Multivariate dynamical modelling of structural change during development.
Multivariate dynamical modelling of structural change during development.
复制标题
开发过程中结构变化的多元动态建模。
DOI：
10.1016/j.neuroimage.2016.12.017
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发表时间：
2017
期刊：
影响因子：
5.7
通讯作者：
Penny,Will
中科院分区：
文献类型：
--
作者：
Ziegler,Gabriel;Ridgway,GerardR;Blakemore,Sarah-Jayne;Ashburner,John;Penny,Will
在这里，我们介绍了一个多变量的框架，在结构磁共振成像的纵向变化的特点，使用动力系统。通用方法能够对通常在大脑发育、可塑性、老化和退化期间观察到的多个成像生物标志物中的状态变化进行建模，例如多个感兴趣区域（ROI）的区域灰质体积。大脑结构状态遵循一个线性系统的内在动力学，额外的输入占大脑发育的潜在驱动力。特别是，系统的输入被指定为考虑已知的或潜在的发育生长/衰退因素，例如，由于生长激素，青春期，或突然的行为变化等的影响，因为发育因素的影响可能是区域特定的，每个ROI的敏感性，每个因素的贡献明确建模。除了发展因素对区域变化的外部影响外，该框架还可以对大脑区域之间由于空间竞争或结构连接等原因而产生的定向（潜在互惠）相互作用进行建模和推断。这种方法解释了在发育和衰老的典型MRI研究中的重复测量。模型反演和后验分布获得使用早期建立的变分方法，使贝叶斯证据为基础的比较各种模型的结构变化。使用这种方法，我们证明了动态皮质变化，在大脑成熟的6至22岁之间，使用一个大型的开放式纵向儿科数据集，从289个人的637次扫描。特别是，我们在26个双边ROI，其中包括大部分的皮质和皮质下灰质的体积变化模型。我们解释了（1）青春期相关的影响，灰质区域;（2）早期短暂的生长过程与额外的时滞参数的影响;（3）性别二型性的建模参数之间的差异男孩和女孩。有证据表明，儿童后期对动态隐藏生长因子的敏感性的区域模式在性别之间是相似的，并且显示出一致的前后梯度，对前额叶皮层（PFC）大脑变化的影响最大。最后，我们展示了潜在的框架，探索耦合的结构变化acrossa priorefined子网络使用以前建立的静息状态功能连接的例子。
Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e.g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes acrossa prioridefined subnetworks using an example of previously established resting state functional connectivity.