缺乏大规模的纵向数据集，个体功能连接组内部和之间变异性的全貌在很大程度上尚不清楚。我们收集了30名健康参与者的300个静息态功能磁共振成像（rfMRI）数据集，这些参与者在一个月内每三天接受一次扫描。利用这些数据，我们在多个空间尺度上估计了六种常见rfMRI指标的个体内和个体间变异性，以及它们的重测可靠性。全局指标比局部区域指标更具动态性。涉及工作记忆、抑制、注意力、语言及相关神经网络的认知成分表现出较高的个体内变异性。相比之下，个体间变异性在多个指标尺度上呈现出更为复杂的情况。在参与者中，边缘系统、默认网络、额顶叶网络和视觉网络及其相关认知成分比躯体运动和注意力网络更具差异性。对个体内和个体间变异性的分析揭示了一组关于多尺度连接组指标重测可靠性的高分辨率图谱。这些发现代表了首次对人类功能连接组在体内的多尺度和多指标特征中的个体差异进行的收集，为该领域在基于rfMRI的应用中指导常用功能指标的使用提供了正常参考。
