|Alternative Title||Modeling Inter-individual Differences in Lame-scale Human Brain Networks with Repeated Measurements|
生物多样性的表现之一是个体差异，对其神经机制的研究是心理学和神经科学的前沿方向，核心与关键是对个体差异的可信测量。类内相关系数(intra-class correlation, ICC)是一种常被应用于考察个体差异的可信度统计量。磁共振技术通过非侵入性的方式对大脑的结构和功能变化进行成像(magnetic resonance imaging, MRI)，己成为心理学及神经科学领域最为倚重的研究工具之一。己有多项研究系统地考察了这些人脑结构形态和功能MRI测量的重测信度，但鲜有研究考察个体间差异的测量是否受到成像仪器、扫描序列等的影响。本研究首先建立多站点重复测量大型数据集，以人脑大尺度功能网络为研究对象，通过对其结构形态和内在功能指标的多层混合线性建模，定量化研究个体间差异、个体内差异和影像站点差异以及它们对测量信度的影响。包括以下三个研究:
Individual differences is one of the most important features of biodiversity and the study of its neural mechanisms is the frontier of psychology and neuroscience. The key point of research individual differences is its stable measurement. Intra-class correlation (ICC) is the metric that was most used to investigate the reliability of individual differences. Magnetic resonance imaging (MRI) investigate the structural and functional changes of the brain in a non-invasive manner and has become the workhorse of neuroscience and psychology. There are already a number of studies that systematically investigated the test-retest reliability of structural and functional MRl metrics but seldom has consider whether the scanner, scanning protocol will affect the investigation of individual differences. In order to give a preliminary answer to this question, this study first established a multi一sites multi-sessions MRI dataset and taking the macro-scale functional networks of human brain as templates and create a multi-layers linear mixed effects modeling on structural morphology and intrinsic functional metrics, quantitative research on inter-individual differences, intra-individual differences, site differences and their influence on different reliability.
Study one introduces the design of a multi一sties mulit-sessions dataset that we collected in last three that is called R3BRAIN (reliability, reproducibility and replicability) and built a linear mixed effect model based on this dataset. In this study 51 participants who finished all 4 times 3T scans were included. Individual differences of structural morphology metrics were investigated in this study. Results showed that individual differences were affected by site factors, cortical thickness suffered more than cortical area on large-scale networks. Inter-sessions reliability were almost perfect across all networks (ICC>0.8) mean while inter-sites reliability were all above 0.6.
In study two we investigate the amplitude, inter-network connectivity, intra-network connectivity derived by implement dual-regression on Yeo's 17 networks. Results showed that amplitude and inter-networks connectivity have higher individual differences after regress global signal and showed less influences by site factors. Intra-network connectivity showed less influence by site factors. Brain networks of control and default have higher individual differences. Area where shows ICC above 0.4 become smaller when introduces site factors.
In study three the sex differences of large-scale network morphology and function metrics was examined by a linear mixed model. The results show that gender-related effects are drives by the global characteristics, and after such global differences are properly controlled, the differences in large-scale networks between men and women are no longer significant. More specifically, the morphological differences between men and women are mainly due to males have larger brain volume. That differences were mainly affected by the difference in surface area. Sex differences are also manifested in the functional brain network level where males have stronger inter-network connectivity between the control network, the attention network and the somatosensory movement network. Intra-networks differences mainly showed on somatosensory network in which male showed stronger connectivity/ The above-mentioned gender differences are no longer significant after controlling the global characteristics accordingly, and are reproducible at different sites.
To sum up, this study provides a reference for multi-site research. It is found that the individual difference measurement of brain morphology has almost perfect test-retest reliability, while the measurement of functional individual difference shows moderate to substantial test-retest reliability. The signal-to-noise ratio (SNR) is an important factor affecting the test-retest reliability. The individual difference measurement of morphology metrics has considerable and near-perfect inter-site test-retest reliability. Functional individual difference measurement's site reliability shows network-dependent differences, control network, dorsal attention network, default network have considerable higher reliability. SNR is also an important factor that affects site test-rest reliability. The cortical morphology of the parietal memory network has high individual differences. The associative cortical network has higher inter-individual differences in the spontaneous functional signal than the primary cortical network, while the brain network gender differences are mainly driven by the global metrics of structure and function.
|Keyword||个体差异 信度 重复测量 脑网络 性别差异|
|Place of Conferral||中国科学院心理研究所|
|王银山. 人脑大尺度网络个体差异的重复测量建模[D]. 中国科学院心理研究所. 中国科学院大学,2019.|
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