A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

中文翻译：

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用静息态 fMRI 估计和验证个性化动态脑模型

神经科学的一个关键挑战是以个性化、数据驱动的方式开发人类神经系统的生成因果模型。以前的举措要么构建了不受个体水平人类大脑活动约束的生物学上合理的模型，要么使用了非机械的个体数据驱动统计特征。我们的目标是通过开发一种称为中尺度个体化神经动力学 (MINDy) 建模的新建模方法来弥合这一差距，其中我们将非线性动力系统模型直接拟合到人脑成像数据。MINDy 框架能够在每个受试者仅 1-3 分钟的时间内为数百到数千个交互的大脑区域生成这些数据驱动的网络模型。我们证明了这些模型是有效的、可靠的和稳健的。我们证明 MINDy 模型可以预测静息态大脑动态活动的个性化模式。此外，与功能连接方法相比，MINDy 能够更好地揭示静息状态活动中个体差异的潜在机制。