State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes
State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes
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DOI：
10.1101/283846
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发表时间：
2018-03
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影响因子：
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通讯作者：
Yu Takagi;J. Hirayama;Saori C. Tanaka
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作者：
Yu Takagi;J. Hirayama;Saori C. Tanaka
最近的功能磁共振成像（fMRI）研究越来越多地揭示了不同类型的脑功能和功能障碍的个体差异的潜在神经基质。虽然大多数以前的研究本质上局限于相关脑网络及其功能的状态特异性表征，但最近的几项研究已经研究了功能性脑网络的潜在状态非特异性性质，例如它们在不同实验条件下的全局相似性（即，包括任务和休息。然而，没有以前的研究进行了直接的，系统的特征的状态非特异性的大脑网络，或其功能的影响。在这里，我们定量地确定了几种模式的状态非特异性个体变化的全脑功能连接模式，称为“共同的神经模式（CNMs）”，从一个大型的功能磁共振成像数据集，包括8个任务/休息状态，从人类连接组项目。此外，我们测试了CNMs如何解释个体行为测量的变异性。结果表明，在各种不同的预处理条件下，三种CNMs被稳健地提取。这些CNMs中的每一个都与流体和结晶智力的行为测量的不同方面显著相关。这三个CNM还能够预测几个生活结果，如收入和生活满意度，当与行为智能测量作为输入相结合时，实现了最高的性能。我们的研究结果强调了状态非特异性脑网络的重要性，以表征基本的个体差异。
Recent functional magnetic resonance imaging (fMRI) studies have increasingly revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have been inherently limited to state-specific characterizations of related brain networks and their functions, several recent studies have examined the potential state-unspecific nature of functional brain networks, such as their global similarities across different experimental conditions (i.e., states) including both task and rest. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variation in whole-brain functional connectivity patterns, called “Common Neural Modes (CNMs)”, from a large fMRI dataset including eight task/rest states, obtained from the Human Connectome Project. Furthermore, we tested how CNMs account for variability in individual behavioral measures. The results revealed that three CNMs were robustly extracted under various different preprocessing conditions. Each of these CNMs was significantly correlated with different aspects of behavioral measures of both fluid and crystalized intelligence. The three CNMs were also able to predict several life outcomes, such as income and life satisfaction, achieving the highest performance when combined with behavioral intelligence measures as inputs. Our findings highlight the importance of state-unspecific brain networks to characterize fundamental individual variation.